Curriculum Vitae
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Curriculum Vitae 01/13/2014
Curriculum Vitae 01/13/2014 Dimitris Papamichail Assistant Professor Department of Computer Science The College of New Jersey P.O. Box 7718 Ewing, NJ 08628-0718, USA Phone (office): (609) 771-2268 E-mail: [email protected] Webpage: www.tcnj.edu/~papamicd RESEARCH INTERESTS Applied algorithms, data structures, computational biology. EDUCATION Ph.D. Computer Science, SUNY at Stony Brook, USA, 8/02-8/07 M.S. Computer Science, University of Arizona, USA, 8/96-5/98 B.E. Computer Engineering and Informatics, University of Patras, Greece, 9/91-6/96 WORK EXPERIENCE • Assistant Professor, Department of Computer Science, The College of New Jersey, 8/13 – present. • Assistant Professor, Department of Computer Science, University of Miami, 8/07 – 8/13 • Research Assistant, Brookhaven National Laboratory, Upton, USA, 10/04 - 8/07 • Research Assistant, Computer Science department, SUNY at Stony Brook, Stony Brook, USA, 9/03 - 8/07 • Teaching Assistant, Computer Science department, SUNY at Stony Brook, Stony Brook, USA, 8/02 - 7/03 • Computer Programmer, Division of Research and Informatics, Greek Army, Greece, 9/00 - 3/02 • Site Engineer, Modular Mining Systems Inc., Tucson, Arizona, USA, 8/98 - 4/00 • Teaching assistant, Computer Science department, University of Arizona, Arizona, USA, 8/97 - 5/98 • Programmer, Lunar and Planetary Laboratory, University of Arizona, Tucson, Arizona, USA, 1/97 - 6/97 • Programmer, National Electric Company of Greece, Athens, Greece, 6/94 - 8/94 • Systems Operator, Computer Institute of Technology, Patras, Greece, 6/92 - 9/93 AWARDS/DISTINCTIONS • University of Miami Faculty Learning Community (FLC) Fellow, 2010. • Best paper award, Graduate Research Conference 2004, Computer Science Department, Stony Brook University. -
On Some Fine-Grained Questions in Algorithms and Complexity
On some fine-grained questions in algorithms and complexity Virginia Vassilevska Williams Massachusetts Institute of Technology, EECS and CSAIL Abstract In recent years, a new “fine-grained” theory of computational hardness has been developed, based on “fine-grained reductions” that focus on exact running times for problems. Mimicking NP-hardness, the approach is to (1) select a key problem X that for some function t, is conjectured to not be solvable by any O(t(n)1−") time algorithm for " > 0, and (2) reduce X in a fine-grained way to many impor- tant problems, thus giving tight conditional time lower bounds for them. This approach has led to the discovery of many meaningful relationships between problems, and to equivalence classes. The main key problems used to base hardness on have been: the 3-SUM problem, the CNF-SAT problem (based on the Strong Exponential Time Hypothesis (SETH)) and the All Pairs Shortest Paths Problem. Research on SETH-based lower bounds has flourished in particular in recent years showing that the classical algorithms are optimal for problems such as Approximate Diameter, Edit Distance, Frechet Distance and Longest Common Subsequence. This paper surveys the current progress in this area, and highlights some exciting new developments. 1 Introduction Arguably the main goal of the theory of algorithms is to study the worst case time complexity of fundamental computational problems. When considering a problem P , we fix a computational model, such as a Random Access Machine (RAM) or a Turing machine (TM). Then we strive to develop an efficient algorithm that solves P and to prove that for a (hopefully slow growing) function t(n), the algorithm solves P on instances of size n in O(t(n)) time in that computational model. -
Curriculum Vitae
