DOCSLIB.ORG

Sketching and Streaming High-Dimensional Vectors Jelani Nelson

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Sketching and Streaming High-Dimensional Vectors Jelani Nelson Sketching and Streaming High-Dimensional Vectors by Jelani Nelson S.B., Massachusetts Institute of Technology (2005) M.Eng., Massachusetts Institute of Technology (2006) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2011 c Massachusetts Institute of Technology 2011. All rights reserved. Author.............................................................. Department of Electrical Engineering and Computer Science May 20, 2011 Certified by. Erik D. Demaine Professor Thesis Supervisor Certified by. Piotr Indyk Professor Thesis Supervisor Accepted by . Leslie A. Kolodziejski Chairman, Department Committee on Graduate Students Sketching and Streaming High-Dimensional Vectors by Jelani Nelson Submitted to the Department of Electrical Engineering and Computer Science on May 20, 2011, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Engineering Abstract A sketch of a dataset is a small-space data structure supporting some prespecified set of queries (and possibly updates) while consuming space substantially sublinear in the space required to actually store all the data. Furthermore, it is often desirable, or required by the application, that the sketch itself be computable by a small-space algorithm given just one pass over the data, a so-called streaming algorithm. Sketching and streaming have found numerous applications in network traffic monitoring, data mining, trend detection, sensor networks, and databases. In this thesis, I describe several new contributions in the area of sketching and streaming algorithms. • The first space-optimal streaming algorithm for the distinct elements problem. Our algorithm also achieves O(1) update and reporting times. • A streaming algorithm for Hamming norm estimation in the turnstile model which achieves the best known space complexity. • The first space-optimal algorithm for pth moment estimation in turnstile streams for 0 < p < 2, with matching lower bounds, and another space-optimal algo- rithm which also has a fast O(log2(1=") log log(1=")) update time for (1 ± ")- approximation. • A general reduction from empirical entropy estimation in turnstile streams to moment estimation, providing the only known near-optimal space-complexity upper bound for this problem. • A proof of the Johnson-Lindenstrauss lemma where every matrix in the support of the embedding distribution is much sparser than previous known construc- tions. In particular, to achieve distortion (1 ± ") with probability 1 − δ, we embed into optimal dimension O("−2 log(1/δ)) and such that every matrix in the support of the distribution has O("−1 log(1/δ)) non-zero entries per column. Thesis Supervisor: Erik D. Demaine Title: Professor Thesis Supervisor: Piotr Indyk Title: Professor 2 Acknowledgments Including my years as an undergraduate I have been at MIT for a full decade, and I have come across many friends, colleagues, research advisors, and teachers who have made the experience very enjoyable. For this, I am very grateful. I thank my advisors Prof. Erik Demaine and Prof. Piotr Indyk for their guidance in my PhD studies. I was primarily interested in external memory data structures when I entered the program, an area within Erik's research interests, and Erik was very supportive while I explored questions in this realm. After taking a course on sketching and streaming algorithms, my research interests migrated toward that area at which point Piotr began giving me much guidance and became an unofficial (and eventually official) co-advisor, and then even officemate if one considers the 6th floor lounge to be an office. I have received a great amount of influence and guidance from Erik and Piotr, and I am grateful to have been their student. I would like to thank my coauthors: Tim Abbott, Miki Ajtai, Michael Bender, Michael Burr, Timothy Chan, Erik Demaine, Marty Demaine, Ilias Diakonikolas, Martin Farach-Colton, Vitaly Feldman, Jeremy Fineman, Yonatan Fogel, Nick Har- vey, Avinatan Hassidim, John Hugg, Daniel Kane, Bradley Kuszmaul, Stefan Langer- man, Raghu Meka, Krzysztof Onak, Ely Porat, Eynat Rafalin, Kathryn Seyboth, David Woodruff, and Vincent Yeung. I have been fortunate to have enjoyed every collaboration I have been a part of with these colleagues, and I have learned much from them. I in particular collaborated quite frequently with Daniel and David, both of whom I knew even before beginning my PhD studies, and it has been a pleasure to work with them so often. I have learned a great deal by doing so. While on the topic of learning from others, aside from advisors and coauthors I have been fortunate to have been surrounded by several colleagues and friends who I have learned a great deal from concerning theoretical computer science and other relevant areas of mathematics, at MIT, at the IBM Almaden Research Center and Microsoft Research New England during summer internships, at the Technion in the summer prior to beginning my PhD, and elsewhere. Last but not least, I would like to thank God and my parents for creating me and enabling me to have an enjoyable life thus far. 3 In memory of my high school mathematics teacher Jos´eEnrique Carrillo. 