Chinese Institute of Engineers – Greater New York Chapter (http://www.cie-ny.org)
美洲中國工程師學會大紐約分會
Chinese Institute of Engineers – USA Greater New York Chapter (CIE-USA/GNYC) http://www.cie-ny.org Saturday, October 22, 2016
2016 Annual Convention Proceedings
Sheraton LaGuardia East Hotel Flushing, New York
2016 CIE – USA / GNYC Annual Convention
Chinese Institute of Engineers – Greater New1 York Chapter (http://www.cie-ny.org)
CIE-USA/GNYC 2016 Annual Convention Program Sheraton LaGuardia East Hotel, Flushing, NY 11354
Theme: Sustaining Economic Prosperity via Research, Innovation & Entrepreneurship 經由研究，創新和創業來維持經濟的繁榮 Saturday, October 22, 2016
12:00 PM Registration 1:00 PM - 2:20 PM Opening Remarks Dr. Kun-Lung Wu (吳坤龍) - Convention Chair Phoenix Ballroom State of the Institute Dr. C. Eric Wu (吳振藩)- President, CIE-USA/GNYC
Plenary Session Chair – Dr. Tien-Jen Cheng (鄭天人)- Globalfoundries
Dr. Paul Tang - VP, IBM Chief Health Transformation Officer “Disrupting the healthcare of things – Personalizing healthcare to N=1” Dr. Minda Ho(何岷達)- Adjunct Prof. Zhejiang Univ., (retired) President, Praxair China “When an engineer meets a business manager - The learning process that transforms engineers into business leaders”
Poster Announcement
Co-Chair – Dr. Pei-Yun Hsueh (薛沛芸) – IBM Research 2:20 PM - 2:30 PM Co-Chair - Dr. Hsinyu Tsai (蔡欣妤)-- IBM Research
2:30 PM Tea & Coffee Break 2:40 PM – 4:00 PM Parallel Session Session I – Science and Technology (Boardroom East)
Chair – Dr. Chia-Yu Chen (陳家佑) – IBM Research
( )- Dr. Ching-Tzu Chen 陳敬慈 IBM Research “Spintronics: Science & Technology”
Dr. Qing Cao (曹庆) - IBM Research “Recent progress on high performance logic electronics based on carbon nanotubes”
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Chinese Institute of Engineers – Greater New2 York Chapter (http://www.cie-ny.org)
Mr. Jintao Zhang (张金涛) - Dept. of Electrical Engineering, Princeton University “Why mix-signal can benefit machine learning”
Dr. Chi-Chun Liu (劉其俊)- IBM Research “Patterning options with directed self-assembly for advanced technology node”
Session II – Cognitive & Analytics (Boardroom West) Chair – Prof. Winston H. Hsu (徐宏民) – National Taiwan University
Dr. Liangliang Cao - Chief Scientist/Co-Founder, customerserviceAI.com “Propelling artificial intelligence: from games to customer service AI”
Dr. Kerry Shih-Ping Chang (張詩平) - IBM Research
“A spreadsheet model for using web services and creating data-driven
applications”
Dr. Chi Xiong (熊驰)- IBM Research “Silicon photonics for data communications and gas sensing”
Session III Entrepreneurship (Phoenix Ballroom, 2:40 – 4:30 pm) Chair – Mr. Fred Yan (顏為民) – Analyst, Education Testing Service
Mr. Bicheng Han (韩璧丞) - CEO & Founder, BrainCo Inc./Harvard University
“Neuro feedback solutions enabling interaction between human brain and hardware devices”
Mr. Jian Chen (陈箭) - President, EDETEK “Experience in creating and developing an innovative clinical technology company”
Mr. Seamon Chan (陈希孟) - Co-Founder, Managing Partner, Palm Drive Ventures
“Tech startup from VC perspective”
Dr. Joseph Barba - Professor, Founder, the CUNY Zahn Innovation Center “Student entrepreneurship at CCNY: The Zahn Innovation Center and the IN2NYC program” Dr. Tony Tao Zhang (张弢) - Principal, Fish & Richardson “Common IP issues for entrepreneurs”
4:00 PM Tea & Coffee Break 4:10 PM - 5:30 PM Parallel Sessions Session IV – Healthcare & life Sciences (Boardroom East) Chair – Dr. Henry Chang (張鴻洋) – IBM Research
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Chinese Institute of Engineers – Greater New3 York Chapter (http://www.cie-ny.org)
Dr. Kun Lin (林堃)- IBM Research “Challenges in transitioning from pay-for-service to pay-for-value”
Dr. Tian Hao (郝添) - IBM Research “Is Wearable Sensing turning us into cyborgs?”
Dr. Ching-Heui Tsou (鄒慶暉) - IBM Research “Synthesizing longitudinal patient records – Restoring order to the HER entropy”
Session V – Robotics (Boardroom West) Chair - Dr. Guangnan Ye (叶广楠) – IBM Research
Mr. Yin Cui (崔崟)- Dept. of Computer Science, Cornell University “Learning deep representations for ground-to-aerial geolocatization” Mr. Jie Feng (冯捷) - Dept. of Computer Science, Columbia University “What RGBD object segmentation and retrieval bring to robot perception?”
Mr. Zheng Shou (寿政) - Dept. of Electrical Engineering, Columbia University “Instance-level action detection for robotic vision: a deep learning approach”
Dr. Guangnan Ye (叶广楠)- IBM Research “Demo: Robotics research at IBM T. J. Watson Research Center”
Session VI – Poster Presentation (Ballroom Hallway 2:20 PM – 5:30 PM)
Co-Chair: Dr. Pai-Yu Hsueh (薛沛芸) – IBM Research
Co-Chair: Dr. Hsinyu Tsai (蔡欣妤) - Manager, IBM Research
5:30 PM Networking & Social Hour 6:00 PM - 9:00 PM Banquet MC – Mr. Chiao-Wei Lee (李僑韋) Phoenix Ballroom Welcome Address – Dr. C. Eric Wu (吳振藩) – President
Presentation by Scholarship Recipients Dr. Howard Chen (陳浩)— HS Scholarship Committee Chair
Keynote Speech Dr. Charles C.-H. Hsu (徐清祥)- Chairman, eMemory Technology Inc., Taiwan “From Zero to One”
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Chinese Institute of Engineers – Greater New4 York Chapter (http://www.cie-ny.org)
Awards Ceremony Chair – Dr. Yew-Huey Liu (劉玉慧) Award Committee Chair
Distinguished Achievement Award Prof. Charles C.-H. Hsu (徐清祥) Chairman, eMemory Technology Inc., Taiwan
Distinguished Service Award Dr. Paul Tang VP, IBM Chief Health Transformation Officer
Distinguished Service Award Dr. Minda Ho (何岷達) Adjunct Professor, Zhejiang University, China Retired President, Praxair China
Institute Service Award Dr. Tien-Jen Cheng (鄭天人) CIE-USA/GNYC President (2015)
High School Student Scholarship Session Chair – Dr. Howard Chen (陳浩) HS Scholarship Committee Chair
9:00 PM - 9:30 PM Entertainment Chair – Dr. Yew-Huey Liu (劉玉慧) – Committee Chair
Phoenix Ballroom Chamber Music Ms. Shi-Yi Chiang (江詩儀), pianist
Ms. Yu-Ting Wu (吳優廷), cellist Saint-Saëns: The Swan Bach: Ariosto from Cantata No. 156 Goltermann: La Foi op. 95 No 1 Chinese folk song: Jasmine Flower in June (六月茉莉) 9:30 PM Raffle Drawing
ACKNOWLEDGEMENT: CIE-USA/GNYC thanks all committee members for their dedication and hard work that make this convention possible.
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Chinese Institute of Engineers – Greater New5 York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton Hotel, Flushing, New York Saturday, October 22, 2016
Plenary Session (1:00pm-2:20pm – Phoenix Ballroom)
Session Chair
Dr. Tien-Jen Cheng (鄭天人) Globalfoundries
Plenary Speakers
Dr. Paul Tang IBM
Dr. Minda Ho (何岷達) Zhejiang Univ.; (R) Praxair China
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Chinese Institute of Engineers – Greater New6 York Chapter (http://www.cie-ny.org)
Plenary Session
Session Chair
Dr. Tien-Jen Cheng (鄭天人) Globalfoundries, Inc. [email protected]
Biography:
Dr. Tien-Jen Cheng received his B.S. degree in Chemical Engineering from the National Taiwan University, and a Ph.D. degree in Chemical Engineering from the University of Rochester. Before joining IBM Microelectronics Division in Fishkill in 1996, he had been a Development Engineer at the Alcoa Electronic Packaging in San Diego for five years. Dr. Cheng has devoted his career to developing advanced processes for Semiconductor Manufacturing, from FEOL CVD processes for Silicon Oxide and Nitride to BEOL Low- k Inter-Level Dielectrics and Cap Layers; to the BEOL metallization, 3Di TSV (through silicon via) copper plating, wafer finishing, and C4 chip joint applications. Dr. Cheng has published over 28 US Patents and contributed to Journals and International Conferences. He has received several IBM Awards, including the Outstanding Technical Award on Semiconductor Technology development. He served as a Development Team Lead at the IBM Semiconductor Research and Development Center in Fishkill, New York, collaborating with research and development teams from internal and Partners’ teams in Yorktown and Albany Nanotech Center on the development of advanced CMOS technologies. In July 2015, Dr. Cheng joined the Advanced Technology Development (ATD) Team of the Globalfoundries Inc., the second largest foundry in the World. Currently, Dr. Cheng is focused on the development of new Cobalt Plating process for MOL application, and BEOL copper plating process for 7nm and beyond Technologies.
Dr. Cheng has been heavily involved in community activities. He was a Counselor, the Calligraphy Teacher, a Board Member, the Principal, and the Chairman of the Board at the Northern Westchester Chinese School (NWCS). Dr. Cheng later served as the Director and Treasurer for the Association of Chinese Schools (ACS) and was elected the President of the ACS and a Board of Directors for the National Council of Associations of Chinese Language Schools (NCACLS) in 2009. In 2012, Dr. Cheng joined the CIE-USA GNYC as a Life Member and was elected a Board of Director, and the Treasurer for the CIE-USA National Council. From that time, he has taken the responsibility as the Secretary, Treasurer, Vice President, and the President for the CIE-GNYC. This year, Dr. Cheng further served as the Academic Program Committee Chair at 2016 AAEOY. Presently Dr. Cheng is a Committee Chair for the CIE-USA GNYC, an occasional substitute teacher at the NWCS, and a proud volunteer at ACS Regional events.
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Chinese Institute of Engineers – Greater New7 York Chapter (http://www.cie-ny.org)
Plenary Speaker Dr. Paul Tang
Disrupting the Healthcare of Things: Personalizing Healthcare to N=1
Paul Tang, M.D., M.S., is an Internist and Vice President, Chief Health Transformation Officer at IBM Watson Health and is Consulting Associate Professor of Medicine (Biomedical Informatics) at Stanford University. At Watson Health, Dr. Tang is responsible for ensuring that IBM technologies, capabilities and data are applied to improve the health and wellbeing of individuals and communities, using cognitive insights.