Curriculum Vitae Dimitris Papamichail Assistant Professor Department of Computer Science 6000 SW 76th St University of Miami South Miami, FL 33143, USA P.O. Box 248154 Phone (cell): (631) 428-4962 Coral Gables, FL 33124-4245, USA Phone (home): (305) 667-7042 Phone (office): (305) 284-4189 e-mail: [email protected] webpage: www.cs.miami.edu/~dimitris Research Interests Bioinformatics, algorithms and data structures, optimization, decision support systems. Education Ph.D. Computer Science, SUNY at Stony Brook, USA, 8/02-8/07 M.S. Computer Science, University of Arizona, USA, 8/96-5/98 B.E. Computer Engineering and Informatics, University of Patras, Greece, 9/91-6/96 Work Experience Assistant Professor, Department of Computer Science, University of Miami, 08/07-date Research Assistant, Brookhaven National Laboratory, Upton, USA, 10/04-8/07 Research Assistant, Computer Science department, SUNY at Stony Brook, Stony Brook, USA, 9/03-8/07 Teaching Assistant, Computer Science department, SUNY at Stony Brook, Stony Brook, USA, 8/02-7/03 Computer Programmer, Division of Research and Informatics, Greek Army, Greece, 9/00-3/02 Site Engineer, Modular Mining Systems Inc., Tucson, Arizona, USA, 8/98-4/00 Teaching assistant, Computer Science department, University of Arizona, Arizona, USA, 8/97-5/98 Programmer, Lunar and Planetary Laboratory, University of Arizona, Tucson, Arizona, USA, 1/97- 6/97 Programmer, National Electric Company of Greece, Athens, Greece, 6/94-8/94 Systems Operator, Computer Institute of Technology, Patras, Greece, 6/92-9/93 Awards/Distinctions Best paper award, Graduate Research Conference 2004, Computer Science Dept. SUNY at Stony Brook. -
Who's Bigger? a Quantitative Analysis of Historical Fame
Who's Bigger? A Quantitative Analysis of Historical Fame Who's Bigger? A Quantitative Analysis of Historical Fame Steven Skiena1 Charles Ward1 1 Department of Computer Science Stony Brook University Stony Brook, NY 11794-4400 September 5, 2012 Who's Bigger? A Quantitative Analysis of Historical Fame Warmup Outline 1 Warmup 2 Methodology 3 Applications Trends Analysis Evaluating Human Selection Processes Gender Imbalance in Wikipedia 4 Conclusions Who's Bigger? A Quantitative Analysis of Historical Fame Warmup Who's Bigger: Historical Rankings Numerical ratings/rankings provide a way to focus greater attention on the best/most important things. Rankings are highly subjective and culturally biased, yet rankings provide a popular mix of education and entertainment. We seek algorithms to construct informative and meaningful historical rankings of all the people described in Wikipedia. George Washington (1732{1799) [6] Abraham Lincoln (1809{1865) [5]* Chester A. Arthur (1829{1886) [490] Who's Bigger? A Quantitative Analysis of Historical Fame Warmup Who's Bigger? (Presidents) Battle: George Washington vs. Abraham Lincoln Who's Bigger? A Quantitative Analysis of Historical Fame Warmup Who's Bigger? (Presidents) Battle: George Washington vs. Abraham Lincoln George Washington (1732{1799) [6] Abraham Lincoln (1809{1865) [5]* Chester A. Arthur (1829{1886) [490] Jesus (7 B.C.{30 A.D.) [1]* John Lennon (1940{1980) [141] Paul McCartney (1942{ ) [399] George Harrison (1943{2001) [615] Ringo Starr (1940{ ) [1729] Who's Bigger? A Quantitative Analysis of Historical Fame Warmup Are the Beatles \Bigger than Jesus"? Battle: Jesus vs. John Lennon Who's Bigger? A Quantitative Analysis of Historical Fame Warmup Are the Beatles \Bigger than Jesus"? Battle: Jesus vs. -
Programming Challenges (PDF)
TEXTS IN COMPUTER SCIENCE Editors David Gries Fred B. Schneider Springer New York Berlin Heidelberg Hong Kong London Milan Paris Tokyo This page intentionally left blank Steven S. Skiena Miguel A. Revilla PROGRAMMING CHALLENGES The Programming Contest Training Manual With 65 Illustrations Steven S. Skiena Miguel A. Revilla Department of Computer Science Department of Applied Mathematics SUNY Stony Brook and Computer Science Stony Brook, NY 11794-4400, USA Faculty of Sciences [email protected] University of Valladolid Valladolid, 47011, SPAIN [email protected] Series Editors: David Gries Fred B. Schneider Department of Computer Science Department of Computer Science 415 Boyd Graduate Studies Upson Hall Research Center Cornell University The University of Georgia Ithaca, NY 14853-7501, USA Athens, GA 30602-7404, USA Cover illustration: “Spectator,” by William Rose © 2002. Library of Congress Cataloging-in-Publication Data Skeina, Steven S. Programming challenges : the programming contest training manual / Steven S. Skiena, Miguel A. Revilla. p. cm. — (Texts in computer science) Includes bibliographical references and index. ISBN 0-387-00163-8 (softcover : alk. paper) 1. Computer programming. I. Revilla, Miguel A. II. Title. III. Series. QA76.6.S598 2003 005.1—dc21 2002044523 ISBN 0-387-00163-8 Printed on acid-free paper. © 2003 Springer-Verlag New York, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer-Verlag New York, Inc., 175 Fifth Avenue, New York, NY 10010, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. -
