DOCSLIB.ORG

Accountable Algorithms

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Accountable Algorithms Accountable Algorithms Joshua Alexander Kroll ADissertation Presented to the Faculty of Princeton University in Candidacy for the Degree of Doctor of Philosophy Recommended for Acceptance by the Department of Computer Science Adviser: Professor Edward W. Felten September 2015 c Copyright by Joshua Alexander Kroll, 2015. All rights reserved. Abstract Important decisions about people are increasingly made by algorithms: Votes are counted; voter rolls are purged; financial aid decisions are made; taxpayers are chosen for audits; air travelers are selected for search; credit eligibility decisions are made. Citizens, and society as a whole, have an interest in making these processes more transparent. Yet the full basis for these decisions is rarely available to a↵ected people: the algorithm or some inputs may be secret; or the implementation may be secret; or the process may not be precisely described. A person who suspects the process went wrong has little recourse. And an oversight authority who wants to ensure that decisions are made according to an acceptable policy has little assurance that pro↵ered decision rules match decisions for actual users. Traditionally, Computer Science addresses these problems by demanding a spec- ification of the desired behavior, which can be enforced or verified. But this model is poorly suited to real-world oversight tasks, where specifications are complicated or might not be known in advance; laws are often ambiguous precisely because it would be politically (and practically) infeasible to give a precise description of their meaning. People do their best to approximate what they believe the law will allow and disputes about what is acceptable happen after-the-fact via expensive investiga- tion and adjudication (e.g., in a court or legislature). Actual oversight, in which real decisions are reviewed for their correctness, fairness, or faithfulness to a rule happens only rarely, if at all. Further, Computer Science often sees rules as self-enforcing: the mere fact that an automated check fails is sufficient to demonstrate that some choice was invalid. However, like all rules, automated rules are just the intentions of a system designer and only bear relevance if people will actually follow them, either due to internalized incentives or the external threat of punishment. iii This dissertation relates the tools of technology to the problem of overseeing de- cision making processes. These methods use the tools of computer science to cryp- tographically ensure the technical properties that can be proven, while providing information necessary for a political, legal, or social oversight process to operate ef- fectively. First, we present an example of the current state-of-the-art in technical systems for accountability: a well-defined policy, specified in advance, is operational- ized with technical tools, and those same tools are used to convince outsiders or auditors. Our system enables the accountable execution of legal orders by a judge allowing an investigator compelled access to private records, so that the investigator’s access to sensitive information is limited to only that information which the judge has explicitly allowed (and this can be confirmed by a disinterested third party). Moving beyond these methods, we present a general framework for accountable algorithms, unifying a suite of cryptographic tools to design processes that enable meaningful after-the-fact oversight, consistent with the norm in law and policy. Accountable al- gorithms can attest to the valid operation of a decision policy even when all or part of that policy is kept secret. iv Acknowledgements Iamextremelygratefultomyadvisor,EdFelten,forhisunflaggingsupport,his generosity, and his willingness to go out of his way to be available and helpful to me and all of his