DOCSLIB.ORG

Kathleen Mary Carley

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Kathleen Mary Carley Institute for Software Research1 tel: (412) 268-6016 Carnegie Mellon University fax: (412) 268-1744 Pittsburgh, PA 15213 email:[email protected] http://www.casos.cs.cmu.edu/bios/carley/carley.html EDUCATION 4/2019 H.D. University of Zurich. Business, Economics and Informatics 9/1978-6/1984 Ph.D. Harvard University. Sociology Thesis: Consensus Construction 9/1974-6/1978 S.B. Massachusetts Institute of Technology. Political Science 9/1974-6/1978 S.B. Massachusetts Institute of Technology. Economics PROFESSIONAL EXPERIENCE REGULAR APPOINTMENTS 8/2002- Professor of Computation, Organization and Society; Appointment in Institute for Software Research International, SCS; Courtesy appointments in SDS, Heinz, GSIA and EPP; Carnegie Mellon University. Pittsburgh, PA 15213 9/1998- 7/2002 Professor of Sociology, Organizations and IT; Appointments in SDS, Heinz, GSIA and EPP; Carnegie Mellon University. Pittsburgh, PA 15213 9/1990-8/1998 Associate Professor of Sociology and Organizations; Carnegie Mellon University. Pittsburgh, PA 15213 9/1984-8/1990 Assistant Professor of Sociology and Information Systems; Carnegie Mellon University. Pittsburgh, PA 15213 OTHER APPOINTMENTS AND POSITIONS 7/2020- Scientific Advisory Board of IP Paris-HEC on AI, Business and Society 7/2020-11/2020 Scientific Review Board ETH Zurich Switzerland 7/2019- Center Director, Center for Informed Democracy and Social cybersecurity (IDeaS) 7/2018- Co-director Social Cybersecurity Working Group 9/1998- Center Director, Center for Computational Analysis of Social and Organizational Systems. http://www.casos.cs.cmu.edu/ 2010-2011 Scientific Advisory Board, Aptima 8/1999-8/2000 Institute for Complex Engineered Systems (ICES) – CASOS Lab Director 3/1999-8/1999 Institute for Complex Engineered Systems (ICES) – CASOS Thrust Leader 1/1997 Invited Professor, Universite' Leonardo DaVinci 6/1992-9/1992 Research Faculty, Learning Research and Development Center (LRDC), U. of Pittsburgh. Pittsburgh, PA. 6/1991-9/1991 Research Faculty, LRDC, U. of Pittsburgh. Pittsburgh, PA. 6/1990-9/1990 Research Faculty, LRDC, U. of Pittsburgh. Pittsburgh, PA. 6/1984-8/1984 RA for Prof. Bob Eccles, Harvard Business School. Cambridge, MA. 12/1982-8/1984 Computer and Statistical Consultant, Harvard University. Cambridge, MA. 1/1982-6/1982 Project Director Cambridge-Sommerville Sociology Internship Program, Harvard University. Sociology. Cambridge, MA. 9/1979-1/1982 TA for Sociology 156: (Intro. Statistics), Harvard University. Cambridge, MA. 6/1980-10/1984 Software Development and System Management for Prof. James Davis, Harvard University. Sociology. Cambridge, MA. 2/1979-5/1981 Instructor in Applied Mathematics, Lowell Institute. Cambridge, MA. 6/1978-8/1978 Research Assistant Prof. Hayward Alker, MIT: Political Science. Cambridge, MA. 2/1977-5/1978 Research Assistant Bloomfield and Alker, MIT: Political Science. Cambridge, MA. 6/1975-10/1976 Research Assistant Prof. Bloomfield, MIT: Political Science. Cambridge, MA. 1 Toward the end of 2007 the Institute for Software Research International, a department in the School of Computer Science at Carnegie Mellon University changed its name to Institute for Software Research. K.M. Carley February 8, 2021 CONSULTING 2020- Accenture Federal 2018-2020 Wright University 2016-2020 Drexel University 2014 Blue Shield 2011 Centra 2009-2019 Columbia University 2005-2006 Global Info Tek, Inc. 2005-2006 New York School of Medicine 2005 DyNaTech 2004-2007 HumRo 2003-2004 ALPHATECH (now BAE) 2003 Sandia 2002-2003 Booze Allen Hamilton 2002-2004 Nursing School, ASU 1998-2005,2009-10 Aptima 3/2000 Merrill Lynch 10/1998-12/1998 Kaufmann Foundation 3-7/1997,2003-06 Charles River Analytics 6/1995,5/1998 Mellon Bank 5-7/1994,10-12/1994 American Red Cross — Initial Response Crisis Management Training Seminar 5/1989-2/1990 Center for Strategic Decision Research — Org. Structure for R&D Funding 4/1987-10/1987 Thomson, Rhodes and Cowie, Pittsburgh, PA — IS 6/1982-8/1984 School for English as a Foreign Language, Harvard University — IS 6/1983 Metametrics Corporation, Carlisle, MA — Statistical Consultant 12/1981-8/1984 Harvard University — Programmer/Consultant INTERESTS Kathleen M. Carley's research involves applying computational social science, cognitive science, organization science, dynamic network analysis, social network analysis, machine learning, data analytics and text analytics to complex social and organizational problems such as social cybersecurity, disinformation, disease contagion, disaster response, and terrorism. She develops social and dynamic network analytical and visual techniques, agent-based