Prior to coming to Watson Health, Dr. Tang was Chief Innovation and Technology Officer at the Palo Alto Medical Foundation (PAMF), overseeing the David Druker Center for Health Systems Innovation, a disruptive innovation center focused on grand challenges in health. He was the first to implement an electronic health record system in California in 1999 and co-developed the personal health record system with Epic in 2000. Currently, over 86% of PAMF patients are using the personal health record system.
Dr. Tang has dedicated his professional career to improving the quality of health care in America, innovative uses of health information technology (HIT), empowering patients through HIT, and shaping public policy to enhance health and health care in the US. He is an elected member of the National Academy of Medicine (formerly the Institute of Medicine) and has served on numerous IOM study committees, including a patient-safety committee he chaired that published two reports: Patient Safety: A New Standard for Care, and Key Capabilities of an Electronic Health Record System.
Dr. Tang is vice chair of the federal Health Information Technology Policy committee, and chair of its Advanced Health Models and Meaningful Use workgroup. He has served as board chair for several health informatics professional associations, including the American Medical Informatics Association (AMIA). Dr. Tang serves on the board of the National Quality Forum and chairs its Health Information Technology Advisory Committee. He also serves on the board and Executive committee of Academy Health. He is a recipient of the AMIA Don E. Detmer Award for Health Policy Contributions in Informatics.
Dr. Tang has served on numerous committees of the National Institutes of Health, National Research Council, National Academy of Sciences, Institute of Medicine, and Computer Science and Technology Board. He is a Fellow of the American College of
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Chinese Institute of Engineers – Greater New8 York Chapter (http://www.cie-ny.org)
Physicians, the American College of Medical Informatics, the College of Healthcare Information Management Executives, and the Healthcare Information and Management Systems Society. He has published numerous papers in medical informatics, especially related to electronic health records, personal health records, health care quality, and public policy, and has delivered over 340 invited presentations to national and international organizations and associations.
Dr. Tang received his B.S. and M.S. in Electrical Engineering from Stanford University and his M.D. from the University of California, San Francisco. He completed his residency in Internal Medicine at Stanford University and is a Board-certified Practicing Internist.
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Chinese Institute of Engineers – Greater New9 York Chapter (http://www.cie-ny.org)
Plenary Speaker Dr. Minda Ho (何岷達)
When an Engineer Meets a Business Manager - The Learning Process that Transforms Engineers into Business leaders
Min-Da Ho ( 何岷達) received his Ph.D. degree in chemical engineering from Carnegie Mellon University in 1983. After graduation, he started his professional career at Union Carbide Corporation in New York, conducting research in combustion energy, wastewater treatment, and hazardous waste incineration. He moved to Shanghai in 1997 and served as the director of business development for Praxair China. Under his leadership, Praxair China grew its industrial gas business exponentially through the establishment of 10 joint ventures and 20 wholly owned subsidiaries, and generated an annual revenue over US$1 billion. He was appointed the vice president of Praxair China in 2003, the executive vice president of Praxair China in 2007, global vice president of gasification in 2009, and the president of Praxair China in 2010.
Dr. Ho’s distinguished career is characterized by a unique combination of his innovation, technical expertise, entrepreneurship and business acumen. In 1989, the Union Carbide team led by Dr. Ho won the Kirkpatrick Chemical Engineering Achievement Award, the highest honor bestowed by Chemical Engineering Magazine in the chemical processing industry. In addition to holding 9 U.S. patents, he is the only person in Praxair history to be honored with Chairman Award more than once. In fact, he received the Chairman Award 3 times for growing Praxair China into a profitable US$1 billion business with high standards of safety, integrity and compliance, for developing and executing strategic business growth strategy and capital projects, and for developing and commercializing environmental and waste management technologies.
As one of the pioneers to champion the concept of chemical industrial parks and utility islands in such well-designed industry parks in China, Dr. Ho was honored with the Shanghai White Magnolia Award in 2011, in recognition of his contribution to the Shanghai Chemical Industry Park, the most successful showcase in China. Dr. Ho also played a significant role in the development and promotion of pure oxygen based clean coal gasification technology for synthetic fuel and chemical feedstock, making China a dominant world leader in the area.
Under Dr. Ho’s leadership, Praxair was not only selected as one of the best employers in China by the Fortune magazine, but also recognized for its exemplary corporate social responsibility (CSR) initiatives. During his 20-year tenure in China, Dr. Ho has served as the chairman of Shanghai American School Board, and promoted diversity, integrity and green sustainability in engineering profession through sponsorship, cooperation and
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Chinese Institute of Engineers – Greater 10New York Chapter (http://www.cie-ny.org)
personal lectures in leading universities. He is also a founding board member of Shanghai Charity Foundation – United Way Worldwide Fund, and instrumental in the development of the Sprout program for the education of migrant workers’ children. After 32 years of services, Dr. Ho retired from Praxair, Inc., a U.S. Fortune 300 company, in 2015. He is currently an adjunct professor of chemical and biological engineering at Zhejiang University.
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Chinese Institute of Engineers – Greater 11New York Chapter (http://www.cie-ny.org)
Convention Chair
Dr. Kun-Lung Wu (吳坤龍) Manager, Data-Intensive Systems and Analytics Group Manager, IBM Streams SPL Language Team IBM T. J. Watson Research Center 1101 Kitchawan Road Yorktown Heights, NY 10598 klwu@us.ibm.com
Biography:
Dr. Wu is the Manager of the Data-Intensive Systems and Analytics Group at the IBM T. J. Watson Research Center. He is also the Manager of the IBM Streams SPL Language Team, IBM Analytics Platform. The combined team currently engages in the research and product development of the IBM Streams product -- a real-time analytics platform for large-scale, distributed stream processing. In addition, his group studies various research issues in Big Data systems and analytics -- including programming language and model for stream processing; automatic exploitation of data parallelism for streaming applications; fault tolerance and application resiliency in stream computing; advanced graph analytic algorithms for stream applications; parallel streaming algorithms for exploiting multicore processors; job management and scheduling, resource management and system optimization for Big Data systems.
An IBM Master Inventor, Dr. Wu is also a Fellow of the IEEE, and a member of the ACM. From 2000 to 2004, he was an Associate Editor for IEEE Transactions on Knowledge and Data Engineering. He also served as an organizing or program committee member for many international conferences and workshops. He has received several IBM awards, including an IBM Corporate Environmental Affair Excellence Award, an Outstanding Technical Achievement Award, a Research Division Award, and many Invention Achievement Awards. He has published extensively in various journals and refereed conferences, and received several best paper awards. He also holds or has applied for over 75 patents.
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Chinese Institute of Engineers – Greater 12New York Chapter (http://www.cie-ny.org)
Program Chair:
Dr. Ching-Yung Lin (林清詠) IBM Chief Scientist, Graph Computing IBM T. J. Watson Research Center 1101 Kitchawan Road Yorktown Heights, NY 10598 [email protected]
Biography: wh
Dr. Ching-Yung Lin is the IBM Chief Scientist, Graph Computing, and an IBM Distinguished Researcher. He is the founder and senior manager of the Network Science and Machine Intelligence Department in IBM T. J. Watson Research Center. He has been with IBM Research since 2000, after receiving his Ph.D. from Columbia University. Dr. Lin is also an Adjunct Associate/Full Professor in the Departments of Electrical Engineering and Computer Science, Columbia University since 2005. He was an Affiliate Assistant/Associate Professor in the University of Washington from 2003 to 2009, and an Adjunct Professor in New York University (NYU) in 2014.
His research interests are on fundamental research of machine learning, artificial intelligence, data mining, multimodality signal understanding, network science, brain analysis, and applied research on security, commerce, and collaboration. Lin was elevated to IEEE Fellow for "contributions to network science and multimedia security and retrieval" in Nov 2011. He was the first IEEE fellow cited for contribution to Network Science.
He is leading a big R&D project of “IBM System G,” which is dedicated to advance science, technology, and industry cognitive solutions based on graph computing, inspired by graph/network being the foundation of brain. He has been a Principal Investigator of many external funded (~$25M) projects: DARPA Anomaly Detection at Multiple Scales (ADAMS), DARPA Social Media in Strategic Communications (SMISC), ARL Social and Cognitive Network Academic Research Center (SCNARC), DHS Mobile Security, and several other projects with major global companies and worldwide governments, across the industries of Governments & Public Sector, Financial Services Sector, Aerospace Sector, Telecommunication Sector, Healthcare Sector, and Energy Sector. He is also leading IBM worldwide research on cognitive solutions for investment & commercial banks and insurance, especially in the areas of Fraud, Surveillance, Risk, Compliance, Anti-Money Laundering, Espionage, Sabotage, etc, and led to create the first cognitive security product in the financial industry — IBM Surveillance Insight for Financial Services. His team focuses on all aspects of Graph Computing -- graph database, high performance and distributed computing infrastructure, graph analysis library, graph visualization, and graphical models that form the foundation of machine judgment, reasoning and strategy, such as deep learning, Bayesian networks, game theory tools, etc.
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Chinese Institute of Engineers – Greater 13New York Chapter (http://www.cie-ny.org)
Ching-Yung is an author of 160+ publications and 23 awarded patents (Google Scholar: 10,000+ citations, hindex: 53, first-author paper citations: 3,500+). In 2010, IBM Exploratory Research Career Review selected Dr. Lin as one of the five researchers "mostly likely to have the greatest scientific impact for IBM and the world.”
He is teaching “Big Data Analytics” and “Advanced Big Data Analytics” graduate courses in Columbia University in the Fall and Spring semesters. In the course evaluations of the last 4 years, in total, 53% of the students rated Prof. Lin's overall teaching as 'excellent' and 28% rated 'very good'. In a 1-5 scale, the mean was 4.25 and the median was 5 (excellent). His classes attract more than 300 students per year, among the largest in the Columbia Engineering School.
He invented and created the SmallBlue system, an IBM production system for Enterprise Social Network Analysis, Expertise Search, and Knowledge Recommendation since 2008. SmallBlue helped IBM Corporation won the 1st place in 2012 Most Admirable Knowledge Enterprise (MAKE) Award in enterprise-wide collaboration knowledge- sharing environment. In May 2013, SmallBlue was selected by APQC as the Industry Leader and Best Practice in Expertise Location. In October 2013, SmallBlue was recognized as having made $117M+ productivity contribution to IBM.
Lin was one of the earliest machine learning researchers on large-scale visual understanding & reasoning. In 2003, Lin created and led 111 researchers in 23 worldwide research institutes for the first large-scale collaborative video semantic annotation project. In 2005, he pioneered the design of a semantic filtering framework which detects more ~150 visual concepts in videos. His multimedia semantic mining project team performed best in the annual US National Institute of Standards and Technology (NIST) semantic video concept detection benchmarking 2002-2004.