students. I’ve learned much from Ed about how to spot interesting research problems, how to execute substantial research, and how to communicate the results of that research for maximum impact. Ihavebeenfortunatetoworkwithawiderangeofcollaboratorsonanumberof projects, and would like to acknowledge the contributions to my graduate career of (in alphabetical order) Andrew Appel, Dan Boneh, Mitchell Berger, Joseph Bonneau, Nicholas Butowski, Joe Calandrino, Jeremy Clark, Will Clarkson, Ian Davey, Anne Edmundson, Steve Englehardt, Ariel Feldman, Steven Goldfeder, J. Alex Halder- mann, Sean Hervey-Jumper, Timothy B. Lee, Peter Johnsen, Ben Jones, Seth Josef- fer, Harry Kalodner, Anna Kornfeld-Simpson, Elliott Krauss, Andrew Miller, Arvind Narayanan, Valeria Nikolaenko, Laura Roberts, Cole Schlesinger, Gordon Stewart, David Wu, Harlan Yu, William Zeller, and Joe Zimmerman. While I did not always get to work with each of you as much as I would have wanted, you certainly all had a strong impact on my thinking about research. I am especially grateful to my fellow students, from whom I learned the most during my time at Princeton. Academically, I have been strongly influenced by Andrew Appel, who taught me the value of thinking clearly about a problem before diving into a solution while remaining efficient, and by Arvind Narayanan, who taught me the value of looking where others are not and the value of eschewing short-term wins when one believes in a long-term plan. Arvind has also taught me the value of real-world adoption as a measure of research output. I would like to thank my entire thesis committee: Andrew Appel, Nick Feamster, Edward W. Felten, Matthew D. Green, and Arvind Narayanan for their candid, careful, and supportive feedback throughout this process. v I have been very fortunate to have the unflagging support of my wife, Ann Frey Kroll, who gave me the strength to keep believing in my work even when it was difficult or seemed not to be going anywhere. Ann has shown immense patience and has taught me to have courage in the face of even great difficulty. Iamalsothankfulforthesupportofmyfamily—myparents,RonandDarcie Kroll; my sister, Amy Kroll; and the many extended family and in-laws who have inspired me—all of them have enriched my experience in pursuing my research career in their own way. This dissertation is dedicated to the memory of my grandparents, Arvel Henning Mattson, who helped stir my childhood interest in engineering, and Verna Trom Mattson, who taught me much about the world and how to live in it e↵ectively. Without them, I would not have had the opportunity to pursue the education I have received, and I am truly grateful. I also acknowledge the memory of my friend, Vice Admiral Thomas J. Hughes, U.S.N., who is responsible for much more of my career than he ever realized. Additionally, I must take the opportunity to thank Princeton for giving me the opportunity and resources to give so much of my time to research in the years since 2009. I am especially grateful to Joy Montero from the office of the Dean of the Graduate School and her successor Lisa Schreyer for their support through my years in New Jersey. Finally, the work in this dissertation was supported in part by a National Science Foundation Graduate Research Fellowship under Grant No. DGE-1148900 and by a gift by Norm B. Tomlinson Jr., ’48 to the Center for Information Technology Policy at Princeton University. vi To my wife, Ann, whose courage and strength impel me to keep trying. vii Contents Abstract..................................... iii Acknowledgements ............................... v 1 Introduction 1 2 Background 21 2.1 Related Concepts from Computer Science . 21 2.1.1 Software Testing . 22 2.1.2 Type Systems and Software Verification . 24 2.1.3 Verified Computation . 26 2.1.4 Fairness in Computer Science . 27 2.1.5 SystemsforCryptographicAssurance . 40 2.1.6 AuditingSystems......................... 