models, network based text mining and machine learning tools, all predicated on blending socio-cognitive theory with advanced computation. She and members of her center have developed novel tools and technologies for analyzing large-scale geo-centric dynamic-networks and various multi-agent simulation systems. These tools include: ORA, a statistical and graphical toolkit for analyzing and visualizing multi-dimensional networks, dynamic-networks, geo- spatial networks with special features for social media analytics; NetMapper and AutoMap are text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks, as well as sentiment. Her simulation models meld agent- based technology with network dynamics and empirical data. For example, BioWar is a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills and weaponized biological attacks; and Construct is an agent-based dynamic-network model for assessing network evolution and the diffusion of information and beliefs under diverse socio-demographic and media environments. Dr. Carley is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS) and the center for Informed Democracy and Social-cybersecurity. She is the founding co-editor of the journal Computational Organization Theory and has co-edited several books in the computational organizations and dynamic network area. HONORS AND PROFESSIONAL RECOGNITION 2019-2020 Conference Chair, SBP-BRiMS 2020 2019 Honorary Doctorate, “Doktor ehrenhalber" Faculty of Business, Economics and Informatics at University of Zurich, Zurich Switzerland 2019 Computational Social Science – Quo Vadis? An Interdisciplinary Symposium Honoring Kathleen M. Carley, University of Zurich, Zurich Switzerland 2019 Global Development Lab Scientific Advisory Panel ~ 2 ~ K.M. Carley February 8, 2021 2019 Iain Cruickshank and Kathleen M. Carley, 2019, Detecting malware communities using socio- cultural cognitive mapping” SBP-BRiMS conference paper. Best Late breaking paper. 2018 Listed in 50+ Women to follow in computational social science – Sage Ocean, https://ocean.sagepub.com/blog/2018/10/8/womentofollow-in-computational-social-science 2018 Listed in 39 Women doing amazing research in computational social science – Sage Ocean, available at https://ocean.sagepub.com/blog/2018/9/28/39-women-doing-amazing-research-in- computational-social-science 2018-2019 Conference Chair, SBP-BRiMS 2019 2018 David M. Beskow and Kathleen M. Carley, 2018, “Using Random String Classification to Filter and Annotate Automated Accounts,” In Proceedings of the International Conference SBP-BRiMS 2018, Halil Bisgin, Ayaz Hyder, Chris Dancy, and Robert Thomson (Eds.) July 10-13, 2018 Washington DC, Springer. Best Student Paper 2018 Member of the NAS/NRC ARO Review Committee 4/2018 United States Geospatial Intelligence Foundation Academic Award, GEOINT 2018 10/27/2017 NSC working group 10/25/2017 Briefing to the NSC - Present and Future Influences of Artificial Intelligence and Cyber Warfare 2017-2018 Conference Chair, SBP-BRiMS 2018 2017-2019 Member of the NAS/NRC Committee on Decadal Survey of the Social Sciences Present and Future Influences of Artificial Intelligence and Cyber Warfare 2016-2017 Conference Chair, SBP-BRiMS 2017 2015-2016 Conference Chair, SBP-BRiMS 2016 2015-2016 Member of the NAS/NRC Committee on Models of the World for the National Geospatial- Intelligence Agency 2014 Allen Newell Award for Research Excellence - “For the creation of empirical methods to rigorously establish the impact of human communication on software quality.” 2014-2018 Member of the NAS/NRC ARL Review Panel 2013 IEEE Fellow 2012-2013 Member of the NAS/NRC Committee on Digital Math Library 2012-2014 DHS Homeland Security Science and Technology Advisory Committee, HSSTAC, SGE 2012 Kenneth Joseph, Chun How Tan and Kathleen M. Carley, 2012, “Beyond 'Local', 'Categories' and 'Friends': Clustering foursquare Users Using Latent 'Topics'” 4th International Workshop on Location-Based Social Networks (LBSN 2012) at UBICOM Sept 8, 2012 - Pittsburgh, PA. Best Paper Award 2011 Senior Member IEEE 2011 Kathleen M. Carley, Simmel Award, International Network for Social Network Analysis, February 2011, St. Petersburg, FL. 2011 Geoffrey P. Morgan and Kathleen M. Carley, "Exploring the impact of a stochastic hiring function in dynamic organizations," In proceedings of the Behavioral Representation in Modeling and Simulation (BRIMS) Conference, Sundance, UT, March 23, 2011, Pp. 106-113. Best Student Paper award. 2010-2013 Member of the NAS/NRC Committee on NGA Workforce Assessment 2010 Patrick Wagstrom, James D. Herbsleb and Kathleen M. Carley, “Communication, Team Performance
Recommended publications
  • Computational Modeling of Complex Socio-Technical Systems