Lecture 1: Introduction to Finance Steven Skiena Department Of
Lecture 1: Introduction to Finance Steven Skiena Department of Computer Science State University of New York Stony Brook, NY 11794–4400 http://www.cs.sunysb.edu/∼skiena Why Computational Finance? It is hard to overstate the importance of the financial industry to the state, national, and world economies. The financial industry relies on a wide variety of advanced computing technologies and mathematical modeling tech- niques. The availability, volume and significance of financial data lends itself to many interesting research problems. There are interesting and relevant theories of how markets work which lend themselves to formal analysis. Why Are You Interested in this Course? • Have a general interest in finance? • Avoiding a more undesirable course? • Want a job in the financial industry? • Want to apply your research expertise to financial data? • Other? Who am I? I am a visiting professor from Stony Brook University. My primary research is in combinatorial algorithms and their applications, particularly to biology and news/blog analysis. Why am I teaching this? I have a side interest in gambling systems, which has made me somewhat known in the hedge fund community. I’ve been an invited speaker at Barra’s Research Conference and O’Reilly Money:Tech, and at certain hedge funds. I’ve previously taught a seminar in computational finance at Stony Brook. Why this Course? Much of this material (e.g. financial engineering and time-series data analysis) is traditionally taught in business schools, and practiced by quantitative researchers from economics, finance, and the mathematical sciences. Computer science researchers have not been as engaged with conceptual problems in finance so much as technological problems like encryption and transaction processing. -
Kent D. Lee Steve Hubbard Data Structures and Algorithms with Python Undergraduate Topics in Computer Science
Undergraduate Topics in Computer Science Kent D. Lee Steve Hubbard Data Structures and Algorithms with Python Undergraduate Topics in Computer Science This copy belongs to 'acha04' Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one- or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions. More information about this series at http://www.springer.com/series/7592 This copy belongs to 'acha04' Kent D. Lee • Steve Hubbard Data Structures and Algorithms with Python 123 This copy belongs to 'acha04' Kent D. Lee Steve Hubbard Luther College Decorah, IA USA Series editor Ian Mackie Advisory Board Samson Abramsky, University of Oxford, Oxford, UK Karin Breitman, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil Chris Hankin, Imperial College London, London, UK Dexter Kozen, Cornell University, Ithaca, USA Andrew Pitts, University of Cambridge, Cambridge, UK Hanne Riis Nielson, Technical University of Denmark, Kongens Lyngby, Denmark Steven Skiena, Stony Brook University, Stony Brook, USA Iain Stewart, University of Durham, Durham, UK ISSN 1863-7310 ISSN 2197-1781 (electronic) ISBN 978-3-319-13071-2 ISBN 978-3-319-13072-9 (eBook) DOI 10.1007/978-3-319-13072-9 Library of Congress Control Number: 2014953918 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. -
Select Research Projects