43 2.1.7 Measurement of Automated Decision Systems . 46 2.1.8 Accountability........................... 47 2.1.9 OurDefinitionofAccountability. 56 2.2 Related Concepts from Fields Outside Computer Science . 58 2.2.1 Philosophy of Law, Rule of Law, and Software as Law . 59 2.2.2 Due Process . 62 2.2.3 Transparency . 63 2.2.4 ReverseEngineering . 67 viii 2.2.5 Oversight and Enforcement . 69 2.2.6 Fairness . 71 2.2.7 Accountability........................... 76 3 Required Cryptographic Primitives 79 3.1 Common Primitives . 79 3.1.1 Signature Schemes . 80 3.1.2 Zero-Knowledge Proofs . 82 3.2 Primitives Required for Accountable Warrant Execution . 88 3.2.1 Identity-BasedEncryption . 88 3.2.2 Oblivious Transfer . 91 3.2.3 Secret Sharing . 94 3.2.4 Threshold Cryptography . 96 3.3 Primitives Required for General-Purpose Accountable Algorithms . 98 3.3.1 CryptographicCommitments . 98 3.3.2 Verified Computation . 101 3.3.3 Pseudorandomness . 108 3.3.4 Fair Randomness . 113 4 Accountable Warrant Execution 117 4.1 Problem and Setting . 117 4.1.1 Applications............................ 121 4.1.2 Our Results . 123 4.2 Security Model . 124 4.2.1 Setting . 124 4.2.2 Security Goals . 126 4.2.3 A simple, insufficient approach . 129 4.2.4 A complete approach . 131 ix 4.3 Protocol-Specific Primitives . 134 4.3.1 AuditableObliviousTransfer . 135 4.3.2 Sharing the IBE master secret . 136 4.4 ProtocolforAccountableCompelledAccess . 138 4.5 Prototype Implementation . 141 4.5.1 Deployment Concerns . 142 4.6 Evaluation . 145 4.6.1 Encryption Benchmarks . 146 4.6.2 Investigation Benchmarks . 147 4.7 Related Literature . 150 4.8 Discussion and Extensions . 152 4.8.1 Extensions . 153 4.8.2 OnselectingDecryptionAuthorities. 154 5 Constructing Accountable Algorithms 156 5.1 Setting and Model . 157 5.2 AccountableAlgorithms: AGeneralProtocol. 159 5.2.1 Protocol . 161 5.2.2 Analysis .............................. 165 5.2.3 Extensions . 169 5.3 Realizing Accountable Algorithms with Concrete Primitives . 171 6 Example Accountable Algorithms 173 6.1 Implementation . 173 6.1.1 Constraint System Generation . 175 6.2 Evaluation . 177 6.3 Examples . 178 6.3.1 Linear Classification: Scoring and Risk Assessment . 178 x 6.3.2 Fair Classification .
Load more

Recommended publications
  • Nil Ib N O Ir Ali Mi S Na El Oo B Ilp Itl
    ecneicS retupmoC retupmoC ecneicS ecneicS - o t- o l t aA l aA DD DD 9 9 / / OOnn BBiilliinneeaarr TTeecchhnniiqquueess ffoorr 0202 0202 a p pa p r p a r K a K ii t t t aM t aM SSiimmiillaarriittyy SSeeaarrcchh aanndd BBoooolleeaann MMaattrriixx M ultiplication Multiplication MMaatttit iK Kaarprpppaa noi taci lpi t luM xi r taM naelooB dna hcraeS yt i ral imiS rof seuqinhceT raeni l iB nO noi taci lpi t luM xi r taM naelooB dna hcraeS yt i ral imiS rof seuqinhceT raeni l iB nO BBUUSSININESESS + + ECECOONNOOMMY Y NSI I NBS NBS 879 - 879 - 259 - 259 - 06 - 06 - 5198 - 5198 7 - 7 ( p ( r p i n r t i n t de ) de ) AARRT T+ + NSI I NBS NBS 879 - 879 - 259 - 259 - 06 - 06 - 6198 - 6198 4 - 4 ( ( dp f dp ) f ) DDESESIGIGNN + + NSI I NSS NSS 9971 - 9971 - 4394 4394 ( p ( r p i n r t i n t de ) de ) AARRCCHHITIETCECTUTURRE E NSI I NSS NSS 9971 - 9971 - 2494 2494 ( ( dp f dp ) f ) SSCCIEINENCCE E+ + TETCECHHNNOOLOLOGGY Y tirvn tlaA ot laA ot isrevinU yt isrevinU yt CCRROOSSSOOVEVRE R ceic fo o oohcS f l cS o oohcS i f l cS i ecne ecne DDOOCCTOTORRAAL L ecneicS retupmoC retupmoC ecneicS ecneicS DDISISSERERTATTAITOIONNS S DDOOCCTOTORRAAL L +hfbjia*GMFTSH9 +hfbjia*GMFTSH9 fi.otlaa.www . www a . a a l a t o l t . o fi . fi DDISISSERERTATTAITOIONNS S ot laA ytot laA isrevinU yt isrevinU 0202 0202 Aalto University publication series DOCTORAL DISSERTATIONS 9/2020 On Bilinear Techniques for Similarity Search and Boolean Matrix Multiplication Matti Karppa A doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Science, at a public examination held at the lecture hall T2 of the school on 24 January 2020 at 12.