    Computational Modeling of Complex Socio-Technical Systems

    Computational Modeling of Complex Socio‐Technical Systems 08‐810, 08‐621 Syllabus Fall 2016 12 Course Units Prof. Kathleen M. Carley E‐Mail: [email protected] Phone: x8‐6016 Office: Wean 5130 Office Hours: by appointment T.A.: Geoffrey Morgan Lectures: Monday, Wednesday 3:30 PM – 5:20 PM **There will be no class scheduled for Labor Day September 5th **There will be no classes scheduled on Wednesday November 23rd for the Thanksgiving Holiday **Recitation sections will be organized as needed for specific technologies – construct, system dynamics, and abms – times TBA and this area optional All course information is available on‐line via CMU Blackboard: http://www.cmu.edu/blackboard For a taste of what the course can be like: A fun little explainer and tutorial on Schelling's Dynamic Segregation Model, with a tie in for the need for awareness about diversity. http://ncase.me/polygons/ DESCRIPTION: We live and work in complex adaptive and evolving socio‐technical systems. These systems may be complex for a variety of reasons. For example, they may be complex because there is a need to coordinate many groups, because humans are interacting with technology, because there are non‐ routine or very knowledge intensive tasks. At the heart of this complexity is a set of adaptive agents who are connected or linked to other agents forming a network and who are constrained or enabled by the world they inhabit. Computational modeling can be used to help analyze, reason about, predict the behavior of, and possibly control such complex systems of "networked" agents.
  • Dynamic Social Network Analysis: Present Roots and Future Fruits

    Dynamic Social Network Analysis: Present Roots and Future Fruits

    Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S. Dornisch, Jeffrey Johnson, Mark Mizruchi, Elizabeth Warner July 2009 DEFENSE THREAT REDUCTION AGENCY •ADVANCED SYSTEMS AND CONCEPTS OFFICE REPORT NUMBER ASCO 2009 009 The mission of the Defense Threat Reduction Agency (DTRA) is to safeguard America and its allies from weapons of mass destruction (chemical, biological, radiological, nuclear, and high explosives) by providing capabilities to reduce, eliminate, and counter the threat, and mitigate its effects. The Advanced Systems and Concepts Office (ASCO) supports this mission by providing long-term rolling horizon perspectives to help DTRA leadership identify, plan, and persuasively communicate what is needed in the near term to achieve the longer-term goals inherent in the agency’s mission. ASCO also emphasizes the identification, integration, and further development of leading strategic thinking and analysis on the most intractable problems related to combating weapons of mass destruction. For further information on this project, or on ASCO’s broader research program, please contact: Defense Threat Reduction Agency Advanced Systems and Concepts Office 8725 John J. Kingman Road Ft. Belvoir, VA 22060-6201 [email protected] Or, visit our website: http://www.dtra.mil/asco/ascoweb/index.htm Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K. Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office and Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S.
  • Learning Within and Among Organizations