Lin is a recipient of 2003 IEEE Circuits and Systems Society Outstanding Young Author Award, IBM Invention Achievement Awards in 2001, 2003, 2007, 2010 and 2011 & 2013, IBM Research Division Award 2005 & 2013, IBM Corporate Outstanding Innovation Award 2011, 2013 & 2014, Association of Information Systems (AIS) Intl. Conf. on Information Systems (ICIS) 2011 Best Theme Paper Award, Acer Best EECS Thesis Award 1993, and the Outstanding Paper Award in CVGIP 1993. His work was featured 4 times by the BusinessWeek magazine, including being the Top Story of the Week on April 9th, 2009. His team won the Best Paper Awards in ACM Intl. Conf. on Knowledge and Information Management (CIKM) 2012 and IEEE International Congress on Big Data (BigData) 2013. His extended team’s papers were selected as the cover paper of Proc. of National Academy of Science (Jan 2013), and were on Science and Nature (twice).
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Chinese Institute of Engineers – Greater 14New York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton Hotel, Flushing, New York Saturday, October 22, 2016
Session I
Science and Technology (2:40pm-4:00pm – Boardroom East)
Session Chair
Dr. Chia-Yu Chen (陳家佑) IBM Research
Session Speakers
Dr. Ching-Tzu Chen (陳敬慈) IBM Research
Dr. Qing Cao (曹庆) IBM Research
Mr. Jintao Zhang (张金涛) Princeton University
Dr. Chi-Chun Liu (劉其俊) IBM Research
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Chinese Institute of Engineers – Greater 15New York Chapter (http://www.cie-ny.org)
Session I: Science and Technology
Session Chair
Dr. Chia-Yu Chen (陳家佑) Research Staff Member IBM TJ Watson Research Center [email protected]
Biography:
Chia-Yu Chen received the B.S. degree from National Central University, Taoyuan, Taiwan, R.O.C. and the M.S. and Ph.D. degree from Stanford University, Stanford, CA in 2012 both in electrical engineering. He is currently a Research Staff Member in IBM T.J. Watson Center, Yorktown Heights NY. Dr. Chen had several industry and academic experiences, in 2006 he was with KLA-Tencor, 2007 with Berkeley Design Automation, 2008 with Taiwan Semiconductor Manufacturing Company (TSMC), 2009 with Advanced Micro Devices (AMD), 2010 with Intel, and 2012-2013 with MIT Microsystems Technology Laboratories (MTL). Dr. Chen joined IBM research in 2014 and recently his research mainly focuses on customized machine learning accelerators and its compiler designs.
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Chinese Institute of Engineers – Greater 16New York Chapter (http://www.cie-ny.org)
Spintronics: Science and Technology
Session Speaker:
Dr. Ching-Tzu Chen (陳敬慈)
Biography:
Ching-Tzu Chen is a Research Staff Member at the IBM Thomas J Watson Research Center. She graduated with a bachelor degree from National Taiwan University and received her PhD in physics from the California Institute of Technology. Her doctoral research focuses on the novel physics of high-temperature superconductors at nanoscale using scanning probe spectroscopy, for which she was awarded the Los Alamos National Laboratory Director Fellowship. She joined IBM Research in 2007 and has since participated in various projects related to spintronics, nanoscience and nanomaterials including graphene, and superconductivity. Her work on the fundamental physics of superconductivity has been awarded the IBM Outstanding Achievement Award. Her current research focuses on the generation, conduction, and manipulation of spins for beyond silicon CMOS computing.
Abstract:
The quest for ultralow-energy, high-performance computing has prompted extensive research in spintronics. The discovery of giant electrical-resistance modulations from the change in magnetic (namely spin) polarization brought magnetic read-heads to the market. The nonvolatile nature of magnetism and the fundamental breakthrough that enabled electrical control of spins further turned magneto-resistive random access memory (MRAM) into reality. In this short talk, I will explain the working principles behind these spintronic devices, review the current challenge for enhancing their operating speed in traditional computing, outline the research directions for solutions, and conclude with potential applications for non-traditional computing paradigms.
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Chinese Institute of Engineers – Greater 17New York Chapter (http://www.cie-ny.org)
Recent Progress on High Performance Logic Electronics Based on Carbon Nanotubes
Session Speaker:
Dr. Qing Cao (曹庆) Research Staff Member IBM research [email protected]
Biography:
Dr. Cao received his Ph.D. degree in Materials Chemistry from the University of Illinois at Urbana-Champaign in 2009. He then joined IBM T.J. Watson Research Center as a postdoctoral scientist in the Thin-Film Photovoltaic Science & Technology group. Currently, he works as a research staff member in the Nanoscale Electronic Device Group. His research interests include functional nanomaterials for unconventional electronic systems, high-performance logic devices, and low-cost energy harvesting.
Dr. Cao is a recipient of MIT Technology Review TR35 (2016), IBM Research Division Award (2016), U.S. Frontiers of Engineering by National Academy of Engineering (2016), IBM Outstanding Technical Achievement Award (2015), Forbes Magazine's "Top 30 under 30" Award in Science (2012), Forbes "30 under 30" All-Star Alumni Award (2016), IBM Invention Achievement Award (2011-2016, 13 times).
Abstract:
Conventional scaling of Si complementary metal-oxide semiconductor (CMOS) devices provided ever-improved transistor performance, density, power, and cost in the last four decades. However, it has become very difficult in recent 10 years with Si devices approaching their physical limits. In search for the next switch beyond silicon, carbon nanotubes are a very promising candidate, with a saturation velocity several times faster than silicon and intrinsic thinness (~ 1 nm in diameter) which enables superior electrostatic control to minimize the off-state leakage current even at ultra-small device dimensions. Here we discuss our recent advances in building high performance nanotube transistors with extremely scaled device dimensions including both device channel length and contact length, separation and assembly techniques for forming nanotube arrays with high semiconducting nanotube purity and tight pitch, as well as experimental and modeling works to study the variability characteristics of scaled carbon nanotubes transistors. These results suggest that replacing Si with carbon nanotubes in high-performance logic devices at 5 nm technology node and beyond is feasible. A concluding discussion highlights most significant challenges remained and provides perspectives on the future of carbon nanotube based nanoelectronics.
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Chinese Institute of Engineers – Greater 18New York Chapter (http://www.cie-ny.org)
Why Mix-Signal Can Benefit Machine Learning
Session Speaker:
Mr. Jintao Zhang (张金涛) Ph.D. candidate Princeton University [email protected]
Biography:
Jintao is currently a 5th year Ph.D. Candidate in Princeton University. He received his B.S. in Electrical Engineering from Purdue University in 2012. Jintao’s research has been focus on applying machine learning algorithms in hardware, both the conventional digital approach and mix-signal approach. He is now working in IBM T.J. Watson Center on customized machine learning accelerator as an Internship.
Abstract:
Emerging machine learning algorithms has been employed in many applications successfully, however, most of these algorithms are not energy-efficient in general- purpose realization. Analog computation, on the other hand, can be much energy- efficient. The challenge of analog computation arises as the limitation of dynamic range and non-ideality, compare to the digital approach. Interestingly, most machine learning algorithms care only about the distribution of the data between classes. This brings us the opportunity, as our group named “Data-Driven Hardware Resilience”, or DDHR, which uses machine learning to fit not only the data distribution, but also the non-ideal effect in hardware. With the proper combination of hardware, algorithm and application, this approach can usually get very low-power machine learning accelerators.
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Chinese Institute of Engineers – Greater 19New York Chapter (http://www.cie-ny.org)
Patterning Options with Directed Self-Assembly for Advanced Technology Node
Session Speaker:
Dr. Chi-Chun (Charlie) Liu (劉其俊) IBM T. J. Watson Research Center [email protected]
Biography:
Chi-Chun (Charlie) Liu received his BS and MS degrees in Chemical Engineering from National Taiwan University and PhD in Chemical Engineering from the University of Wisconsin – Madison in 1998, 2000, and 2011 respectively. Before he started his PhD study in 2005, he worked at Taiwan Semiconductor Manufacturing Company (tsmc) as an etch process engineer for ~3 years. Dr. Liu joined IBM in 2011 and he is currently working at IBM Albany NanoTech as a Research Staff Member focusing on Directed self-assembly for patterning applications. He has published over 35 peer-reviewed journal articles, which have been cited over 1300 times. His current technical focus includes DSA material study, imaging analysis, DSA compact model and Monte Carlo simulation, DSA process development, optimization, and implementation. He also serves as a program committee member for SPIE Advanced Lithography and EIPBN.
Abstract:
Directed self-assembly (DSA) of block copolymers (BCPs) has drawn great attention in logics, hard disk drive, and memory applications due to its material-controlled patterning capability, including density multiplication and defect/feature size rectification. Recent studies on 193i/High Volume Manufacturing (HVM) compatibility, defectivity, and device demonstration of DSA further reinforce its role as a potential candidate for lithography extension rather than merely a lab-scale nanofabrication method. Lately, many research groups have illustrated structures for specific applications using a variety of chemoepitaxy and graphoepitaxy DSA processes, such as sub-30nm pitch line/space array for FinFET, hexagonal array of holes for DRAM, elongated holes/bars for vias/contacts, and posts for memory applications. In this talk, we will review the fundamentals of DSA and discuss the advantages and challenges of several potential DSA applications in logics and memory field based on published literature.
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Chinese Institute of Engineers – Greater 20New York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton LaGuardia East Hotel, Flushing, New York Saturday, October 22, 2016
Session II
Cognitive & Analytics (2:40pm-4:00pm –Boardroom West)
Session Chair
Prof. Winston Hsu (徐宏民) National Taiwan University
Session Speakers
Dr. Liangliang Cao customerserviceAI.com
Dr. Kerry Shih-Ping Chang (張詩平) IBM Research
Dr. Chi Xiong (熊驰) IBM Research
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Chinese Institute of Engineers – Greater 21New York Chapter (http://www.cie-ny.org)
Session II: Cognitive & Analytics
Session Chair:
Prof. Winston Hsu (徐宏民) Visiting Scientist, IBM TJ Watson Research Center [email protected]
Professor National Taiwan University [email protected]
Biography:
Prof. Winston Hsu is a visiting scientist – cognitive computing in IBM TJ Watson Research Center. He is an active researcher dedicated to large-scale image/video retrieval, social media mining, visual recognition, and machine intelligence. He is keen to realizing advanced researches towards business deliverables via academia-industry collaborations and co-founding startups. He is now a Professor in the Department of Computer Science and Information Engineering, National Taiwan University, also a Visiting Scientist at Microsoft Research Redmond (2014). He received his Ph.D. degree (2007) from Columbia University, New York. Before that, he worked for years as a founding engineer in the multimedia software company, CyberLink Corp., where experiencing Engineer, Project Leader, and R&D Manager. He has been recognized with technical awards in multimedia research community. He was awarded 2011 Ta-You Wu Memorial Award, a national and prestigious recognition for young researchers, and 2013 National Outstanding IT Elite Award, for his contributions in advanced research and industry collaborations.
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Chinese Institute of Engineers – Greater 22New York Chapter (http://www.cie-ny.org)
Propelling Artificial Intelligence: from Games to Customer Service AI
Session Speaker:
Dr. Liangliang Cao Chief Scientist/Co-Founder customerserviceAI.com [email protected]
Biography:
Dr. Liangliang Cao is the chief scientist and co-founder of customerserviceAI.com. He is also an adjunct professor at Columbia University. Before his startup, he was a Senior Scientist at Yahoo and a Research Staff Member at IBM Watson Research. He won the first place of ImageNet Challenge in 2010. His research has been reported by a number of media press including Vice, and Scientific American.