    [Show full text]
  • On-Device Machine Learning: an Algorithms and Learning Theory Perspective
    On-Device Machine Learning: An Algorithms and Learning Theory Perspective SAUPTIK DHAR, America Research Center, LG Electronics JUNYAO GUO, America Research Center, LG Electronics JIAYI (JASON) LIU, America Research Center, LG Electronics SAMARTH TRIPATHI, America Research Center, LG Electronics UNMESH KURUP, America Research Center, LG Electronics MOHAK SHAH, America Research Center, LG Electronics The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state-of-the-art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects ofon-device learning. ACM Reference Format: Sauptik Dhar, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh Kurup, and Mohak Shah.
    [Show full text]
  • Algorithms and Data Structures for Strings, Points and Integers Or, Points About Strings and Strings About Points
    Downloaded from orbit.dtu.dk on: Sep 23, 2021 Algorithms and Data Structures for Strings, Points and Integers or, Points about Strings and Strings about Points Vind, Søren Juhl Publication date: 2015 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Vind, S. J. (2015). Algorithms and Data Structures for Strings, Points and Integers: or, Points about Strings and Strings about Points. Technical University of Denmark. DTU Compute PHD-2015 No. 366 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Algorithms and Data Structures for Strings, Points and Integers or, Points about Strings and Strings about Points Søren Vind Ph.D. Thesis PHD-2015-366 ISSN: 0909-3192 DTU Compute Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 2800 Kongens Lyngby, Denmark Phone +45 4525 3031 [email protected] www.compute.dtu.dk PHD-2015-366 ISSN: 0909-3192 Abstract This dissertation presents our research in the broad area of algorithms and data struc- tures.
    [Show full text]
  • ABSTRACT Adaptive Constraint Reduction for Convex Quadratic
    ABSTRACT Title of dissertation: Adaptive Constraint Reduction for Convex Quadratic Programming and Training Support Vector Machines Jin Hyuk Jung, Doctor of Philosophy, 2008 Dissertation directed by: Professor Dianne P. O'Leary Department of Computer Science Convex quadratic programming (CQP) is an optimization problem of minimiz- ing a convex quadratic objective function subject to linear constraints. We pro- pose an adaptive constraint reduction primal-dual interior-point algorithm for convex quadratic programming with many more constraints than variables. We reduce the computational effort by assembling the normal equation matrix with a subset of the constraints. Instead of the exact matrix, we compute an approximate matrix for a well chosen index set which includes indices of constraints that seem to be most crit- ical. Starting with a large portion of the constraints, our proposed scheme excludes more unnecessary constraints at later iterations. We provide proofs for the global convergence and the quadratic local convergence rate of an affine scaling variant. A similar approach can be applied to Mehrotra's predictor-corrector type algorithms. An example of CQP arises in training a linear support vector machine (SVM), which is a popular tool for pattern recognition. The difficulty in training a support vector machine (SVM) lies in the typically vast number of patterns used for the training process. In this work, we propose an adaptive constraint reduction primal- dual interior-point method for training the linear SVM with l1 hinge loss. We reduce the computational effort by assembling the normal equation matrix with a subset of well-chosen patterns. Starting with a large portion of the patterns, our proposed scheme excludes more and more unnecessary patterns as the iteration proceeds.