    Learning Within and Among Organizations

    LEARNING WITHIN AND AMONG ORGANIZATIONS Kathleen M. Carley Social and Decision Sciences and H. J. Heinz III School of Policy and Management Carnegie Mellon University ACKNOWLEDGMENT This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037 and by the National Science Foundation under Grant No. IRI9633 662. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation, or the U.S. government. Reference: Kathleen M. Carley, 1999, “Learning Within and Among Organizations.” Ch. 1 in Philip Anderson, Joel Baum and Anne Miner (Eds.) Advances in Strategic Management: Population-Level Learning and Industry Change, Vol. 16. Elsevier Science Ltd. Pp. 33-56. - 1 - ABSTRACT Change is readily seen both within organizations and within populations of organizations. Such change has been characterized as organization or population level learning or evolution. Underlying such change, is change at the individual human and social network level. Herein, it is asked, how does the way in which individuals learn and the way in which networks evolve reflect itself in organizational and population level learning. What changes should emerge at the organization and population level due to learning, information diffusion, and network change at the individual level? Herein it is argued that organization and population level phenomena, such as performance improvements, mis-learning, and shakeouts emerge from the on-going processes of change at the individual level. Looking at learning and information diffusion enables the organizational theorist to link micro and macro level organizational phenomena.
  • Intra-Organizational Complexity and Computation

    Intra-Organizational Complexity and Computation

    Intra-organizational Complexity and Computation Kathleen M. Carley Social and Decision Sciences H. J. Heinz III School of Policy and Management Engineering and Public Policy Carnegie Mellon University December 2000 Running Head: Intra-organizational Complexity and Computation Direct all correspondence to: Prof. Kathleen M. Carley Dept. of Social and Decision Sciences Carnegie Mellon University Pittsburgh, PA 15213 Email: [email protected] Tel: 1-412-268-3225 Fax: 1-412-268-6938 This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037 and by the National Science Foundation under Grant No. IRI9633 662. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation, or the U.S. government. The author thanks the following people for their comments on this and related works: Carter Butts, Ju-Sung Lee, Bill McKelvey, Benoit Morel and Ranga Ramanujam. To be published in Companion to Organizations, Joel C. Baum (Ed.), Oxford UK: Blackwell, July 2001. Intra-organizational Complexity and Computation Organizations are complex systems. They are also information processing systems comprised of a large number of agents such as human beings. Combining these perspectives and recognizing the essential non-linear dynamics that are at work leads to the standard non-linear multi-agent system conclusions such as: history matters, organizational behavior and form is path dependent, complex behavior emerges from individual interaction, and change is inevitable.
  • Dynamic Network Analysis (Spring 2020) Course 17-801, 17‐685, 19‐640 Instructor Prof

    Dynamic Network Analysis (Spring 2020) Course 17-801, 17‐685, 19‐640 Instructor Prof

    Dynamic Network Analysis (Spring 2020) Course 17-801, 17-685, 19-640 Instructor Prof. Kathleen M Carley Phone: x86016 Office: Wean 5130, ISR E‐Mail: [email protected] Office Hours: by appointment; contact Sienna Watkins for scheduling [email protected] Teaching Assistant Mihovil Bartulovic, [email protected], Wean 4211 Introduction Who knows who? Who knows what? Who communicates with whom? Who is influential? How do ideas, diseases, and technologies propagate through groups? How do social media, social, knowledge, and technology networks differ? How do these networks evolve? How do network constrain and enable behavior? How can a network be compromised or made resilient? What are network cascades? Such questions can be addressed using Network Science. Network Science, a.k.a. social network analysis, link analysis, geo‐network analysis, and dynamic network analysis is a fast‐growing interdisciplinary field aimed at understanding simple & high dimensional networks, from both a static and a dynamic perspective. Across an unlimited application space, graph theoretic, statistical, & simulation methodologies are used to reason about complex systems as networks. An interdisciplinary perspective on network science is provided, with an emphasis on high‐dimensional dynamic data. The fundamentals of network science, methods, theories, metrics & confidence estimation, constraints on data collection & bias, and key research findings & challenges are examined. Illustrative networks discussed include social media based (e.g., twitter), disaster response, organizational, semantic, political elite, crises, terror, & P2P networks. Critical procedures covered include: basic centralities and metrics, group and community detection, link inference, network change detection, comparative analytics, and big data techniques. Applications from business, science, art, medicine, forensics, social media & numerous other areas are explored.
  • On Generating Hypotheses Using Computer Simulations Kathleen M