Abstract:
The last decade has witnessed great progress in artificial intelligence. In this talk, we will first introduct the PokerCNN project, and discuss whether AI outperforms human player. We would like to discuss three keys of solving these challenges: (1) how to help the computer to understand challenging questions; (2) how to benchmark the AI; and (3) how to scale up the computational power for AI. Based on these lessons, we will at last briefly introduce our current project, which develops AI to revolutionize customer service with fast responses at a fraction of the cost.
22
Chinese Institute of Engineers – Greater 23New York Chapter (http://www.cie-ny.org)
A Spreadsheet Model for Using Web Services and Creating Data-driven Applications
Session Speaker:
Dr. Kerry Shih-Ping Chang (張詩平) Research Staff Member IBM TJ Watson Research Center [email protected]
Biography:
Dr. Kerry Shih-Ping Chang (張詩平) is a research staff member at IBM TJ Watson Research Center. She recently received her PhD from the Human-Computer Interaction Institute at Carnegie Mellon University in May, 2016. Kerry's research explores novel tools that assist users in various aspects of the software development process, such as debugging, testing, and creating tutorials and documentations. Her work has been published at top HCI conferences such as CHI, UIST, UbiComp and VL/HCC.
Abstract:
Today there are lots data and analytics services available on the Internet. However, to use these services often requires significant programming efforts and thus limits the people who can take advantage of them to only a small group of skilled programmers. How can we enable more people to efficiently collect, analyze and present their data like professional programmers do? In this talk, I will present a programming tool that I developed called Gneiss, which aims to enable end-users to use web services and create data-driven applications using spreadsheets. Gneiss extends the spreadsheet model to support four challenging aspects of using web services: programming two-way data communications with web services, creating interactive GUI applications that use web services, analyzing hierarchical JSON data, and analyzing streaming data. Gneiss contributes innovations in spreadsheet languages, spreadsheet user interfaces and interaction techniques to allow programming tasks that currently require writing complex, lengthy code to be done using familiar spreadsheet mechanisms. Spreadsheets are arguably the most successful and popular data tools among people of all programming levels. This work advances the use of spreadsheets to new domains and could benefit a wide range of users from professional programmers to end-user programmers.
23
Chinese Institute of Engineers – Greater 24New York Chapter (http://www.cie-ny.org)
Silicon photonics for data communications and gas sensing
Session Speaker:
Dr. Chi Xiong (熊驰) Research Staff Member IBM TJ Watson Research Center [email protected]
Biography:
Dr. Chi Xiong is currently a Research Staff Member in the Silicon Photonics group at IBM Thomas J. Watson Research Center. Dr. Xiong’s research at IBM focuses on silicon photonics for data communication and on-chip trace gas spectroscopy. His research interests include silicon traveling-wave modulators, advanced modulation format, quantum optics, and optomechanical resonators.
Dr. Xiong received the B.S. degree in microelectronics from Peking University in 2006, and the Ph.D. degree in electrical engineering from Yale University in 2012. His doctoral dissertation focused on chip-scale second order nonlinear optics and high-frequency piezo-optomechanical resonators. From 2012 to 2013, Dr. Xiong was a postdoctoral associate at Yale University. From 2013 to 2015, he was a postdoctoral researcher at IBM Research. He has published more than 25 journal articles and holds one U.S. patent. In 2016, Dr. Xiong received an Outstanding Technical Achievement Award from IBM for his contribution to develop silicon photonics toward commercialization.
Abstract:
Silicon photonics promises to transform data communications through low cost manufacturing of photonics and electronics on a single platform. At IBM, we are developing a sub-100nm monolithic silicon photonics technology (CMOS9WG) for wavelength-multiplexed short reach transceiver applications. In the first part of the talk, I will present our reference design of a four channel 100 Gb/s transceiver. In addition, I will show our recent results of a 56 Gb/s pulse-amplitude modulation transmitter.
In the second part of the talk, I will explore extending the current capabilities of our technological platform to trace-gas sensing. Natural gas leaks from production wells and pipelines pose a significant environmental risk due to the strong greenhouse effect caused by its main constituent, methane. I will present a silicon photonic methane sensing platform, which uses laser spectroscopy to realize cost-effective sensor networks for fugitive methane emission monitoring.
24
Chinese Institute of Engineers – Greater 25New York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton LaGuardia East Hotel, Flushing, New York Saturday, October 22, 2016
Session III: Entrepreneurship
(2:40pm-4:30pm – Phoenix Ballroom)
Session Chair
Mr. Fred Yan (顏為民) Educational Testing Service
Session Speakers
Mr. Bicheng Han (韩璧丞) BrainCo, Inc./Harvard University
Mr. Jian Chen (陈箭) EDETEK
Mr. Seamon Chan (陈希孟) Palm Drive Ventures
Dr. Joseph Barba CUNY
Dr. Tony Zhang (张弢) Fish & Richardson
25
Chinese Institute of Engineers – Greater 26New York Chapter (http://www.cie-ny.org)
Session III: Entrepreneurship
Session Chair:
Mr. Fred Yan (顏為民) Principal Research Data Analyst Research Division Educational Testing Service [email protected]
Biography:
Fred Yan is a Principal Research Data Analyst in the Research Division of Educational Testing Service. Besides working on various data analysis projects in education and testing fields, he had been a key contributor to a diagnostic test scoring software system.
After graduating from Tianjin University with a Bachelor’s degree in Engineering Mechanics, Fred obtained a master’s of science degree in Civil Engineering from Pennsylvania State University, focusing on Computational Fluid Mechanics. He also completed course requirements for a master’s in Computer Science in NJIT.
Fred had been a student member of CIE in the 1990s, and has been actively involved in CIE-GNYC Executive Committee since 2006, acting as a member of the Board of Directors and as secretary. He had been the vice chair of CIE-GNYC annual convention from 2011-2015.
26
Chinese Institute of Engineers – Greater 27New York Chapter (http://www.cie-ny.org)
Neuro feedback solutions enabling interaction between Human Brain and Hardware devices
Session Speaker:
Mr. Bicheng Han (韩璧丞) CEO & Founder of BrainCo. Inc PhD Program Harvard University Center of Brain Science
Biography:
Bicheng Han, a PhD student at Harvard University Center of Brain Science dedicates his days and nights to making this science fiction technology a reality. In 2014, he founded BrainCo, a company focused on creating the world’s most advanced neurofeedback solution.
Abstract:
The human body is a well-designed machine, every part connect and communicate with each other through electronic signals, from your brain to every fiber of your being. To harness that power and use our own bio-signal as a mean to communicate with computers is an unthinkable task. BrainCo, a company focused on creating the world’s most advanced neurofeedback solution BrainCo’s product, Focus 1, was created to allow users interact with both software and hardware via brain status, and while this entertaining process takes place, a permanent reinforcement is applied almost automatically to improve user’s concentration. On top of that, BrainCo Robotics, a recent branch of the tech startup begin their research into muscle signal detection and created an incredible robotic hand that could easily be adapted as prosthetics with the help of machine learning.
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Chinese Institute of Engineers – Greater 28New York Chapter (http://www.cie-ny.org)
Experience in Creating and Developing an Innovative Clinical Technology Company
Session Speaker:
Mr. Jian Chen (陈箭) President EDETEK
Biography:
Jian Chen is the president of EDETEK, Inc. He is the chief architect for Panther Clinical Trial Management Platform (CTMP). Jian actively participates in multiple CDISC standard development teams, and has been involved in hundreds of clinical studies across all therapeutic areas. Jian has a Masters in Biomedical Engineering and Biostatistics, a Masters in Management in Information Systems, and an MBA.
Abstract:
In this session, Jian will share his experience in developing a hi-growth technology company, EDETEK Inc., in a highly regulated environment. He will share his vision of how to manage a young company, including business strategies, financial management, risk management, employee motivation, and collaboration. Jian will share his experience throughout the company’s development stages, how to overcome the initial fear and anxiety, how to promote the company, how to segment the market, and how to develop different growth strategies. Jian will discuss when VC funding should be introduced, what needs to be considered when balancing risk and growth.
EDETEK
EDETEK, Inc. is a technology platform, product and business services company focusing on the needs of Clinical Research and Development in the Lifesciences Industry. Our business benefits constantly from our utilization of our innovative technology products to provide targeted business services to our clients in the Pharmaceutical, Biotech and Medical Device sectors.
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Chinese Institute of Engineers – Greater 29New York Chapter (http://www.cie-ny.org)
Tech Startup from VC Perspective
Session Speaker:
Mr. Seamon Chan (陈希孟) Co-Founder Palm Drive Capital Lair East
Biography:
Seamon Chan is Co-Founder and Managing Partner of New York-based Palm Drive Capital, a venture capital and growth equity firm that invests mostly in US technology companies and supports them with a network in New York, Silicon Valley, and China. Portfolio companies include Jet.com, Zenefits, Addepar, WeLab, Bench Accounting, Hyperloop, Magic Leap, and Boom Aero. Seamon is also Co-Founder of Lair East, an innovation space in SoHo, New York bridging East and West through tech and culture. Previously, he was at Insight Venture Partners, a $13 billion venture capital and private equity firm with investments in Jingdong and Alibaba. Seamon brings entrepreneurship and operating experience through various startup roles in the mobile, gaming, advertising, and social networking spaces in US, China, and Europe. Earlier in his career, he has worked at enterprises such as Hitachi Data Systems and Citigroup, as well as research labs such as Stanford Artificial Intelligence Lab and Center for Integrated Systems. Seamon graduated from Stanford and was on the founding teams of Stanford Chinese Entrepreneurs Organization (Stanford CEO) and NYC Chinese Entrepreneurs Organization (NYCCEO), both organizations with thousands of members and alumni in US and China.
Abstract:
As successful scientists and engineers, why get involved with a startup? There are many career choices these days so what are the advantages and disadvantages? What are the qualities needed and the risks to consider? How to develop these qualities and mitigate the risks? How to build something that people want and scale it? How to recruit the right team members, think about raising capital, and build a successful startup?
29
Chinese Institute of Engineers – Greater 30New York Chapter (http://www.cie-ny.org)
Student Entrepreneurship at CCNY: The Zahn Innovation Center and the IN2NYC program
Session Speaker:
Dr. Joseph Barba Professor of Electrical Engineering Founder of the CUNY Zahn Innovation Center Director of Student Entrepreneurship The City College of New York, CUNY 160 Convent Avenue, Steinman Hall [email protected]
Biography:
Dr. Joseph Barba holds a Ph.D. in electrical engineering from The City University of New York and is professor of Electrical Engineering at the City College of New York. He has served as the Deputy Provost of the City College (2000 – 2004) and is the founding dean of the Grove School of Engineering (2004 - 2013). During his tenure as dean he spearheaded the development of the Grove School of Engineering student entrepreneurship program leading to the establishment of the endowed $50,000 annual Kaylie Prize in Entrepreneurship in 2010 and the established the Zahn Innovation Center for entrepreneurship in 2012. He spearheaded university IN2NYC program with the New York Economic Development Corporation – an H-1B visa program to bring international entrepreneurs to New York City. He currently serves as the Director of Student Entrepreneurship at the City College and also serves as Director of the STEM Institute, an intensive summer enrichment program for NYC high school students interested in math, science, and engineering careers.