    [Show full text]
  • Contents U U U
    Contents u u u ACM Awards Reception and Banquet, June 2018 .................................................. 2 Introduction ......................................................................................................................... 3 A.M. Turing Award .............................................................................................................. 4 ACM Prize in Computing ................................................................................................. 5 ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award ............. 6 ACM – AAAI Allen Newell Award .................................................................................. 7 Software System Award ................................................................................................... 8 Grace Murray Hopper Award ......................................................................................... 9 Paris Kanellakis Theory and Practice Award ...........................................................10 Karl V. Karlstrom Outstanding Educator Award .....................................................11 Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics ..........................................................12 Distinguished Service Award .......................................................................................13 ACM Athena Lecturer Award ........................................................................................14 Outstanding Contribution
    [Show full text]
  • Novel Frameworks for Mining Heterogeneous and Dynamic
    Novel Frameworks for Mining Heterogeneous and Dynamic Networks A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering and Computer Science of the College of Engineering by Chunsheng Fang B.E., Electrical Engineering & Information Science, June 2006 University of Science & Technology of China, Hefei, P.R.China Advisor and Committee Chair: Prof. Anca L. Ralescu November 3, 2011 Abstract Graphs serve as an important tool for discrete data representation. Recently, graph representations have made possible very powerful machine learning algorithms, such as manifold learning, kernel methods, semi-supervised learning. With the advent of large-scale real world networks, such as biological networks (disease network, drug target network, etc.), social networks (DBLP Co- authorship network, Facebook friendship, etc.), machine learning and data mining algorithms have found new application areas and have contributed to advance our understanding of proper- ties, and phenomena governing real world networks. When dealing with real world data represented as networks, two problems arise quite naturally: I) How to integrate and align the knowledge encoded in multiple and heterogeneous networks? For instance, how to find out the similar genes in co-disease and protein-protein inter- action networks? II) How to model and predict the evolution of a dynamic network? A real world exam- ple is, given N years snapshots of an evolving social network, how to build a model that can cap- ture the temporal evolution and make reliable prediction? In this dissertation, we present an innovative graph embedding framework, which identifies the key components of modeling the evolution in time of a dynamic graph.
    [Show full text]
  • Multidimensional and Spatial Structures
    IV Multidimensional and Spatial Structures 16 Multidimensional Spatial Data Structures Hanan Samet ......... 16-1 Introduction • Point Data • Bucketing Methods • Region Data • Rectangle Data • Line Data and Boundaries of Regions • Research Issues and Summary 17 Planar Straight Line Graphs Siu-Wing Cheng....................... 17-1 Introduction • Features of PSLGs • Operations on PSLGs • Winged-Edge • Halfedge • Quadedge • Further Remarks • Glossary 18 Interval, Segment, Range, and Priority Search Trees D. T. Lee .. 18-1 Introduction • Interval Trees • Segment Trees • Range Trees • Priority Search Trees 19 Quadtrees and Octrees Srinivas Aluru ............................... 19-1 Introduction • Quadtrees for Point Data • Spatial Queries with Region Quadtrees • Image Processing Applications • Scientific Computing Applications 20 Binary Space Partitioning Trees Bruce F. Naylor ................... 20-1 Introduction • BSP Trees as a Multi-Dimensional Search Structure • Visibility Or- derings • BSP Tree as a Hierarchy of Regions 21 R-trees Scott Leutenegger and Mario A. Lopez ........................ 21-1 Introduction • Basic Concepts • Improving Performance • Advanced Operations • Analytical Models 22 Managing Spatio-Temporal Data Sumeet Dua and S. S. Iyengar ... 22-1 Introduction and Background • Overlapping Linear Quadtree • 3D R-tree • 2+3 R- tree • HR-tree • MV3R-tree • Indexing Structures for Continuously Moving Objects 23 Kinetic Data Structures Leonidas Guibas ............................ 23-1 Introduction • Motion in Computational Geometry
    [Show full text]
  • A Complete Bibliography of Publications in Communications of the ACM : 2010–2019
    A Complete Bibliography of Publications in Communications of the ACM : 2010{2019 Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 11 December 2019 Version 1.130 Title word cross-reference [2146]. 2017 [2336, 2313]. 21st [2065, 2833, 2656, 1052, 2499, 141]. 3 [2677]. 30-year [2751]. 33 [343]. 3G [352]. #1 [667]. 50th [2359]. + [859, 2507]. $1 [1598, 1575]. 10 2 [166]. 3 [1290, 962, 2165, 1291, 2659, 2164,± 1339, 600km [2693]. 2381, 2572, 250, 2747, 2158]. = [859, 378]. b [614]. λ>4 [2041]. n [857]. n = me [1398]. = [377]. = [2507]. 6 Aadhaar [2995]. abbey [2716]. ABCs -bit [614]. -gram [857]. -Spectre [2728]. [2607]. abductive [67]. abilities [2670]. -sweep [2165]. Ability [2582]. Ability-based [2582]. Abolish [1680]. abound [738]. above 12 [337, 1421, 2056, 1824]. 14th [564]. [2633, 186]. abstract [642, 643]. Abstracting [2459, 642, 643]. Abstraction 2.0 [373, 473, 1306, 1650, 33, 501]. 2008 [661, 1020, 2153, 403]. Abstractions [382]. 2009 [12]. 2011 [626, 711]. 2016 1 2 [1060, 1541]. abstracts [1638]. abuse [104, 694, 207, 237]. adults [2332]. advance [552, 1123]. academia [2194, 992]. [1277, 2217]. advanced [1681]. Advances Academic [1098, 2435, 2437, 2071, 2644, [780]. Advancing [1840, 553]. advantage 2284, 2128, 2232, 1159, 2118, 2023, 170]. [2341]. Adventures [1156]. adversarial academic-unit [2023]. academics [1228]. [2604]. Advertising academies [3016].