    On Generating Hypotheses Using Computer Simulations Kathleen M

    On Generating Hypotheses using Computer Simulations Kathleen M. Carley Social and Decision Sciences and H.J. Heinz III School of Public Policy and Management Carnegie Mellon University Tel: 1-412-268-3225 Fax: 1-412-268-6938 Email: [email protected] url: http://sds.hss.cmu.edu/faculty/carley/carley.htm Keywords: simulation, computer modeling, hypotheses, organizations, dynamic systems Grant Sponsors: ONR, NSF Contract grant numbers: ONR N00014-97-1-0037 NSF IRI9633 662 Citation: Kathleen M. Carley, 1999, “On Generating Hypotheses Using Computer Simulations. “ Systems Engineering, 2(2): 69-77. (short version in CCRTS Proceedings) Abstract Computational modeling is increasingly being used to do theory development. Computer simulation, numerical estimation, and emulation models are used to describe complex systems and generate a series of hypotheses about the behavior of these systems under different scenarios. These hypotheses can serve as guides to the design of human laboratory experiments and can suggest what data to collect in field and gaming situations. Models that are particularly useful in this venue are process models of non-linear systems where multiple factors dynamically interact. This chapter describes these models and illustrates how they can be used to generate a series of hypotheses that guide future endeavors. 1 On Generating Hypotheses using Computer Simulations Kathleen M. Carley 1 Introduction The use of formal techniques in general, and computational analysis in particular, is playing an ever increasingly important
  • AAAI-16 / IAAI-16 / EAAI-16 Program

    AAAI-16 / IAAI-16 / EAAI-16 Program

    Thirtieth AAAI Conference on Artificial Intelligence Twenty-Eighth Conference on Innovative Applications of Artificial Intelligence The Sixth Symposium on Educational Advances in AI AAAI-16 / IAAI-16 / EAAI-16 Program February 12–17, 2016 Phoenix Convention Center / Hyatt Regency Phoenix Phoenix, Arizona, USA Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by AI Journal, the National Science Foundation, Baidu, IBM Research, Infosys, Lionbridge, Microsoft Research, Disney Research, University of Southern California/Information Sciences Institute, Yahoo Labs!, ACM/SIGAI, CRA Computing Community Consortium (CCC), and David E. Smith MORNING AFTERNOON EVENING (AFTER 5:00 PM) Friday, February 12 Tutorial Forum Tutorial Forum Student Welcome Reception Workshops Workshops AAAI/SIGAI DC AAAI/SIGAI DC Saturday, February 13 Tutorial Forum Tutorial Forum Speed Dating Workshops Workshops Opening Reception AAAI/SIGAI DC AAAI/SIGAI DC EAAI EAAI Open House Invited Talks Open House Robotics Exhibits Robotics Exhibits Sunday, February 14 AAAI / IAAI Welcome / AAAI Awards Lunch with a Fellow Presidential Address: Dietterich IAAI Invited Talk: Rao AAAI Invited Talk: Krause AAAI / IAAI Technical Program AAAI / IAAI Technical Program AI Job Fair Classic Paper/Robotics Talks What’s Hot Talks / Robotics Talks Poster / Demo Session 1 EAAI EAAI Fellows Dinner Robotics/Vendor Exhibits Robotics/Vendor Exhibits Video Competition Viewing Video Competition Viewing Monday, February 15 Women’s Mentoring Breakfast AAAI Invited Talk:
  • 1 Organizational Change and the Digital Economy

