I am a Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science.
I am also a member of Theory of
Computation Group in
Computer Science and Artificial Intelligence Lab, Wireless@MIT, Big Data@CSAIL and Mifods.
High-dimensional computational geometry (including approximate nearest neighbor search), data stream algorithms, sparse recovery, compressive sensing, machine learning.
Conference PCs and journal editorial boards:
- Journal of the ACM (Area Editor)
- ICALP'17 (Program Committee Chair)
- SODA'15 (Program Committee Chair)
- SIAM Journal on Computing (2012-2014)
- IEEE Transactions on Signal Processing (2011-2013)
(22nd ACM-SIAM Symposium on Discrete Algorithms, San Francisco, CA)
- STOC'10 (42th ACM Symposium on Theory of Computing, Cambridge, MA)
- MADALGO 2007 Summer School on Data Stream Algorithms, Aarhus, Denmark.
- FOCS'07 (48th Annual Symposium on Foundations of Computer Science, Providence, RI)
- RANDOM'07, Princeton, NJ.
- ICALP'07 (34th International Colloquium on
Automata, Languages and Programming, Wroclaw, Poland)
- VLDB'07 (33rd International Conference on Very Large Data Bases, Vienna, Austria)
- STOC'06 (38th ACM Symposium on Theory of Computing, Seattle, WA)
- SIGMOD'06 (25th ACM SIGMOD International Conference on Management of Data, Chicago, IL)
(24th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Baltimore, MA)
(16th ACM-SIAM Symposium on Discrete Algorithms, Vancouver, Canada)
(10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA)
- Workshop on Discrete Metric Spaces and Their Algorithmic Applications , Princeton, August 20-23, 2003.
- Workshop on Approximate Nearest Neighbors Methods for Learning and Vision , at NIPS, Whistler, BC, Canada, December 2003.
(9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC)
- FOCS'02 (42nd Symposium on Foundations of Computer Science, Vancouver)
(17th ACM Symposium on Computational Geometry, Tufts U, Medford, MA)
Here is a list of some of my papers.
Surveys, tutorials and talks:
- Approximate nearest neighbor search in high dimensions, invited talk at the International Congress of Mathematicians, 2018. PDF version.
- Recent Developments in the Sparse Fourier Transform , at ICASSP, 2015.
- Recent Developments in the Sparse Fourier Transform , at GlobalSIP, 2013.
- Sketching via Hashing: from Heavy Hitters to Compressive Sensing to Sparse Fourier Transform: slides and writeup, at PODS, 2013.
- Lectures on high-dimensional near(est) neighbor search:
at MADALGO & CTIC Summer School on HIGH-DIMENSIONAL GEOMETRIC COMPUTING, 2011.
Tutorial on Sparse Recovery Using Sparse Matrices, given at MMDS'10 and China Theory Week, 2010.
A survey on Sparse Recovery Using Sparse Matrices (with A. Gilbert), Proceedings of IEEE, 2010.
A talk on Sparse Recovery Using Sparse Matrices, given in various forms in various places.
A tutorial on Streaming, Sketching and Sub-linear Space Algorithms, given at the 2009 Information Theory and Applications Workshop, San Diego, 2009.
A tutorial on Compressed Sensing (or Compressive Sampling or Linear Sketching), given at the
Workshop on Geometry and Algorithms, Princeton, 2008.
- A talk on Explicit constructions in high-dimensional geometry, given at 2007 von Neumann Symposium on Sparse Representation and High-Dimensional Geometry, Snowbird, 2007.
- An invited talk on Hashing, sketching and other randomized algorithms for high-dimensional data", given at EMNLP'07
- Two lectures on algorithms for finding nearest neighbors in low and high dimensions, Summer School on Algorithmic Data Analysis, Helsinki, 2007.
- A tutorial on Low-distortion embeddings and data structures, given at the Concentration Week on Metric Geometry and Geometric Embeddings of Discrete Metric Spaces, Texas A&M University, 2006.
- A talk on "Near-Optimal Hashing Algorithms for Approximate Near(est) Neighbor Problem", given at MMDS'06: Workshop on Algorithms for Modern Massive Data Sets, Stanford, 2006.
- Approximation Algorithms for Embedding Problems, given at
Fast Manifold Learning Workshop, College of William and Mary, 2006.
- An invited talk on "Streaming Algorithms for Geometric Problems" at
Sublinear Algorithms Workshop.
- A talk on Efficient Similarity Search in High Dimensions" that I gave at LEMS, Brown University, July 2004.
- An invited talk on "Embedded Stringology" at the Symposium on Combinatorial Pattern Matching (CPM 2004)".
- An invited talk on "Algorithmic Applications of Low-Distortion Embeddings" at the Snowbird Learning Workshop 2004 .
- Two surveys for the upcoming 2nd edition of "CRC Handbook of Discrete and Computational Geometry":
"Nearest Neighbors in High-dimensional Spaces" and "Low Distortion Embeddings of Finite Metric Spaces" (with Jiri Matousek).
- An invited talk on Approximate Nearest Neighbor (Beyond the l_p norms) at the
International Workshop on Designing Tomorrow's Category-Level 3D Object Recognition Systems.
An invited talk
on "Approximate Algorithms for High-Dimensional Geometric Problems" at the
DIMACS Workshop on Computational Geometry'02.
A tutorial at FOCS 2001 on "Algorithmic Aspects of Geometric Embeddings".
A short course on
"Nearest Neighbors and Other Problems in High Dimensional Computational Geometry" at AT&T during the summer of 1999.
Fall 2017: Sketching Algorithms for Big Data (jointly with Jelani Nelson at Harvard).
Fall 2014: Algorithms and Signal Processing (6.893).
Spring 2013: Sub-linear Algorithms (6.893).
Spring 2012: Geometric Computing (6.850).
Fall 2010: Sub-linear Algorithms (6.896).
Spring 2009: Streaming Etc. (at Rice University).
Fall 2007: Sketching, Streaming, and Sub-linear Space algorithms (6.895).
Spring 2007: Geometric Computation (6.850).
Fall 2006: Computational Biology, Too (6.895/6.085).
Fall 2005: Computational Biology (6.895/6.095).
Spring 2005: Geometric Computing (6.838) .
Fall 2003: Geometric Computing (6.838) .
Fall 2002: Algorithms for Massive Data Sets (6.897).
Fall 2001: Geometric Computation (6.838)
Algorithmic Aspects of Embeddings (6.978).
Sparse Fourier Transform From Theory to Practice
MIT Computer Science and Artificial Intelligence Lab
32 Vassar Street
Cambridge, Massachusetts 02139
Fax: (617) 258-8682
email: indyk ατ theory.lcs.mit.edu