    [Show full text]
  • Return of Organization Exempt from Income
    r OMB NO 1545-004 Return of Organization Exempt From Income Tax Form Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung 2004 990 benefit trust or private foundation) Department of the Treasury Open to Public Internal Revenue Service " The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2004 calendar year, or tax year beginning JAN 1 2004 and B Check .r C Name of organization D Employer identification number applicable please, use IRS ssociation for Computing Address label or change print or Machinery, Inc . 1J -171+1J Name type =change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial See return Specific 1515 Broadway 17th Floor Final Instruc- ~return lions City or town, state or country, and ZIP + 4 F Accounting method U IC-171 $ Accrual aAmended return New York NY 10036 - 5701 Application pending 0 Section 501(c)(3) organizations and 4947(a)(1) nonexempt charitable trusts H and I are not applicable to section 527 organizations. must attach pleted Schedule A (Foror 990-EZ) . H(a) Is this a group return for affiliates? Yes No H(b) If "Yes ;" enter number of affdiates " J Organization type (cneckonly one)10- LXJ 501(c) ( 3 ) 1 cnsert no) L_j 4947(a)(1)orU 52 H(c) Are all affiliates included? N/A D Yes 0 No (If "No ;' attach a list.) K Check here 1[:j if the organization's gross receipts are normally not more than $25,000 .
    [Show full text]
  • Department Newsletter Spring 2013
    COMPUTER SCIENCE UNIVERSITY OF MARYLAND Department Newsletter Spring 2013 Chair’s Message This spring saw an impressive start with the overwhelming success of February’s CS Welcome to the spring 2013 edition of UMD’s undergraduate internship and career fair. For the Department of Computer Science newsletter. first time, the event was moved to the Samuel Our department turns 40 this year, continuing its Riggs Alumni Center with approximately 525 legacy as one of the oldest CS collegiate programs students and 72 companies in attendance. Also in in the country. I began my appointment as Chair February was the Department’s 23rd annual High on July 1, 2012 and would like to thank Prof. Larry School Programming Contest with 30 teams from Davis for his 12 years of dedicated service to the Maryland, Virginia and the D.C. metropolitan area. department as Chair. I have been a UMD faculty member for 21 years, Two prestigious speakers will arrive to UMD in and previously served as late April: Jennifer Chayes (Managing Director of the Associate Chair for Microsoft Research New England) and Maneesh Graduate Education for Agrawala (Professor at UC Berkeley). four years. In addition to the events above, this newsletter During this academic contains articles about The Corporate Partners year, the Department Program, led by Eric Chapman. The purpose of the had three teams partnership, which is joint with the Department participate in the Mid- and UMIACS, is to facilitate internship, career and Atlantic regional of research opportunities with our students and the ACM International faculty members. Collegiate Programming Samir Khuller Contest where they all I would like to thank our current partners for CS Department Chair ranked in the top 10 spots.