4 Contents 1 Introduction 9 1.1 Notation and Preliminaries . 11 1.2 Distinct Elements . 12 1.3 Moment Estimation . 13 1.4 Entropy Estimation . 15 1.5 Johnson-Lindenstrauss Transforms . 16 1.5.1 Sparse Johnson-Lindenstrauss Transforms . 16 1.5.2 Derandomizing the Johnson-Lindenstrauss Transform . 17 2 Distinct Elements 20 2.1 Balls and Bins with Limited Independence . 22 2.2 F0 estimation algorithm . 24 2.2.1 RoughEstimator . 25 2.2.2 Full algorithm . 28 2.2.3 Handling small F0 ......................... 32 2.2.4 Running time . 33 2.3 `0 estimation algorithm . 36 2.3.1 A Rough Estimator for `0 estimation . 38 2.4 Further required proofs . 40 2.4.1 Various calculations for balls and bins . 40 2.4.2 A compact lookup table for the natural logarithm . 43 3 Moment Estimation 45 3.1 FT-Mollification . 46 3.2 Slow Moment Estimation in Optimal Space . 49 3.2.1 Alterations for handling limited precision . 60 3.2.2 Approximating the median of jDpj . 61 3.3 Fast Moment Estimation in Optimal Space . 66 3.3.1 Estimating the contribution from heavy hitters . 68 3.3.2 Estimating the contribution from light elements . 75 3.3.3 The final algorithm: putting it all together . 80 3.3.4 A derandomized geometric mean estimator variant . 84 3.3.5 A heavy hitter algorithm for Fp . 88 3.4 Lower Bounds . 90 3.5 The additive log log d in `p bounds . 94 5 3.5.1 The upper bounds . 94 3.5.2 The lower bounds . 95 4 Entropy Estimation 96 4.1 Noisy Extrapolation . 97 4.1.1 Bounding the First Error Term . 98 4.1.2 Bounding the Second Error Term . 98 4.2 Additive Estimation Algorithm . 100 4.3 Multiplicative Estimation Algorithm . 105 4.4 Lower Bound . 109 5 Johnson-Lindenstrauss Transforms 111 5.1 Sparse JL transforms . 111 5.1.1 A Warmup Proof of the JL Lemma . 113 5.1.2 Code-Based Sparse JL Constructions . 114 5.1.3 Random Hashing Sparse JL Constructions . 116 5.1.4 Tightness of Analyses . 123 5.2 Numerical Linear Algebra Applications . 127 5.2.1 Linear regression . 129 5.2.2 Low rank approximation . 129 5.3 A proof of the Hanson-Wright inequality . 131 5.4 Derandomizing the JL lemma . 133 6 Conclusion 134 6 List of Figures 2-1 RoughEstimator pseudocode. With probability 1 − o(1), Fe0(t) = Θ(F0(t)) at every point t in the stream for which F0(t) ≥ KRE. The value ρ is :99 · (1 − e−1=3). 25 2-2 F0 algorithm pseudocode. With probability 11=20, Fe0 = (1 ± O("))F0. 29 2-3 An algorithm skeleton for F0 estimation. 36 3-1 Indyk's derandomized `p estimation algorithm pseudocode, 0 < p < 2, assuming infinite precision. 50 3-2 LightEstimator, for estimating contribution from non-heavy hitters . 76 4-1 Algorithm for additively approximating empirical entropy. 100 5-1 Three constructions to project a vector in Rd down to Rk. Figure (a) is the DKS construction in [35], and the two constructions we give in this work are represented in (b) and (c). The out-degree in each case is s, the sparsity. 112 7 List of Tables 1.1 Catalog of problems studied in this thesis, and our contributions. 10 1.2 Comparison of our algorithm to previous algorithms on estimating the number of distinct elements in a data stream. Update time not listed for algorithms assuming random oracle. 12 1.3 Comparison of our contribution to previous works on Fp estimation in data streams. 14 8 Chapter 1 Introduction In 1975, Morris discovered the following surprising result: one can count up to m using exponentially fewer than log m bits. For example, he showed how to approximately count up to numbers as large as 130; 000 using 8-bit registers, with 95% confidence that his returned estimate was within 24% of the true count. In general terms, the method allows one to approximately count the number of items in a sequence of length m using only O(log log m) bits, given only one pass over the sequence and such that the estimated sequence length has small relative error with large probability. The method was simple: maintain a value ν in memory which one imagines represents log(1 + m), then after seeing a new item in the sequence, increment ν with proba- bility exponentially small in ν. Morris' algorithm was the first non-trivial low-space streaming algorithm: an algorithm that computes a function of its input given only one or a few passes over it.