    1 Organizational Change and the Digital Economy

    Organizational Change and the Digital Economy: A Computational Organization Science Perspective Kathleen M. Carley Social and Decision Sciences and H.J.Heinz III School of Public Policy and Management Carnegie Mellon University Pittsburgh, PA 15213 412-268-3225 [email protected] http://sds.hss.cmu.edu/faculty/carley/carley.htm Abstract E-commerce, the web, computers, and information technology in general are often viewed as a technological panacea where all that is needed is better technology to eliminate social and organizational problems, to make organizations more efficient, effective and productive, and to create an effective digital economy. Technological solutions are expected to eliminate barriers to entry, increase the amount of available information, and provide uniform access to information, people, and information based services. There can be little doubt that information technology is transforming social and economic systems, particularly commerce. However, it is still the case that the networks linking people, knowledge, and companies both enable and constrain the impact of this technology. These social and cognitive networks, along with the needs of individuals for privacy, and the needs of companies to protect core intellectual property, are at odds with the open and uniform access assumptions often made about the digital economy. As we move into a digital economy we need to understand how these networks and individual and corporate needs will influence and shape the resulting organizations and markets. Recent work in computational organization science provides guidance for assessing, measuring, monitoring, and predicting organizational change as we move into a digital economy where technological change is increasing information and access in the face of social and cognitive constraints.
  • Formalizing Common Sense Reasoning for Scalable Inconsistency-Robust Information Integration Using Direct Logictm Reasoning and the Actor Model

    Formalizing Common Sense Reasoning for Scalable Inconsistency-Robust Information Integration Using Direct Logictm Reasoning and the Actor Model

    Formalizing common sense reasoning for scalable inconsistency-robust information integration using Direct LogicTM Reasoning and the Actor Model Carl Hewitt. http://carlhewitt.info This paper is dedicated to Alonzo Church, Stanisław Jaśkowski, John McCarthy and Ludwig Wittgenstein. ABSTRACT People use common sense in their interactions with large software systems. This common sense needs to be formalized so that it can be used by computer systems. Unfortunately, previous formalizations have been inadequate. For example, because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic. Our goal is to develop a standard foundation for reasoning in large-scale Internet applications (including sense making for natural language) by addressing the following issues: inconsistency robustness, classical contrapositive inference bug, and direct argumentation. Inconsistency Robust Direct Logic is a minimal fix to Classical Logic without the rule of Classical Proof by Contradiction [i.e., (Ψ├ (¬))├¬Ψ], the addition of which transforms Inconsistency Robust Direct Logic into Classical Logic. Inconsistency Robust Direct Logic makes the following contributions over previous work: Direct Inference Direct Argumentation (argumentation directly expressed) Inconsistency-robust Natural Deduction that doesn’t require artifices such as indices (labels) on propositions or restrictions on reiteration Intuitive inferences hold including the following: . Propositional equivalences (except absorption) including Double Negation and De Morgan . -Elimination (Disjunctive Syllogism), i.e., ¬Φ, (ΦΨ)├T Ψ . Reasoning by disjunctive cases, i.e., (), (├T ), (├T Ω)├T Ω . Contrapositive for implication i.e., Ψ⇒ if and only if ¬⇒¬Ψ . Soundness, i.e., ├T ((├T) ⇒ ) . Inconsistency Robust Proof by Contradiction, i.e., ├T (Ψ⇒ (¬))⇒¬Ψ 1 A fundamental goal of Inconsistency Robust Direct Logic is to effectively reason about large amounts of pervasively inconsistent information using computer information systems.
  • Validating Computational Models

    Validating Computational Models

    Validating Computational Models Kathleen M. Carley Associate Professor of Sociology Department of Social and Decision Sciences Carnegie Mellon University September 1996 This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-93-1-0793 (UConn FRS 521676). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research or the U.S. government. Validating Computational Models Abstract The use of computational models in the social sciences has grown quickly in the past decade. For many these models represent a bewildering and possibly intimidating approach to examining data and developing social and organizational theory. Few researchers have had courses or personal experience in the development and building of computational models and even fewer have an understanding of how to validate such models. And while many papers extort the relative advantages and disadvantages of the computational approach, and many call for the validation of such models, few provide insight into how to validate such models and the issues involved in validation. This paper represents an attempt at redressing this oversight. An overview is provided of computational modeling in the social sciences, types of validation, and some of the issues in doing model validation. Validating Computational Models There is a growing trend in the social and organizational sciences to employ computational models1 in developing and testing theories. Such models are uniquely valuable for addressing issues of learning, adaptation, and evolution. The value of these models for theory building, however, will require an increased understanding of the potential of these models, and when and how they should be validated.
  • Program Guide