    [Show full text]
  • Most Prolific DBLP Authors
    Most Prolific DBLP Authors http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/prolific/index... Most Prolific DBLP Authors number of publications / names 484 Philip S. Yu 428 Chin-Chen Chang 421 Elisa Bertino 415 Edwin R. Hancock 412 Sudhakar M. Reddy 410 Grzegorz Rozenberg 393 Alberto L. Sangiovanni-Vincentelli 390 Thomas S. Huang 378 Micha Sharir - Kang G. Shin 375 Wei Wang 373 Mahmut T. Kandemir 349 Wen Gao 345 Irith Pomeranz 341 Hector Garcia-Molina 339 Moshe Y. Vardi 336 Joseph Y. Halpern 329 Noga Alon 322 Anil K. Jain 321 Sushil Jajodia - Ming Li - Wei Li 318 Sajal K. Das 317 Jiawei Han 316 Kaushik Roy - Donald F. Towsley 312 Hans-Peter Seidel 311 Jack Dongarra 302 Josef Kittler 297 Azriel Rosenfeld 296 Christos H. Papadimitriou 293 HongJiang Zhang 286 Mario Piattini - Moti Yung 278 Francky Catthoor 275 Witold Pedrycz 274 Leonidas J. Guibas 273 Kurt Mehlhorn 272 Stephan Olariu 1／26 2008/4/14 上午 11:38 Most Prolific DBLP Authors http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/prolific/index... 268 Christoph Meinel - Ugo Montanari 266 Horst Bunke - Niraj K. Jha - Massoud Pedram 263 Divyakant Agrawal - Michel Raynal - Jie Wu 262 Tharam S. Dillon 261 Hsinchun Chen - John Mylopoulos - Viktor K. Prasanna 260 Oscar H. Ibarra 257 Andrew B. Kahng 255 Hans-Peter Kriegel - Paul G. Spirakis 254 David Eppstein - Martin D. F. Wong 252 Henri Prade 251 Luca Benini - Makoto Takizawa 250 Alok N. Choudhary - Hartmut Ehrig 248 Mary Jane Irwin - Gerhard Weikum 247 Oded Goldreich 245 Gheorghe Paun - David Peleg 243 Erik D.
    [Show full text]
  • Association for Computing Machinery 2 Penn Plaza, Suite 701, New York
    ACM A N W N A N R N D N S Association for Computing Machinery 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA +1-212-869-7440 www.acm.org Contents N N N ACM Awards Reception and Banquet, June 2017 . .2 Introduction . .3 A.M. Turing Award . .4 ACM Prize in Computing . .5 ACM – AAAI Allen Newell Award . .6 Software System Award . .7 Grace Murray Hopper Award . .8 Paris Kanellakis Theory and Practice Award . .9 Karl V. Karlstrom Outstanding Educator Award . .10 ACM Policy Award . .11 Distinguished Service Award . .12 ACM Athena Lecturer Award . .13 Outstanding Contribution to ACM Award . .14 ACM Presidential Award . .15-17 Doctoral Dissertation Award . .18 ACM Student Research Competition . .19 ACM Fellows . .20 Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics . .21 ACM Gordon Bell Prize . .21 ACM – IEEE CS Eckert-Mauchly Award . .22 ACM – IEEE CS Ken Kennedy Award . .22 ACM – IEEE CS George Michael HPC Memorial Fellowships . .23 SIAM – ACM Prize in Computational Science and Engineering . .23 ACM – CSTA Cutler-Bell Prize . .24 ACM India Doctoral Dissertation Award . .24 ACM China Doctoral Dissertation Award . .25 ACM China Rising Star Award . .25 IPSJ/ACM Award for Early Career Contributions to Global Research . .25 ACM Special Interest Group Awards . .26-27 2017 ACM Award Subcommittee Chairs . .28 ACM Award Nomination Submission Procedures . .29 2018 ACM Award Subcommittee Chairs and Members . .30-31 Past Recipients . .32-36 ACM Fellows . .37-43 In Memoriam, ACM Fellows . .44 1 ACM Awards Reception & Banquet ACM AWARDS N N N N N N The Westin St.
    [Show full text]