Load more

Recommended publications
  • Iasinstitute for Advanced Study
    IAInsti tSute for Advanced Study Faculty and Members 2012–2013 Contents Mission and History . 2 School of Historical Studies . 4 School of Mathematics . 21 School of Natural Sciences . 45 School of Social Science . 62 Program in Interdisciplinary Studies . 72 Director’s Visitors . 74 Artist-in-Residence Program . 75 Trustees and Officers of the Board and of the Corporation . 76 Administration . 78 Past Directors and Faculty . 80 Inde x . 81 Information contained herein is current as of September 24, 2012. Mission and History The Institute for Advanced Study is one of the world’s leading centers for theoretical research and intellectual inquiry. The Institute exists to encourage and support fundamental research in the sciences and human - ities—the original, often speculative thinking that produces advances in knowledge that change the way we understand the world. It provides for the mentoring of scholars by Faculty, and it offers all who work there the freedom to undertake research that will make significant contributions in any of the broad range of fields in the sciences and humanities studied at the Institute. Y R Founded in 1930 by Louis Bamberger and his sister Caroline Bamberger O Fuld, the Institute was established through the vision of founding T S Director Abraham Flexner. Past Faculty have included Albert Einstein, I H who arrived in 1933 and remained at the Institute until his death in 1955, and other distinguished scientists and scholars such as Kurt Gödel, George F. D N Kennan, Erwin Panofsky, Homer A. Thompson, John von Neumann, and A Hermann Weyl. N O Abraham Flexner was succeeded as Director in 1939 by Frank Aydelotte, I S followed by J.
    [Show full text]
  • Curriculum Vitae
    Massachusetts Institute of Technology School of Engineering Faculty Personnel Record Date: April 1, 2020 Full Name: Charles E. Leiserson Department: Electrical Engineering and Computer Science 1. Date of Birth November 10, 1953 2. Citizenship U.S.A. 3. Education School Degree Date Yale University B. S. (cum laude) May 1975 Carnegie-Mellon University Ph.D. Dec. 1981 4. Title of Thesis for Most Advanced Degree Area-Efficient VLSI Computation 5. Principal Fields of Interest Analysis of algorithms Caching Compilers and runtime systems Computer chess Computer-aided design Computer network architecture Digital hardware and computing machinery Distance education and interaction Fast artificial intelligence Leadership skills for engineering and science faculty Multicore computing Parallel algorithms, architectures, and languages Parallel and distributed computing Performance engineering Scalable computing systems Software performance engineering Supercomputing Theoretical computer science MIT School of Engineering Faculty Personnel Record — Charles E. Leiserson 2 6. Non-MIT Experience Position Date Founder, Chairman of the Board, and Chief Technology Officer, Cilk Arts, 2006 – 2009 Burlington, Massachusetts Director of System Architecture, Akamai Technologies, Cambridge, 1999 – 2001 Massachusetts Shaw Visiting Professor, National University of Singapore, Republic of 1995 – 1996 Singapore Network Architect for Connection Machine Model CM-5 Supercomputer, 1989 – 1990 Thinking Machines Programmer, Computervision Corporation, Bedford, Massachusetts 1975
    [Show full text]
  • Dynamic Ham-Sandwich Cuts in the Plane∗