Abstract
My presentation will cover student entrepreneurship at the City College of New York focusing on the Zahn Innovation Center, a startup incubator offering co-working space and an array of resources including: annual competitions, a startup bootcamp, mentorship and pro-bono services, networking opportunities, and rapid prototyping facilities. I will also describe the IN2NYC program being piloted this year through the Zahn Center in collaboration with the NYC Economic Development Corporation. This is a scalable cap exempt H-1B visa program to bring international entrepreneurs to NYC. By forming collaborative agreements the entrepreneurs will utilize their expertise to contribute to the school’s mission.
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Chinese Institute of Engineers – Greater 31New York Chapter (http://www.cie-ny.org)
Common IP Issues for Entrepreneurs
Session Speaker:
Dr. Tony Zhang (张弢), Ph.D., J.D. Principal Fish & Richardson P.C.
Biography:
Dr. Tony Tao Zhang is a Principal in the New York office of Fish & Richardson. Dr. Zhang’s practice emphasizes patent prosecution and counseling, opinion work, due diligence studies, and development and management of patent portfolios. Dr. Zhang has substantial patent prosecution experience in various sub-fields of chemistry including organic chemistry, inorganic chemistry, physical chemistry, and biochemistry.
Technical areas of Dr. Zhang’s patent prosecution work include pharmaceuticals, nutraceuticals (e.g., herbal extracts), drug formulations, chemical synthetic methods, nanotechnologies, medical devices (e.g., stents, biosensors. and biochips), fuel cells, solar cells, catalysts, semiconductor materials, and polymers. Dr. Zhang has previous legal experience as a patent agent and technology specialist with Fish & Richardson.
Dr. Zhang obtained his BS degree in Chemistry from Peking University, PhD in Chemistry from Rensselaer Polytechnic Institute, and JD from Suffolk University Law School.
Abstract:
Tony will discuss common IP issues that entrepreneurs may encounter, such as ownership, inventorship, patents, trade secrets, employment agreements, and confidentiality agreements.
31
Chinese Institute of Engineers – Greater 32New York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton LaGuardia East Hotel, Flushing, New York Saturday, October 22, 2016
Session IV Healthcare & Life Sciences Informatics Challenges in a Data-Driven World
(4:10pm-5:30pm –Boardroom East)
Session Chair
Dr. Henry Chang (張鴻洋) IBM Research
Session Speakers
Dr. Kun Lin (林堃) IBM Research
Dr. Tian Hao (郝添) IBM Research
Dr. Ching-Huei Tsou (鄒慶暉) IBM Research
32
Chinese Institute of Engineers – Greater 33New York Chapter (http://www.cie-ny.org)
Session IV: Healthcare and Life Sciences
Informatics Challenges in a Data-Driven World
Session Chair:
Dr. Henry Chang （張鴻洋） Center for Computational Health IBM Thomas J. Watson Research Center 1101 Kitchawan Rd, Yorktown Heights, NY 10598 [email protected]
Biography:
Henry Chang is a senior technical staff member at IBM research where he leads the investigation of the cognitive health coaching paradigm for real-time personalized health advises using bio-metric wearables and clinic knowledge bases. Henry got his Ph.D. and M.S. of Computer Sciences in Wisconsin-Madison, but migrated to Healthcare Informatics through a multi-year technology research program for chronic disease care such as diabetes with community hospitals since 2010.
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Chinese Institute of Engineers – Greater 34New York Chapter (http://www.cie-ny.org)
Challenges in Transitioning from Pay-for-Service to Pay-for- Value
Session Speaker:
Dr. Kun Lin (林堃) Center for Computational Health IBM Thomas J. Watson Research Center 1101 Kitchawan Rd, Yorktown Heights, NY 10598 [email protected]
Biography:
Kun Lin is a research engineer at IBM research. He earned his Doctorate in Electrical and Computer Engineering from the University of Maryland – College Park. He studied control theory and optimization during his time at Maryland. He has developed and implemented various algorithms for manufacturing, communication, finance and healthcare.
Abstract:
In recent years, there has been an increasing emphasis on enabling value-driven healthcare, aimed at improving outcomes, lowering costs, and increasing overall access to care for patients prompted by the Affordable Care Act. Value-driven approach to healthcare is a significant departure from our traditional fee-for-service model; hence, we are facing with many challenges during this transition.
In this talk, we will highlight these challenges and offer some technology-enabled solutions.
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Chinese Institute of Engineers – Greater 35New York Chapter (http://www.cie-ny.org)
Is Wearable Sensing Turning Us into Cyborgs?
Session Speaker:
Dr. Tian Hao (郝添) Center of Computational Health IBM T.J. Watson Research Center [email protected]
Biography:
Tian Hao is a researcher at IBM Research where he develops technologies that employ mobile sensing and smart devices to deliver effortless daily health/fitness tracking and personalized feedback. Currently, he is spearheading a project on stress management which aims to help users track and reduce stress using wearables. Prior to joining IBM Research, he co-founded HAO Tech, a spinout startup where he managed to tailor and transfer the sleep-tracking technology to industry.
Tian Hao obtained his PhD in Computer Science at Michigan State University, where he gained hands-on experience in developing disruptive mobile sensing systems, such as iSleep, the first sound-based smartphone system that offers contact-free sleep status and snoring tracking. He orchestrated experiments involving human subjects for feasibility and performance evaluation, and prototyped research ideas into award-winning mobile applications (MobiCom Best App Award 2013 and 2014). The results of his research have been acknowledged in conferences including MobiSys, SenSys and UbiComp.
Abstract:
In this talk, Tian will discuss the current progress and future trends about human-centric sensing technologies. He will also share his understanding of health- and fitness-related sensing through several of his research projects, which aim to provide users with deeper insights into their daily activities ranging from sleeping, running and meditation. He will also talk about how to turn awareness into positive actions using personalized feedback.
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Chinese Institute of Engineers – Greater 36New York Chapter (http://www.cie-ny.org)
Synthesizing Longitudinal Patient Records - Restoring Order to the EHR Entropy
Session Speaker:
Dr. Ching-Huei Tsou (鄒慶暉) Senior Software Engineer IBM Watson Research 1101 Kitchawan Rd, Yorktown Heights, NY 10598 [email protected]
Biography:
Dr. Ching-Huei Tsou is a senior software engineer in the Watson EMRA (electronic medical records analysis) group at IBM Research. His area of specialty includes machine learning, numerical optimization, and software engineering. Dr. Tsou joined the Watson research team in 2012, after Watson won the Jeopardy! TV show and shifted the research focus to adapting for medical domain. His current research focus is on building health informatics that support clinical cognition.
Prior to join IBM, for 6 years Dr. Tsou was the principle software architect of LightPharma Inc., a Cambridge, MA based software company specialized in pharmaceutical manufacturing process control. During the same period, he was also a research scientist at MIT Auto-ID lab. Dr. Tsou holds a Ph.D. and M.Eng. in Information Technology from Massachusetts Institute of Technology, and a BS in Civil Engineering from National Taiwan University.
Abstract:
A longitudinal patient record stored in an Electronic Health Record (EHR) system typically consists of hundreds of clinical notes and thousands of semi-structured data entries. Together, the data and the relations among them form a semantic network representing the context of a patient’s medical problems – a rich source of information that is hard to comprehend. Recently, there have been growing interests in the natural language processing and machine learning communities to summarize, distill, and synthesize the data, in order to reduce clinicians’ cognitive load and ultimately improve healthcare.
This talk presents our efforts toward creating a “problem-oriented medical record” to assist physicians navigate today’s otherwise billing-oriented EHR.
36
Chinese Institute of Engineers – Greater 37New York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton LaGuardia East Hotel, Flushing, New York Saturday, October 22, 2016
Session V: Robotics
(4:00pm-5:30pm –Boardroom West)
Session Chair
Dr. Guangnan Ye (叶广楠) IBM Research
Session Speakers
Mr. Yin Cui (崔崟) Cornell University
Mr. Jie Feng (冯捷) Columbia University
Mr. Zheng Shou (寿政) Columbia University
Dr. Guangnan Ye (叶广楠) IBM Research
37
Chinese Institute of Engineers – Greater 38New York Chapter (http://www.cie-ny.org)
Session V: Robotics
Session Speaker:
Dr. Guangnan Ye (叶广楠) Research Staff Member Network Science and Big Data Analytics IBM T.J. Watson Research Center 1101 Kitchawan Rd. Yorktown Heights, NY, 10598 [email protected]
Biography:
Guangnan Ye is a researcher at IBM T.J. Watson Research Center. He received his PhD degree at Dept. of Electrical Engineering, Columbia University, supervised by Prof. Shih- Fu Chang . His interests fall in topics of multimedia data analytics from multi-modalities, machine learning applications on big data. Ye is an active participant at the U.S. NIST TREC video retrieval evaluation (TRECVID). He has been involved in designing a few top performance systems in multimedia event detection task in 2010, 2011, 2013.
38
Chinese Institute of Engineers – Greater 39New York Chapter (http://www.cie-ny.org)
Learning Deep Representations for Ground-to-aerial Geolocalization
Session Speaker:
Mr. Yin Cui (崔崟) Ph.D. Student Department of Computer Science Cornell University / Cornell Tech 111 8th Ave #302, New York City, NY 10011 [email protected]
Biography:
Yin Cui is a third-year Ph.D. student in Department of Computer Science at Cornell University working with Professor Serge Belongie. Before coming to Cornell, he received Bachelor's degree from Beihang University, China, advised by Professor Yu-Jin Zhang and M.S. degree from Columbia University, advised by Professor Shih-Fu Chang. He also spent some time working at NEC Labs and Baidu Research. His research interests lie in Computer Vision and Machine Learning.
Abstract:
The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial view imagery.
In this talk, I will introduce my recent work on localizing ground-level query images by matching to aerial imagery. The primary challenge for this task is that traditional computer vision methods cannot handle the wide baseline and appearance variations between these crossview pairs. Therefore, we used a deep learning based approach to learn feature representations in which matching views are near one another and unmatched views are far apart. I will show the effectiveness of our method in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations. The potential applications of our method will also be discussed.
39
Chinese Institute of Engineers – Greater 40New York Chapter (http://www.cie-ny.org)
What RGBD Object Segmentation and Retrieval Bring to Robot Perception
Session Speaker:
Mr. Jie Feng (冯捷) Ph.D Candidate Digital Video | Multimedia Lab Department of Computer Science Columbia University [email protected]
Biography:
Jie is a Ph.D student at Columbia University, specializing in machine learning and computer vision. He is a member of DVMM lab and advised by Prof.Shih-Fu Chang. Before coming to Columbia, he obtained M.S. from Peking University, China. His previous work on visual attention and object retrieval has been published in top international conferences and granted U.S. patent. His Ph.D research focuses on learning from RGBD data to solve vision problems including segmentation, 3D object retrieval and pose estimation which can drive applications like augmented reality, 3D printing and robotics. He is also a passionate software designer and developer and has worked at Microsoft, Amazon, Adobe and Google as an intern and some of his work has been incorporated in products like Microsoft Bing and Amazon Firefly.