    Program Guide

    The First AAAI/ACM Conference on AI, Ethics, and Society February 1 – 3, 2018 Hilton New Orleans Riverside New Orleans, Louisiana, 70130 USA Program Guide Organized by AAAI, ACM, and ACM SIGAI Sponsored by Berkeley Existential Risk Initiative, DeepMind Ethics & Society, Future of Life Institute, IBM Research AI, Pricewaterhouse Coopers, Tulane University AIES 2018 Conference Overview Thursday, February Friday, Saturday, 1st February 2nd February 3rd Tulane University Hilton Riverside Hilton Riverside 8:30-9:00 Opening 9:00-10:00 Invited talk, AI: Invited talk, AI Iyad Rahwan and jobs: and Edmond Richard Awad, MIT Freeman, Harvard 10:00-10:15 Coffee Break Coffee Break 10:15-11:15 AI session 1 AI session 3 11:15-12:15 AI session 2 AI session 4 12:15-2:00 Lunch Break Lunch Break 2:00-3:00 AI and law AI and session 1 philosophy session 1 3:00-4:00 AI and law AI and session 2 philosophy session 2 4:00-4:30 Coffee break Coffee Break 4:30-5:30 Invited talk, AI Invited talk, AI and law: and philosophy: Carol Rose, Patrick Lin, ACLU Cal Poly 5:30-6:30 6:00 – Panel 2: Invited talk: Panel 1: Prioritizing The Venerable What will Artificial Ethical Tenzin Intelligence bring? Considerations Priyadarshi, in AI: Who MIT Sets the Standards? 7:00 Reception Conference Closing reception Acknowledgments AAAI and ACM acknowledges and thanks the following individuals for their generous contributions of time and energy in the successful creation and planning of AIES 2018: Program Chairs: AI and jobs: Jason Furman (Harvard University) AI and law: Gary Marchant
  • CVF Conference on Computer Vision and Pattern Recognition Workshops 2020 CVPR Workshop on Learning with Limited Labels 2020 Oral Presentation, Best Paper Award

    CVF Conference on Computer Vision and Pattern Recognition Workshops 2020 CVPR Workshop on Learning with Limited Labels 2020 Oral Presentation, Best Paper Award

    Rohit Kumar Jena Email j Website j Google Scholar j GitHub Education University of Pennsylvania Philadelphia, PA Ph.D. in Computer and Information Science 2021 { Present Advisors: Prof. Jianbo Shi, Prof. James Gee Carnegie Mellon University Pittsburgh, PA Master of Science in Robotics 2019 { 2021 Advisor: Prof. Katia Sycara GPA: 4.19/4.0 Indian Institute of Technology, Bombay Mumbai, India B.Tech. with Honors in Computer Science and Engineering 2015-2019 Advisor: Prof. Suyash Awate GPA: 9.54/10 Received the Research Excellence Award for outstanding research work during undergraduate Research Interests Computer Vision, Medical Image Analysis, Machine Learning, Deep Learning Conference and Journal Publications Self supervised vessel enhancement using flow-based consistencies Rohit Jena, Sumedha Singla, Kayhan Batmanghelich International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021 Early Accept, MICCAI 2021 Student Travel Award Transfer Learning for Human Navigation and Triage Strategies Prediction in Search and Rescue Task Yue Guo, Rohit Jena, Dana Hughes, Michael Lewis, Katia Sycara International Conference on Robot and Human Interactive Communication (RO-MAN 2021) Augmenting GAIL with behavior cloning for sample efficient imitation learning Rohit Jena, Changliu Liu, Katia Sycara (Long version) Conference on Robot Learning (CoRL) 2020 (Short version) RSS Workshop on Advances & Challenges in Imitation Learning for Robotics 2020, Invited Paper Learning Image Inpainting from Incomplete Images using Self-Supervision Sriram Yenamandra, Ansh Khurana, Rohit Jena, Suyash Awate International Conference on Pattern Recognition (ICPR) 2020 A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration Rohit Jena, Suyash Awate International conference on Information Processing in Medical Imaging (IPMI) 2019 Oral Presentation, opening talk of conference, acceptance rate ∼11% Perfect MCMC Sampling in Bayesian MRFs for Uncertainty Estimation in Segmentation Suyash P.