    Dynamic Ham-Sandwich Cuts in the Plane∗ Timothy G. Abbott† Michael A. Burr‡ Timothy M. Chan§ Erik D. Demaine† Martin L. Demaine† John Hugg¶ Daniel Kanek Stefan Langerman∗∗ Jelani Nelson† Eynat Rafalin†† Kathryn Seyboth¶ Vincent Yeung† Abstract We design efficient data structures for dynamically maintaining a ham-sandwich cut of two point sets in the plane subject to insertions and deletions of points in either set. A ham- sandwich cut is a line that simultaneously bisects the cardinality of both point sets. For general point sets, our first data structure supports each operation in O(n1/3+ε) amortized time and O(n4/3+ε) space. Our second data structure performs faster when each point set decomposes into a small number k of subsets in convex position: it supports insertions and deletions in O(log n) time and ham-sandwich queries in O(k log4 n) time. In addition, if each point set has convex peeling depth k, then we can maintain the decomposition automatically using O(k log n) time per insertion and deletion. Alternatively, we can view each convex point set as a convex polygon, and we show how to find a ham-sandwich cut that bisects the total areas or total perimeters of these polygons in O(k log4 n) time plus the O((kb) polylog(kb)) time required to approximate the root of a polynomial of degree O(k) up to b bits of precision. We also show how to maintain a partition of the plane by two lines into four regions each containing a quarter of the total point count, area, or perimeter in polylogarithmic time.
    [Show full text]
  • Jelani Nelson Current Position Previous Positions Education
    Jelani Nelson Curriculum Vitae John A. Paulson School of Engineering and Applied Sciences Tel: (617) 496-1440 Maxwell Dworkin 125 Fax: (617) 496-3012 33 Oxford St. [email protected] Cambridge, MA 02138 http://people.seas.harvard.edu/~minilek/ Current Position Harvard University Cambridge, MA • John L. Loeb Associate Professor of Engineering and Applied Sciences, Jul 2017{present • Associate Professor of Computer Science, Jul 2017{present Previous Positions Harvard University Cambridge, MA • Assistant Professor of Computer Science, Jul 2013{Jun 2017 Institute for Advanced Study Princeton, NJ • Member, Sep 2012{Jun 2013 • Mentor: Avi Wigderson Princeton University Princeton, NJ • Postdoctoral fellow, Center for Computational Intractibility, Jan 2012{Aug 2012 Mathematical Sciences Research Institute Berkeley, CA • Postdoctoral fellow, Program in Quantitative Geometry, Aug 2011{Dec 2011 • Mentor: Adam Klivans Education Massachusetts Institute of Technology Cambridge, MA • Ph.D. in Computer Science, June 2011. • Advisors: Professor Erik D. Demaine, Professor Piotr Indyk. • Thesis: Sketching and Streaming High-Dimensional Vectors. Massachusetts Institute of Technology Cambridge, MA • M.Eng. in Computer Science, June 2006. • Advisors: Dr. Bradley C. Kuszmaul, Professor Charles E. Leiserson. • Thesis: External-Memory Search Trees with Fast Insertions. Massachusetts Institute of Technology Cambridge, MA • S.B. in Computer Science, June 2005. • S.B. in Mathematics, June 2005. Honors • Presidential Early Career Award for Scientists and Engineers (PECASE), 2017. • Alfred P. Sloan Research Fellow, 2017. • ONR Director of Research Early Career Grant, 2017{2022. • Harvard University Clark Fund Award, 2017. • ONR Young Investigator Award, 2015{2018. • NSF Early Career Development (CAREER) Award, 2014{2019. • George M. Sprowls Award, given for best doctoral theses in computer science at MIT, 2011.