Abstract: Object perception is fundamental for robots to interact with our physical world. Object segmentation is usually the very first task before any high-level processing, e.g. semantic reconstruction, recognition and grasping. The emergence of low-cost RGBD sensors has enabled computer vision system to capture the world in 3D and significantly improve the performance of challenging vision problems with the added depth information. In our recent work, we proposed a cue-selection framework to effectively combine multiple cues such as color, depth and normal to determine the spatial context of a particular target. With the segmented object, we dive deeper to connect a physical object captured by the sensor with a large 3D database via object retrieval. By retrieving similar 3D models, we are able to transfer knowledge from human annotation to perform difficult tasks like reconstruction, recognition and pose estimation. I will introduce a deep learning approach which takes a single depth image as input and returns a list of visually similar 3D models. An ensemble of auto-encoders is the trained on each individual object model to represent an arbitrary view and at the same time provides extensibility when a new object is added. A weakly supervised domain adaptation layer is introduced to bridge the gap between the synthesized training data and scanned test data. Experiments have shown promising results in terms of retrieval accuracy and robustness.
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Chinese Institute of Engineers – Greater 41New York Chapter (http://www.cie-ny.org)
Instance-level Action Detection for Robotic Vision: A Deep Learning Approach
Session Speaker:
Mr. Zheng Shou (寿政) Ph.D. Candidate Digital Video | Multimedia Lab Department of Electrical Engineering Columbia University [email protected]
Biography:
Zheng Shou is currently a third year Ph.D. student in Electrical Engineering Department at Columbia University working with Prof. Shih-Fu Chang. He is the recipient of the Ph.D. fellowship from the Wei Family Private Foundation for 2014-2017. He received B.Eng. degree from Wuhan University. His research interests include automatic video analysis, computer vision, and deep learning.
Abstract:
This presentation focuses on detecting action in instance-level for robotic vision based on deep learning approach. An action instance means performing an action once instead of repeatedly. In many cases, the action of interest spans over a period of time and the robot is supposed to react right after the end of the action instance rather than when the action instance is still on-going or finished. To this end, the robot should be able to detect when the action instance begins and ends. This problem can be regarded as detecting action instance category as well as its starting time and ending time in untrimmed long videos.
To address this problem, we exploit the effectiveness of deep networks for instance-level action detection via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes the learned classification network to localize each action instance. We propose a novel loss function for the localization network to explicitly consider temporal overlap and achieve high temporal localization accuracy. In the end, only the proposal network and the localization network are used during prediction. On two large-scale benchmarks, our approach achieves significantly superior performances compared with other state-of-the-art systems.
41
Chinese Institute of Engineers – Greater 42New York Chapter (http://www.cie-ny.org)
Demo: Robotics research at IBM T.J. Watson Research Center
Session Speaker:
Dr. Guangnan Ye (叶广楠) Research Staff Member Network Science and Big Data Analytics IBM T.J. Watson Research Center 1101 Kitchawan Rd. Yorktown Heights, NY, 10598 [email protected]
Biography:
Guangnan Ye is a researcher at IBM T.J. Watson Research Center. He received his PhD degree at Dept. of Electrical Engineering, Columbia University, supervised by Prof. Shih- Fu Chang . His interests fall in topics of multimedia data analytics from multi-modalities, machine learning applications on big data. Ye is an active participant at the U.S. NIST TREC video retrieval evaluation (TRECVID). He has been involved in designing a few top performance systems in multimedia event detection task in 2010, 2011, 2013.
Abstract:
In this talk, I will introduce the recent robotics research at Network Science and Big Data Analytics department in IBM T.J. Watson Research Center. Specifically, I will take humanoid robot NAO as an example. Robotics applications on object detection, pedestrian detection, vehicle detection, face detection, OCR, as well as high-level semantic event detection based on popular deep learning models will be introduced. Certain compression methodology on deep learning models which will deploy on robotics and related mobile platforms will also be included.
42
Chinese Institute of Engineers – Greater 43New York Chapter (http://www.cie-ny.org)
Chinese Institute of Engineers, USA/GNYC 2016 Annual Convention
Sheraton LaGuardia East Hotel, Flushing, New York Saturday, October 22, 2016
Session VI
Poster Presentation
(2:40pm – 5:30pm Ballroom Hallway)
Session Co-Chairs
Dr. Pai-Yu Hsueh (薛沛芸) IBM Research
Dr. Hsinyu Tsai (蔡欣妤) IBM Research
43
Chinese Institute of Engineers – Greater 44New York Chapter (http://www.cie-ny.org)
Session VI: Poster Presentation
Session Co-Chair:
Dr. Pei-Yun Sabrina Hsueh (薛沛芸) Computational Health Behavior and Decision Science Group Center for Computational Health IBM T.J. Watson Research Center 1101 Kitchawan Road, Yorktown Heights, NY 10598 [email protected]
Biography:
Dr. Pei-Yun Sabrina Hsueh is currently working as Research Staff Member in the Group of Computational Behavioral and Decision Science at IBM T.J. Watson Research Center, leading the technical and thought leadership building initiatives of Cognitive Behavioral Learning and Adaptation for consumer and pervasive health informatics. She is instrumental to the development of behavioral analytics and instrumented health framework to put patient-generated data in action. She is also serving as the co-chair of IBM Health Informatics Professional Interest Community (PIC) and the Secretary of Consumer and Pervasive Health Informatics Working Group at American Medical Informatics Association (AMIA CPHI-WG). In 2014, she was the co-lead of IBM healthcare global technology topic. Dr. Hsueh specializes in translating real-world problems into pilot designs that can be illuminated with cognitive services on the edge with the adaptive personalization need learned from mobiles, wearables, and bio-sensors. Her background and experience enables her to consult, publish and patent avidly in the areas of healthcare cognitive service design, computational linguistics, big-data and personalization analytics. She holds 20+ patent disclosures, 40+ peer-reviewed publications, and organizes a series of workshops and panels on the use of patient- controlled devices and patient-generated data in healthcare. She is a serial recipient of IBM Innovation and Manager Choice Awards and active in ACM, IEEE, EFMI and IMIA. Prior to IBM, she has served as European Google Anita Borg Scholar, worked in EU FP projects with 22 partner sites across 7 countries, and consulted in advanced technology lab and pharmaceutical sector. She received Bachelors in Computer Science at National Taiwan Univeristy, Masters in Information Management & Systems and the Ph.D. in Informatics from University of California, Berkeley and the University of Edinburgh respectively.
44
Chinese Institute of Engineers – Greater 45New York Chapter (http://www.cie-ny.org)
Session VI: Poster Presentation
Session Co-Chair
Dr. Hsinyu Tsai (蔡欣妤) Research Staff Member Exploratory Device and Integration Group TJ Watson Research Center, IBM 1101 Kitchawan Road, Yorktown, NY [email protected]
Biography:
HsinYu (Sidney) Tsai received her Ph.D. from the Electrical Engineering and Computer Science department at Massachusetts Institute of Technology in 2011, studying super- resolution optical lithography and imaging combining photo-chromic films and diffractive optics. After joining the IBM T. J Watson Research Center, Sidney focuses on next generation lithography for circuit applications using directed self-assembly (DSA) patterning and exploratory device integration. Her main research activities include sub- 30nm pitch pattern transfer with DSA, device integration for FinFET fabrication, and design technology co-optimization.
45
Chinese Institute of Engineers – Greater 46New York Chapter (http://www.cie-ny.org)
Cluster Analysis for the Location of a Crime and Stopped People Using the Reason of Stopped
Poster Presenter:
MR. Adel Ali Alkhaibari Student at Long Island University Brooklyn Camps 1 University Plaza, Brooklyn, NY 11201 [email protected]
Biography: Adel Alkhaibari received his Bachelor degree in Computer Science from King Saud university, Saudi Arabia, in 2007, and currently he is a Master’s student in Computer Science with Long Island university Brooklyn Camps, New York, where he is sponsored by a Government Scholarship from Kingdom of Saudi Arabia. Previously, he was an Instructor in Saudi Arabia for teaching Computer Science courses for five years and did data analysis projects using SPSS for Mobily company in KSA and AlOthaim company for several years. He is familiar with C#, Java, R Programming, SPSS, and Databases. His research interests are Data Mining, Big Data Analysis, Databases and Web applications. He is a member of IEEE, IEEE Computer Society, and IEEE Systems, Man, and Cybernetics (SMC) Society.
Abstract: In this Project, I used different clustering techniques for analyzing the dataset from the NYPD public website "stop-and-frisk data", I performed cluster analysis for the location of the crime and stopped people using the reason of stopped. The cluster analysis is performed using several approaches like k-means, hierarchical clustering, single link clustering,and complete link. The cluster analysis gave good result about reasons for a stop. Each method included Making Graffiti on it. In addition, I used Jaccard method when I used Hierarchical Clustering with the reason for the stop. Moreover, this report discusses how to determine the proper number of clusters for each method. I used internal and external validation measures. Internal validation measures are used to evaluate how well the results of a cluster analysis fit the data without reference to external information, an external evaluation, clustering results are evaluated based on data that were not used for clustering. In the findings and evaluation part, I got the best result that can help NYPD to improve their work. My project revealed that the best clustering algorithm is k- means algorithm. Finally, I realized that good features play an important role in ensuring that the models are helpful.
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Chinese Institute of Engineers – Greater 47New York Chapter (http://www.cie-ny.org)
A Guiding Attraction Based Random Tree for UAV Motion Planning With VINS
Poster Presenter:
Liang (Eric) Yang PH.D. Candidate of City College of New York/CUNY, State Key Laboratory of Robotics, University of Chinese Academy of Sciences Convent Ave 160, ST - 539, NYC, NY, 10031 [email protected]
Biography:
Liang Yang is a PH.D. Student of City College of New York/CUNY, and he is also a Ph.D. candidate of State Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences. He has won the ‘Best Student Paper Award’ of ICMA 2014, and ‘Best Student Paper Award Finalist’ of ICIRA 2015. He also helped to successfully apply two NSFC grants, and one State Key Lab grant as co-PI. His research interests cover Visual Odometry, Sensor Fusion, and Motion Planning.
Abstract:
The problem of three-dimensional (3D) path planning in obstacle-crowded environments is a challenge (a NP-hard problem), which is even complicated considering environmental uncertainty and system control. This poster proposed a new active exploring sampling- based algorithms based on Rapidly-exploring Random Tree (RRT), namely Guiding Attraction based Random Tree (GART). GART introduces Bi-directional Potential Field to re-distribute each newly sampled state, such that the in-collision-samples can be redistribute to extend the tree. Furthermore, GART applies forward dynamic constraints to ensure dynamic reachability as well as smoothness.