Select Research Projects CEWIT | Center of Excellence in Wireless and Information Technology Table of Contents Security Securing Mobile Apps and Platforms .........................................................................................6 National Security Institute .........................................................................................................6 Cloud Computing Security .........................................................................................................6 Security Policy Mining ...............................................................................................................7 Security Policy Analysis .............................................................................................................7 Fast, Architecture-neutral Static Binary Instrumentation for Security Applications.......................7 Security and Privacy in Geo-Social Networks .............................................................................8 NFS4Sec: An Extensible Security Layer for Network Storage ......................................................8 Leadership Security and Privacy in Geo-Social Networks .............................................................................8 NFS4Sec: An Extensible Security Layer for Network Storage ......................................................8 Yacov Shamash, PhD UID: A Strongly-Secure Usable Identity Ecosystem with Privacy .................................................9 Vice President of Economic Cloudtracker: Transparent, -
BIOGRAPHICAL SKETCH Steven Skiena Distinguished Teaching
Program Director/Principal Investigator (Last, First, Middle): BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors in the order listed on Form Page 2. Follow this format for each person. DO NOT EXCEED FOUR PAGES. NAME POSITION TITLE Steven Skiena Distinguished Teaching Professor eRA COMMONS USER NAME (credential, e.g., agency login) Dept. of Computer Science Stony Brook University EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable.) DEGREE INSTITUTION AND LOCATION MM/YY FIELD OF STUDY (if applicable) University of Virginia, Charlottesville, VA B.S. 05/83 Computer Science University of Illinois at Urbana-Champaign Ph.D 05/88 Computer Science A. Personal Statement I have worked in computational biology / bioinformatics for almost twenty years, since the very start of the genomics revolution. My interests over the years have ranged from genome assembly algorithms, microrarray design and analysis, and meta-genomic sequence analysis. My primary interest lies in sequence design algorithms for synthetic biology. My students and I pioneered the study of algorithms coding for given protein sequencing while optimizing specific criteria such as RNA secondary structure, pattern placement / avoidance, and coding in alternate reading strands. This work forms the foundation of my current collaborations on sequence design for live attenuated vaccines, multiplex sequence design algorithms for large-scale synthesis technologies, and design/synthesis approaches to signal-detection in coding sequences. B. Positions and Honors Positions and Employment 2009-date Distinguished Teaching Professor of Computer Science, Stony Brook University 2008-2009 Visiting Professor of Computer Science, Hong Kong University of Science and Technology 2001-2009 Professor of Computer Science, Stony Brook University Affiliated faculty, Dept. -
Representation Learning for Natural Language Processing Representation Learning for Natural Language Processing Zhiyuan Liu • Yankai Lin • Maosong Sun
Zhiyuan Liu · Yankai Lin · Maosong Sun Representation Learning for Natural Language Processing Representation Learning for Natural Language Processing Zhiyuan Liu • Yankai Lin • Maosong Sun Representation Learning for Natural Language Processing 123 Zhiyuan Liu Yankai Lin Tsinghua University Pattern Recognition Center Beijing, China Tencent Wechat Beijing, China Maosong Sun Department of Computer Science and Technology Tsinghua University Beijing, China ISBN 978-981-15-5572-5 ISBN 978-981-15-5573-2 (eBook) https://doi.org/10.1007/978-981-15-5573-2 © The Editor(s) (if applicable) and The Author(s) 2020. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adap- tation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publi- cation does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.