    [Show full text]
  • Curriculum Vitae
    Curriculum Vitae David P. Woodruff Biographical Computer Science Department Gates-Hillman Complex Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 Citizenship: United States Email: [email protected] Home Page: http://www.cs.cmu.edu/~dwoodruf/ Research Interests Compressed Sensing, Data Stream Algorithms and Lower Bounds, Dimensionality Reduction, Distributed Computation, Machine Learning, Numerical Linear Algebra, Optimization Education All degrees received from Massachusetts Institute of Technology, Cambridge, MA Ph.D. in Computer Science. September 2007 Research Advisor: Piotr Indyk Thesis Title: Efficient and Private Distance Approximation in the Communication and Streaming Models. Master of Engineering in Computer Science and Electrical Engineering, May 2002 Research Advisor: Ron Rivest Thesis Title: Cryptography in an Unbounded Computational Model Bachelor of Science in Computer Science and Electrical Engineering May 2002 Bachelor of Pure Mathematics May 2002 Professional Experience August 2018-present, Carnegie Mellon University Computer Science Department Associate Professor (with tenure) August 2017 - present, Carnegie Mellon University Computer Science Department Associate Professor June 2018 – December 2018, Google, Mountain View Research Division Visiting Faculty Program August 2007 - August 2017, IBM Almaden Research Center Principles and Methodologies Group Research Scientist Aug 2005 - Aug 2006, Tsinghua University Institute for Theoretical Computer Science Visiting Scholar. Host: Andrew Yao Jun - Jul
    [Show full text]
  • Visions in Theoretical Computer Science
    VISIONS IN THEORETICAL COMPUTER SCIENCE A Report on the TCS Visioning Workshop 2020 The material is based upon work supported by the National Science Foundation under Grants No. 1136993 and No. 1734706. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. VISIONS IN THEORETICAL COMPUTER SCIENCE A REPORT ON THE TCS VISIONING WORKSHOP 2020 Edited by: Shuchi Chawla (University of Wisconsin-Madison) Jelani Nelson (University of California, Berkeley) Chris Umans (California Institute of Technology) David Woodruff (Carnegie Mellon University) 3 VISIONS IN THEORETICAL COMPUTER SCIENCE Table of Contents Foreword ................................................................................................................................................................ 5 How this Document came About ................................................................................................................................... 6 Models of Computation .........................................................................................................................................7 Computational Complexity ................................................................................................................................................7 Sublinear Algorithms ........................................................................................................................................................
    [Show full text]
  • Dimensionality Reduction in Euclidean Space
    Dimensionality Reduction in Euclidean Space Jelani Nelson I begin with a description of what this article is not about. but complementary to the above-mentioned approaches, It is not about Principal Component Analysis (PCA), Ker- as the form of dimension reduction we focus on here for nel PCA, Multidimensional Scaling, ISOMAP, Hessian example can be used to obtain faster algorithms for approx- Eigenmaps, or other methods of dimensionality reduction imate PCA. primarily created to help understand high-dimensional Moving back a few steps from dimension reduction, datasets. Rather, this article focuses on high dimension- more generally an effective technique in the design of al- ality as a barrier to algorithmic efficiency (i.e., low run- gorithms processing geometric data is to employ a metric ning time and/or memory consumption), and explores embedding to transform the input in one given metric space how dimension reduction can be used as an algorithmic to another that is computationally friendlier, and then to tool to overcome this barrier. In fact, as we discuss more in work over the latter space (see the survey [Ind01]). To mea- length in Section 5.2, this view is not only different than sure the quality of such an embedding, we use the follow- ing terminology: given a host metric space 풳 = (푋, 푑푋 ) and Jelani Nelson is a professor of electrical engineering and computer science at the a target space 풴 = (푌, 푑푌 ), 푓 ∶ 푋 → 푌 is said to be a bi- University of California, Berkeley. His email address is minilek@berkeley Lipschitz embedding with distortion 퐷 if there exists a (scal- .edu.