For online navigation, we introduced a visual inertial based navigation system with high frequency localization for unmanned aerial vehicle (UAV), which enable robust control. In the visual odometry system, we use a 3D ‘model’ based on iterative closet point (ICP) via using RGB-D cameras, where we use ‘bag of words’ for feature detection and matching. Thus, we can achieve a 30 HZ performance with a desktop, and 7 HZ with a compact single board computer.
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Chinese Institute of Engineers – Greater 48New York Chapter (http://www.cie-ny.org)
Cognitive and Software Defined Radio
Poster Presenter:
Mr. Pengcheng Li (李鹏程) Undergraduate Student in Electrical and Computer Engineering Department Stevens Institute of Technology 1 Castle Point on Hudson, Hoboken, NJ, 07030 [email protected]
Biography:
Pengcheng Li is an undergraduate student at Stevens Institute of Technology in Hoboken, NJ, and anticipated to obtain his Bachelor Degree of Electrical Engineering in Dec 2017. He was transferred from Northeastern University in Boston, MA in July 2014. He is a member of Institute of Electrical and Electronic Engineers (IEEE), and a member of Stevens Institute of Technology Innovation and Entrepreneurship Scholar Program.
He is studying electrical engineering in the department of Electrical and Computer Engineering (ECE), Steven Institute of Technology. From January 2016 to May 2016, he researched on an image processing project by using Raspberry Pi. From May 2016 to August 2016, he worked on the project about simulation of Cognitive and software Defined Radio. Now, he is exploring areas of Internet of Things (IoT) and wireless telecommunication.
Abstract:
With the development of society, wireless communication is considered to be really essential in many areas, like military and communication companies, and prevalently applied. However, once it has been widely used with limited channels, there must be some unavoidable signal interferences and distractions, which directly cause quality reduction of wireless communication. Therefore, cognitive radio is created for solving such problem because this kind of radio can monitor the status of different channels. By doing this, it successfully avoids using occupied channels several times, and effectively makes use of the channels unoccupied. As a result, the communication quality improves by avoiding interferences.
Pengcheng Li spent ten weeks on cognitive radio and software research to explore the properties of cognitive radio and created a simplified simulation platform by applying certain algorithms and using HTML and JavaScript. Through doing this, people can compare between theoretical result obtained from simulation and actual result obtained by doing experiment to improve data processing and analysis.
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Chinese Institute of Engineers – Greater 49New York Chapter (http://www.cie-ny.org)
ACCESS CONTROL RULE LOGIC CIRCRUIT SIMULATION(ACRLCS)
Poster Presenter:
Xinyu Xiong Undergraduate student of The city College of New York 160 Convent Avenue New York, NY 10031 [email protected]
Biography:
Xinyu Xiong is pursuing his bachelor degree at the city college of New York. He is a junior computer science student. In the first two years of xinyu's college time, he not only did well in study, but also had been actively seeking for internship and working opportunity. He had worked in the CEE web solution as web graphic designer and optimizer, and was a former math tutor of the BMCC. In this summer time, he took an internship at NIST. Now he is working as a math and CS tutor at the city college of New York.
Abstract:
Access control (AC) policies can be implemented based on different AC models, which are fundamentally composed by semantically independent AC rules in expressions of privilege assignments described by attributes of subjects, actions, objects, and environment variables of the protected system. Incorrect implementations of AC policies result in faults that not only leak but also disable access of information, and faults in AC policies are difficult to detect without support of verification or automatic fault detection mechanisms.
Most research on AC model or policy verification techniques are focused on one particular model, and almost all of the research is in applied methods, which require the completed AC policies as the input for verification or test processes to generate fault reports. Those methods provide no information about the source of rule faults that might allow conflicts in privilege assignment, leakage of privileges, or conflict of interest permissions.
To address the issue, AC Rule Logic Circuit Simulation (ACRLCS) project provides an automatic method through the construction of a simulated logic circuit that simulates AC rules in AC policies to allow real-time detection of policy faults including conflicts of privilege assignments, leaks of information, and conflicts of interest assignments. For this project, I developed new critical functions (algorithms) that replace the original implementation that does not allow inheritance capabilities nor reloading previously saved work required to be continued.
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Chinese Institute of Engineers – Greater 50New York Chapter (http://www.cie-ny.org)
3D Assistive Indoor Localization with a Google Glass
Poster Presenter: Feng Hu PhD candidate, The Graduate Center, CUNY Email and homepage: [email protected] (ccvcl.org/~fhu) Additional authors: Kenichi Yamamoto, CUNY [email protected] Zhigang Zhu, CUNY [email protected] Advisor: Prof. Zhigang Zhu, [email protected]
Biography: Feng Hu is a Computer Science PhD candidate (expected graduation Feb. 2017) in The Graduate Center, CUNY. He is supervised by the Herbert G. Kayser Professor Dr. Zhigang Zhu. His research interests include computer vision, 2D/3D based indoor localization, AR/VR, self-driving vehicles and machine learning. He obtained his Bachelor degree majoring in Automation (Automatic Control) from Xi’an Jiaotong University in 2009. He obtained his Master degree majoring in Pattern Recognition and Intelligent Systems from University of Chinese Academy of Sciences in 2012. Since 2012, he has been a research assistant in Prof. Zhigang Zhu’s Visual Computing Lab in the City College of New York. His thesis research project is vision-based indoor localization for the visually impaired using 2D images (iPhone + GoPano lens; Google Glass) and 3D depth information (Google Project Tango tablet) under the support of National Science Foundation “Emerging Frontiers in Research and Innovation" program (NSF EFRI #1137172). He worked/interned in NLPR, SKL-MCCS and IBM. He is an IEEE member and reviewer of CVIU, JVCIR, ACVR, ICTAI, ACM MM, etc.
Abstract: An accurate, real-time, and robust indoor localization system with a natural interface is important for visually impaired people to live a convenient social life. This project aims to build a Google Glass application which can in real-time determine the device’s camera (i.e. user's) position and orientation within indoor environments, such as in campus buildings or rooms at home. Pre-captured 2D images are utilized to reconstruct the indoor environment 3D model using SfM technique in the server end. Newly captured Glass image features are matched against feature database (aligned with 3D model) using FLANN, and pose is estimated with pNp and RANSAC algorithm. Multi-images aggregation strategy is applied for refined localization. Hands-free interface-–voice input and text-to-speech feedback are designed for enhanced user experience. Tests on real environment are provided to demonstrate the accuracy and robustness of our solution.
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Chinese Institute of Engineers – Greater 51New York Chapter (http://www.cie-ny.org)
EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
Poster Presenter: Wei Li CUNY City College, Email: [email protected] Additional authors: Farnaz Abtahi, CUNY Graduate Center Lijun Yin, SUNY Bingham Advisor: Zhigang Zhu, CUNY City College & Graduate Center
Email: [email protected] Biography: Wei Li is a Ph.D student at CUNY City College. He is advised by Prof. Zhigang Zhu. His research topic includes computer vision, deep learning, human computer interaction and assistive technology for visually impaired people. Wei Li obtained his Bachelor and Master degree from Beijing University of Aeronautic and Astronautic. He majored in Instrument Science in Bachelor study and worked on 3D image processing for his Master degree. He also won the Chinese National Award for Graduate Students in 2013 and graduated with honor. After graduation from Beijing University of Aeronautic and Astronautic, Wei joined Visual Computing Lab in the fall of 2013 and advised by Prof. Zhigang Zhu. Wei has wide interests in research area including deep learning, face recognition, emotion detection, and human computer interaction. Wei now focused on combining deep learning and video emotion detection to find a better way to recognize people’s emotion from real-time video. Wei is also good at applying research results to practical scenario. He developed a real- time emotion detection system which can tell the expression of people in high accuracy to help visually impaired people. He also extended the application and developed a facial emotion based game in both webpage and smart phone versions. Wei participated in a lot of academic activity. He worked as an intern at IBM China research Lab in summer 2014 and worked at SRI International as a research assistant. He also acted as a reviewer for ACVR2014, ACVR 2015, WACV2014 and WACV 2015.
Abstract: Facial Action Unit (AU) detection is an essential process in facial analysis. With a robust AU detector, facial expression and facial action problems can be solved more effectively. AU detection is the process to find some basic facial actions defined by FACS, the Facial Action Coding System. The AUs are elements for more complicated facial actions. For instance, sadness might be the combination of AU1 (inner brow raiser), AU4 (brow lower), and AU15 (lip corner depressor). Most of current AU detection approaches either need the processed faces with frontal views or the texture features are artificially designed, making the features not well learned. To tackle these problems, we proposed the EAC (enhancing and cropping) Net to a convolutional neural network (CNN) to detect facial AUs automatically. We first build the enhancing net (E-Net), which is constructed by adding attention layers to a pretrained VGG net, one of the very effective CNNs. The E-net yields a significant improvement in average F1 score and accuracy on BP4D dataset compared to the state of the art approaches. We then add cropping layers on top of the E-net and design the EAC Net. The cropping layers are implemented by cropping AU areas of interests from high-level convolutional feature maps. The EAC net yields up to 7.6\% increase in average F1 score and 19.2\% improvement in accuracy compared to the state of the art approaches applying to BP4D dataset. As a summary, this paper has the following contributions: 1) A Candid Images Facial Expression dataset, CIFE, is created from online images. 2) A convolutional neural network based approach was proposed for facial expression recognition, which has been shown to be effective in recognizing facial expression of candid images.
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Chinese Institute of Engineers – Greater 52New York Chapter (http://www.cie-ny.org)
The Picat Language and its Application to Games and AI Problems Poster Presenter:
Jie Mei Research Assistant at Research Foundation CUNY Computer and Information Science Department City University of New York – Brooklyn College 1732 Gerritsen Ave, Brooklyn, NY 11229 [email protected]
Biography:
Mr. Mei earned his Bachelor’s degree in Mathematics and Applied Mathematics from Shanghai Jiao Tong University Mathematics Department in Shanghai, China. He has received Master’s degree in Actuarial Mathematics at the University of Michigan. He is a member of The Honor Society of Phi Kappa Phi at the University of Michigan.
Currently, he is a first-year graduate studying Computer Science at CUNY Brooklyn College. He works as a research assistant for Professor Zhou at the Research Foundation CUNY. He uses Picat to develop applications to solve constraint satisfaction problems and linear programming.
Abstract:
This presentation focuses on the “The Picat Language and its Application to Games and AI Problems” and discusses Picat, Constraint Satisfaction Problem, Planning, Artificial Intelligence, SAT Solver and Modules. Mr. Mei would like to talk about the introduction of Picat, many games and AI problems that can be solved easily by Picat, the efficiency of the SAT solver and its advantages over other programming languages such as Python and Prolog.
Logic programming languages entered the scene of computer science in the early 1970s as the answer to the need for paradigms capable of representing and reasoning about different kinds of knowledge. Picat is a new logic-based multi-paradigm programming language that integrates logic programming, functional programming, dynamic programming with tabling and scripting. Picat provides facilities for solving combinatorial search problems, including solver modules that are based on CP, SAT, and MIP and a module for planning that is implemented with the use of tabling. As the computer technology advances, the programming language that has its strength in dealing with games and artificial intelligence should be shared with the public.
