Encyclopedia of Algorithms
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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. -
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 -
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. -
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. -
Biography Five Related and Significant Publications
GUY BLELLOCH Professor Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 [email protected], http://www.cs.cmu.edu/~guyb Biography Guy E. Blelloch received his B.S. and B.A. from Swarthmore College in 1983, and his M.S. and PhD from MIT in 1986, and 1988, respectively. Since then he has been on the faculty at Carnegie Mellon University, where he is now an Associate Professor. During the academic year 1997-1998 he was a visiting professor at U.C. Berkeley. He held the Finmeccanica Faculty Chair from 1991–1995 and received an NSF Young Investigator Award in 1992. He has been program chair for the ACM Symposium on Parallel Algorithms and Architectures, program co-chair for the IEEE International Parallel Processing Symposium, is on the editorial board of JACM, and has served on a dozen or so program committees. His research interests are in parallel programming languages and parallel algorithms, and in the interaction of the two. He has developed the NESL programming language under an ARPA contract and an NSF NYI award. His work on parallel algorithms includes work on sorting, computational geometry, and several pointer-based algorithms, including algorithms for list-ranking, set-operations, and graph connectivity. Five Related and Significant Publications 1. Guy Blelloch, Jonathan Hardwick, Gary L. Miller, and Dafna Talmor. Design and Implementation of a Practical Parallel Delaunay Algorithm. Algorithmica, 24(3/4), pp. 243–269, 1999. 2. Guy Blelloch, Phil Gibbons and Yossi Matias. Efficient Scheduling for Languages with Fine-Grained Parallelism. Journal of the ACM, 46(2), pp. -
Paging with Connections: FIFO Strikes Again
Paging with connections: FIFO strikes again Leah Epstein a Yanir Kleiman b Jiˇr´ıSgall c,1 Rob van Stee d,2 aDepartment of Mathematics, University of Haifa, 31905 Haifa, Israel. bThe Academic College of Tel-Aviv Yaffo, Antokolski 4 61161 Tel-Aviv, Israel. cMathematical Institute, AS CR, Zitn´a25,ˇ CZ-11567 Praha 1, Czech Republic, and Dept. of Applied Mathematics, Faculty of Mathematics and Physics, Charles University, Praha. dDepartment of Computer Science, University of Karlsruhe, P.O. Box 6980, D-76128 Karlsruhe, Germany. Tel. nr. +49-721-6086781, Fax +49-721-6083088 Abstract We continue the study of the integrated document and connection caching problem. We focus on the case where the connection cache has a size of one and show that this problem is not equivalent to standard paging, even if there are only two locations for the pages, by giving the first lower bound that is strictly higher than k for this problem. We then give the first upper bound below the trivial value of 2k for this problem. Our upper bound is k + 4ℓ where ℓ is the number of locations where the requested pages in a phase come from. This algorithm groups pages by location. In each phase, it evicts all pages from one location before moving on to the next location. In contrast, we show that the lru algorithm is not better than 2k-competitive. We also examine the resource augmented model and show that the plain fifo algorithm is optimal for the case h = 2 and all k ≥ 2, where h is the size of the offline document cache. -
Raluca Budiu
Raluca Budiu 5559 Bartlett St., Apt. B3 Pittsburgh, PA 15217 (412) 422-7214 or (412) 216-4862 [email protected] http://www.cs.cmu.edu/˜ralucav Research Interests Computational models of language, natural language understanding, tutoring systems, educa- tional testing, metaphor comprehension, sentence and discourse processing, learning new word meanings, learning from instruction, knowledge formation Education 2001 Ph.D. in Computer Science. Carnegie Mellon University, Pittsburgh, PA. Supervisor: Prof. John R. Anderson. Ph.D. Thesis: The Role of Background Knowledge in Sentence Processing. This disser- tation describes a computational theory of sentence processing at the semantic level. It offers a unique explanation to behavioral data from a number of domains such as metaphor comprehension, sentence memory and semantic illusions and is validated by comparing the results of its simulation with those produced by human subjects on similar tasks. 1999 M.S. in Computer Science. Carnegie Mellon University, Pittsburgh, PA. 1996 M.S. in Signal Processing. University “Politehnica” of Bucharest, Romania. 1995 B.S. in Computer Science. University “Politehnica” of Bucharest, Romania. Research Experience 2001–2003 Carnegie Mellon University, Pittsburgh, PA. Postdoctoral research associate for Prof. John R. Anderson. Developed INP, a com- putational model of language processing that integrated syntactic and semantic processing. Designed, conducted, and analyzed experiments involving human subjects. 1 Raluca Budiu 2 1996–2001 Carnegie Mellon University, Pittsburgh, PA. Graduate research assistant for Prof. John R. Anderson. Collected behavioral data and developed computational models for learning metaphors and artificial words in context, knowledge transfer in artificial language acquisition, Moses illusion, metaphor comprehension, sentence memory, semantic language processing within the ACT-R framework. -
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 -
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 ........................................................................................................................................................ -
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. -
New Paths from Splay to Dynamic Optimality∗
New Paths from Splay to Dynamic Optimality∗ Caleb C. Levyy1 and Robert E. Tarjanz2,3 1Baskin School of Engineering, UC Santa Cruz 2Department of Computer Science, Princeton University 3Intertrust Technologies arXiv:1907.06310v1 [cs.DS] 15 Jul 2019 ∗Research at Princeton University partially supported by an innovation research grant from Princeton and a gift from Microsoft. [email protected] [email protected] 1 Abstract Consider the task of performing a sequence of searches in a binary search tree. After each search, an algorithm is allowed to arbitrarily restructure the tree, at a cost proportional to the amount of restructuring performed. The cost of an execution is the sum of the time spent searching and the time spent optimizing those searches with restructuring operations. This notion was introduced by Sleator and Tarjan in (JACM, 1985), along with an algorithm and a conjecture. The algorithm, Splay, is an elegant procedure for performing adjust- ments while moving searched items to the top of the tree. The conjecture, called dynamic optimality, is that the cost of splaying is always within a constant factor of the optimal algorithm for performing searches. The con- jecture stands to this day. In this work, we attempt to lay the foundations for a proof of the dynamic optimality conjecture. Central to our methods are simulation embeddings and approximate monotonicity. A simulation embedding maps each execution to a list of keys that induces a target algorithm to simulate the execution. Approximately monotone algorithms are those whose cost does not increase by more than a constant factor when keys are removed from the list. -
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).