    [Show full text]
  • Daniel M. Kane Education
    Daniel M. Kane CV (Last updated 10/1/2020) Work Address: Department of Computer Science and Engineering, 9500 Gilman Drive #0404, La Jolla, CA 92093-0404 Email: dakane at ucsd dot edu Phone: (858) 246-0102 Website: http://cseweb.ucsd.edu/~dakane/ Citizenship: USA Education: Harvard University: September 2007-May 2011 o M.A. in Mathematics, June 2008 o Ph.D. in Mathematics, May 2011 o Research Advisors: Barry Mazur, Benedict Gross, Henry Cohn Massachusetts Institute of Technology: September 2003- May 2007 o B.S. in Mathematics with Computer Science, June 2007 o B.S. in Physics, June 2007 o Graduated Phi Beta Kappa with a Perfect GPA o Research Advisors: Erik Demaine, Joe Gallian, Cesar Silva University of Wisconsin-Madison: September 1999 – May 2003, enrolled as a special student while in high school o 20 Courses in Mathematics, Physics, Computer Science and Economics o GPA 3.99/4.00 o Research Advisor: Ken Ono Employment: Associate Professor Mathematics and Computer Science and Engineering, University of California, San Diego, 2014-present. Past Employment: Postdoctoral Fellow, Stanford University Department of Mathematics (2011-2014) [on NSF fellowship] Other Employment/Summer Internships: Intern at Center for Communications Research (summers of 2007, 2008, 2009, 2011, 2012, 2013,2014). Continuing consulting. Consultant for Beyondcore (2011-2014). Intern at Microsoft Research New England working with Henry Cohn (summer 2010). Consultant for Professor Peter Coles of the Harvard Business School (2008-2009). MIT Undergraduate Research Opportunities Program (UROP) working under Erik Demaine on problems in theoretical computer science (summer 2006). Participant in the Duluth Research Experiences for Undergraduates program (summer 2005, as a visitor in 2003 and 2006).
    [Show full text]
  • Random 2020 Approx 2020
    RANDOM 2020 The 24th International Conference on Randomization and Computation and APPROX 2020 The 23rd International Conference on Approximation Algorithms for Combinatorial Optimization Problems http://randomconference.wordpress.com http://approxconference.wordpress.com August 17-19, 2020 U. Washington, Seattle CFP - Call for papers SCOPE The 24th International Workshop on Randomization and Computation (RANDOM 2020) and the 23rd International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2020) will be held on August 17-19, 2020 at the University of Washington, Seattle. RANDOM 2020 focuses on applications of randomness to computational and combinatorial problems while APPROX 2020 focuses on algorithmic and complexity theoretic issues relevant to the development of efficient approximate solutions to computationally difficult problems. TOPICS Papers are solicited in all research areas related to randomization and approximation, including but not limited to: RANDOM • design and analysis of randomized algorithms • randomized complexity theory • pseudorandomness and derandomization • random combinatorial structures • random walks/Markov chains • expander graphs and randomness extractors • probabilistic proof systems • random projections and embeddings • error-correcting codes • average-case analysis • smoothed analysis • property testing • computational learning theory APPROX • approximation algorithms • hardness of approximation • small space, sub-linear time and streaming algorithms • online algorithms
    [Show full text]
  • Alfred P. Sloan Research Fellowships 2017
    C M Y K Nxxx,2017-02-21,A,007,Bs-BW,E1 THE NEW YORK TIMES, TUESDAY, FEBRUARY 21, 2017 N A7 Alfred P. Sloan Research Fellowships 2017 The Alfred P. Sloan Foundation congratulates the winners of the 2017 Sloan Research Fellowships. These 126 early-career scholars represent the most promising scientifi c researchers working today. Their achievements and potential place them among the next generation of scientifi c leaders in the U.S. and Canada. Since 1955, Sloan Research Fellows have gone on to win 43 Nobel Prizes, 16 Fields Medals, 69 National Medals of Science, 16 John Bates Clark Medals, and numerous other distinguished awards. CHEMISTRY Siavash Mirarab Tom Goldstein OCEAN SCIENCES University of California, San Diego University of Maryland, College Park Russ Algar Lei Qi Wei Ho Austin Becker University of British Columbia StaNford UNiversity University of Michigan University of Rhode Island Shane Ardo Sriram Sankararaman Thomas Koberda Natalie Burls University of California, Irvine University of California, Los Angeles University of Virginia George Mason University JeY erson Chan Randy Stockbridge Chi Li Bradford Gemmell UNiversity of IlliNois, UrbaNa-ChampaigN University of Michigan Purdue University University of South Florida Bryan Dickinson Gang Liu Pincelli Hull The University of Chicago COMPUTER SCIENCE Northwestern University Yale University Kamil Godula Amir Ali Ahmadi Han Liu Morgan Kelly University of California, San Diego Princeton University Princeton University LouisiaNa State UNiversity Catherine Leimkuhler Grimes Mohammad