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Chinese Institute of Engineers – Greater 53New York Chapter (http://www.cie-ny.org)
Modeling of Wiresaw System with a Moving Boundary Condition
Corresponding Author: Poster Presenter: Imin Kao Liming Li Professor of Mechanical Engineering, Ph.D. Candidate [email protected] [email protected] Manufacturing Automation Laboratory, Stony Brook University, NY, 11794-2300, USA
Biography of Presenter: Liming Li is a Ph.D. Candidate of mechanical engineering at Stony Brook University. His research is in the areas of wiresaw, vibration and fault diagnosis in rotating machine. He aims at developing the correlation between wafer quality and wiresawing conditions. He obtained his Master degree at Stony Brook University and Bachelor degree at Zhengzhou University.
Abstract: Wiresaw is a major technology in the manufacturing of wafers for semiconductor and photovoltaics applications. It is used to slice wafer from silicon ingots for integrated circuit and solar cell fabrication. It has the advantage of low kerf loss and high throughput with better surface finish. Wiresaw can slice an ingot to produce hundreds pieces of wafer simultaneously, and it is a green and high-efficient technique in wafer manufacturing. Due to the demand for thinner wafer and higher surface quality of large-sized wafers, the vibration analysis of wiresaw is an indispensable research in its slicing processing.
In the slurry wiresawing, the longitudinal moving wire drives the abrasive, which is carried by the slurry, into the slicing region. The tension force and bow angle of wire produce sufficient stiffness to the wire so that it is able to transfer the feeding force to the slurry. The abrasives in the slurry cut ingot through the rolling-indenting mechanism. Thus, the wire immersed in the slurry can dominate the slicing process. Current industrial application shows the rocking motion in wiresaw can improve the throughput of wafers. This research work simulates the rocking movement as a moving boundary to derive the analytical solution of the moving wire with a moving boundary condition for a damped traveling wire system. Based on the solution, the moving boundary condition determines the vibration frequency of wiresaw. It has a great impact on the vibration pattern of wire with axial wire speed and damping factor, while the vibration pattern can dominate the topography and thickness of wafer. In the simulation, nodal points are found in the wiresaw system without damping. The distribution of those nodal points is related to the axial wire speed and the frequency of moving boundary. The damping can change those node points, and make the vibration of wire cooperate the movement at neighbor spans. This may facilitate the transportation of abrasives through the cutting region, which in turn affects the efficiency of slicing in the wiresawing process.
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Chinese Institute of Engineers – Greater New York Chapter (http://www.cie-ny.org)
Estimation of Adaptive Interpretable Policy for Fitness App Users
Poster Presenter:
Xinyu Hu Department of Biostatistics Columbia University 722W 168th St, New York, NY10032 [email protected]
Other authors: Pei-Yun Hsueh, IBM T.J. Watson Research Center, [email protected] Ching-Hua Chen, IBM T.J. Watson Research Center, [email protected] Ying-Kuen Cheung, Columbia University, [email protected] Min Qian, Columbia University, [email protected]
Biography:
Xinyu Hu is a third year Ph.D. candidate in Biostatistics Department of Columbia University, supervised by Professor Ying-Kuen Cheung. Her research interests focus on using machine learning techniques in clinical research.
Abstract:
In this poster, we introduce a novel method to estimate a sequence of decision rules, which map up-to-date individual information to adaptively recommended actions for n-stage observational data. The proposed method is a first step leading to progressive coaching in the field of behavior intervention for fitness app users. In the past, reinforcement learning (RL) has been widely applied to quantify sequential human decision-making procedures. However, the black-box nature of RL algorithms made it difficult to apply it in scenarios where recommended actions need to be explained. In our work, we use the combination of statistical method and reinforcement learning techniques, i.e. Q learning, to derive an interpretable form of estimated policy. In addition, leveled thresholds are identified to help understand the difference of goal achievements in sub-population. Since the estimated policy is in an interpretable form and action is recommended based on the goal achievements at previous stages, it can be further incorporated into explanations with information relevant to users’ contexts. This method is evaluated based on simulation study and applied on observational data of fitness app users to understand how coaching advice might be different from one to another.
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Chinese Institute of Engineers – Greater New York Chapter (http://www.cie-ny.org)
Increasing Accuracy of Bone Tumor Resection using Customized 3D Printed Guides
Poster Presenter: Mr. Vamiq M Mustahsan Ph.D. Student Stony Brook University Stony Brook, NY 11790 [email protected]
Biography:
Vamiq M Mustahsan has earned his Master of Science in Mechanical Engineering from Stony Brook University and is now pursuing PhD in Mechanical Engineering at Stony Brook University. He has previously worked in Confederation of Indian Industries, Sorabji-Godrej Green Business Center, Hyderabad, India as an efficiency and performance auditor. He is a student member of American Society of Mechanical Engineers and Society of Automotive Engineering.
He is presently working as a graduate research assistant in Manufacturing and Automation Lab at Stony Brook University with Dr. Imin Kao.
Abstract:
Surgical treatment of Osteosarcoma or bone tumors has been highly imprecise and inaccurate as the techniques available today inhibit the surgeon in accurately follow the pre-operative plan. The research in customizing the procedure of surgical resection is expanding as new techniques are being developed. One such technique is to use customized 3D printed cutting guides for wide resection of bone tumors. This technique has been shown to be much more accurate compared to free hand resection (Khan, Lipman Jd Fau - Pearle et al. , Helguero, Kao et al. 2015). This study investigates how much, the errors in positioning of the cutting guide caused due to soft tissue present on the partially skeletonized bone effect the accuracy of resection of the bone tumor. By comparing these two deviations from the ideal positioning, we formulate a factor of safety which can be incorporated during the design of cutting path pre-operatively. Many specialized features were also added in the design of the cutting guide to help minimize the positioning error and the accuracy of resection with each of these specialized feature was also compared. The objectives of this study is to save as much healthy bone in resection as possible in guided resection by defining design factors in the determination of the pre-operative plan. This would eventually increase the safety and rate of success of the surgical resection.
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Chinese Institute of Engineers – Greater New York Chapter (http://www.cie-ny.org)
Link Community Discovery in Multi-Layer Networks
Poster Speaker:
Mr. Liu Weiyi (刘唯⼀) Ph.D. Candidate University of Electronic Science and Technology of China, IBM Thomas J. Watson Research Center Watson Research Center, Yorktown Heights, NY 10598. [email protected]
Biography: Mr. Liu Weiyi comes from Sichuan Province, China. He is a Ph.D. Candidate in the University of Electronic Science and Technology of China (UESTC), this year is his fourth year. Now, he has joined Ching-Yung Lin’s team in the IBM Watson Research center as a visiting scholar for two years. He is a MD-PhD in the school of Communication & Information Engineering, UESTC; prior he is a member in Professor Hu Guangmin’s group. Since 2013, he has focused his research interesting area in Social Network Analysis. Abstract: This presentation focuses on the “overlapping community discovery in multi-layer networks”. Mr. Liu would like to present a new community discovery algorithm based on the idea of link community, as he believes doing so will provide a steady, high- efficient solution to uncover communities via multiple networks. In generally speaking, multiple types of relationships (links) among a same set of nodes can be depicted by the multiplex or multilayer networks, where each network represents a relationship among nodes. In addition, these relationships are not independent from one to another, for example, a network of friendship may have the ability to affect a network of phone calls, vice and versa. Hence, if we merge all networks into a single aggregated network, it will surely lose the unique topology information in each layer, and ignore the important interplays among networks. In here, we present a novel community discovery method for multi-layer networks. As the existence of links among two nodes in multi-layer networks is no longer a binary problem, first of all, we separate the links for a node pair into same-layer case and cross- layer case, the former requires that the link for two nodes must exist in a same layer, the latter specifies that the existence of a link for the node-pair in networks must rely on more than one network; secondary, we use Jaccard similarity to measure both cases, and construct the hierarchy structure of the multi-layer network; at last, we introduce the density of communities in multi-layer networks, and by finding the maximum density in the hierarchy structure, we can discover overlapping communities in multi-layer networks. The performance in both synthetic datasets and real world datasets reveals that our algorithms can discover communities in networks more accurately and efficiently, and this method can also reveal the inner structure for the multi-layer networks.
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Chinese Institute of Engineers – Greater New York Chapter (http://www.cie-ny.org)
Visualizing Research Impact Through Citation Ego-network
Yong Wang (王勇) Ph.D Candidate in CSE Department The Hong Kong University of Science and Technology (HKUST)
Research Intern in System G Group IBM T. J Watson Research Center Room 21-147, 1101 Kitchawan Rd, Yorktown Heights, NY 10598 [email protected], [email protected]
Biography:
Mr. Wang is a Ph.D candidate in the department of computer science and technology, the Hong Kong University of Science and Technology. He is currently a research intern in IBM T. J Watson Research Center, working with Dr. Ching-Yung Lin and Dr. Conglei Shi. He received his bachelor and master degree from Harbin Institute of Technology in 2011 and Huazhong University of Science and Technology in 2014, respectively.
His major research interest includes information visualization, visual analytics and image processing. He believes that a picture is worth a thousand words and is passionate about helping the exploration of big data through transforming big data to novel and intuitive visual designs. Recently, he is mainly focusing on graph visualization and its related analysis.
Abstract:
Research impact plays a critical role in evaluating the research quality and influence of a scholar, a journal, or a conference. Many researchers have attempted to quantify research impact by introducing different types of metrics based on the ego-network of citation, such as h-index, citation count, and impact factor. These metrics are widely used in academic community. However, quantitative metrics are highly aggregated in most cases and sometimes biased, probably resulting in the loss of impact details such as research area, influenced researcher, temporal trend of impact, possible contributors of impact. More detailed exploration for the dynamic ego-network of citation is needed. Previous work on visualizing ego-network of citation usually only shows limited aspects of research impact and may suffer from other problems including visual clutter and scalability issues. To fill this gap, we propose an interactive visualization tool ImpactVis for better exploration of research impact through ego-network of citation. Case studies and in-depth expert interviews are conducted to demonstrate the effectiveness of ImpactVis.
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Chinese Institute of Engineers – Greater New York Chapter (http://www.cie-ny.org)
CIE Centennial Celebration
HNA Palisades Premier Conference Center Saturday, October 14, 2017
The Chinese Institute of Engineers (CIE) was founded in 1917 by a group of Chinese engineers who had studied in the United States. Our mission is to promote the communication among Chinese engineers and the advancement of science, technology, engineering, and mathematics (STEM). Over the past century, CIE has held or co- sponsored many important events, such as the Asian American Engineer of the Year (AAEOY) Award, Modern Engineering and Technology Seminar (METS), and Sino- American Technology and Engineering Conference (SATEC). On October 14, 2017, we will celebrate our Centennial at the picturesque HNA Palisades Premier Conference Center in Rockland County, New York. If you would like to sponsor or participate in this once-in-a-lifetime event, please contact the CIE Centennial Planning Committee ([email protected]).
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