    [Show full text]
  • COMPUTING RESEARCH NEWS CRN At-A-Glance
    COMPUTING RESEARCH NEWS Computing Research Association Uniting Industry, Academia and Government to Advance Computing Research and Change the World. NOVEMBER 2020 Vol. 32 / No. 10 CRN At-A-Glance CRA Releases ‘2020 Quadrennial Papers’ Focused on Illuminating Computing In This Issue Research Challenges and Opportunities for the Next Four Years 2 CRA Releases ‘2020 Quadrennial Papers’ Focused on Illuminating Computing Research Challenges The Computing Research Association (CRA) recently released the first of more than and Opportunities for the Next Four Years a dozen planned white papers produced through its subcommittees, exploring 3 CCC Quadrennial Papers: Core Computer Science areas and issues around computing research with the potential to address national priorities over the next four years. Called Quadrennial Papers, the white papers 5 CCC Quadrennial Papers: Broad Computer Science attempt to portray a broad picture of computing research detailing potential 7 2020 Quadrennial Papers: Socio-Technical research directions, challenges, and recommendations for policymakers and the Computing and Diversity & Education computing research community. The release of the 2020 Quadrennial Papers covers 9 Assured Autonomy Workshop Report Released five thematic areas: Core Computer Science, Broad Computing, Socio-Technical 11 CCC/NAE Workshop Report – The Role of Computing, Artificial Intelligence, and Diversity & Education. Robotics in Infectious Disease Crises See page 2 for full article. 12 CRA-E Graduate Fellows Program Accepting Nominations Are You Working on the Taulbee Survey? 13 CRA-E Releases Report on Best Practices for Scaling Undergraduate CS Research Opportunities The CRA Taulbee Survey is in progress. 14 CRA-E Spotlights Outstanding Undergraduate The deadline for the salary section is November 24.
    [Show full text]
  • Curriculum Vitae
    Jelani Nelson Curriculum Vitae Deparment of EECS Soda Hall 633 [email protected] Berkeley, CA 94720-1776 https://people.eecs.berkeley.edu/~minilek/ Current Position UC Berkeley Berkeley, CA • Professor, Department of Electrical Engineering and Computer Science, July 2019{present Previous Positions Harvard University Cambridge, MA • John L. Loeb Associate Professor of Engineering and Applied Sciences, Jul 2017{June 2019 • Associate Professor of Computer Science, Jul 2017{June 2019 • Assistant Professor of Computer Science, Jul 2013{Jun 2017 Institute for Advanced Study Princeton, NJ • Member, Sep 2012{Jun 2013 • Mentor: Avi Wigderson Princeton University Princeton, NJ • Postdoctoral fellow, Center for Computational Intractibility, Jan 2012{Aug 2012 Mathematical Sciences Research Institute Berkeley, CA • Postdoctoral fellow, Program in Quantitative Geometry, Aug 2011{Dec 2011 • Mentor: Adam Klivans Education Massachusetts Institute of Technology Cambridge, MA • Ph.D. in Computer Science, June 2011. • Advisors: Professor Erik D. Demaine, Professor Piotr Indyk. • Thesis: Sketching and Streaming High-Dimensional Vectors. Massachusetts Institute of Technology Cambridge, MA • M.Eng. in Computer Science, June 2006. • Advisors: Dr. Bradley C. Kuszmaul, Professor Charles E. Leiserson. • Thesis: External-Memory Search Trees with Fast Insertions. Massachusetts Institute of Technology Cambridge, MA • S.B. in Computer Science, June 2005. • S.B. in Mathematics, June 2005. Honors • International Congress of Mathematicians, Invited Lecture, 2022. • Presidential Early Career Award for Scientists and Engineers (PECASE), 2017. • Alfred P. Sloan Research Fellow, 2017. • ONR Director of Research Early Career Grant, 2017{2022. • Harvard University Clark Fund Award, 2017. • ONR Young Investigator Award, 2015{2018. • NSF Early Career Development (CAREER) Award, 2014{2019. • George M. Sprowls Award, given for best doctoral theses in computer science at MIT, 2011.
    [Show full text]