Exploring game theory, market equilibriums, efficient algorithms
We are working in emerging fields within theoretical computer science, including privacy in statistical databases and quantum computing. We also investigate algorithms and mathematics for the Internet, including web search, social-network analysis, spam fighting, and web security.
Classical areas of interest include complexity, cryptography, algebraic computation, random structures, and spectral methods for data analysis. We strive to develop scalable algorithms for learning and data mining, cryptographic algorithms, graph algorithms, synchronization algorithms, networking algorithms, and sampling algorithms. We also look at problems at the intersection of systems, networking, and algorithms research: We study the algorithmic foundations of the systems that drive today’s computing—such as cloud computing, data centers, large-scale distributed systems, and mobile computing—and we apply our expertise in practice to advance the state of the art in applied algorithm design and deliver highly efficient, scalable, robust solutions.
We also conduct research in several theoretical areas in mathematics and physics that are beyond the traditional scope of computer science but are closely connected. Researchers actively work on combinatorics, geometry and topology, probability theory, statistical physics, number theory, and functional analysis.
Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore, Quantum Nearest-neighbor Algorithms for Machine Learning, in Quantum Information and Computation, vol. 15, no. 3&4, pp. 0318-0358, Rinton Press, March 2015.
Margus Veanes and Nikolaj Bjørner, Symbolic Tree Automata, in Information Processing Letters, vol. 115, no. 3, pp. 418-424, Elsevier, March 2015.
Alex Bocharov, Martin Roetteler, and Krysta M. Svore, Efficient Synthesis of Universal Repeat-Until-Success Circuits, in Physical Review Letters, vol. 114, no. 080502, American Physical Society, 27 February 2015.
Dan Alistarh, Jennifer Iglesias, and Milan Vojnovic, Min-Max Hypergraph Partitioning, no. MSR-TR-2015-15, February 2015.
Moignard V., Woodhouse S., Haghverdi L., Lilly J., Tanaka Y., Wilkinson A., Buettner F., Nishikawa S.I., Piterman N., Kouskoff V., Theis F., Fisher J., and Gottgens B., Decoding the Transcriptional Program for Blood Development from Whole Tissue Single Cell Gene Expression Measurements, in Nature Biotechnology, Nature Publishing Group, February 2015.
- Sparse Reflections Analysis
- Opinion Dynamics in Social Networks
- Microsoft 3-Handpose dataset
- Learning Theory
- Virtual Algorithms Center (VIRAL)
- Face In The Crowd
- Explore-Exploit Learning
- Crowdsourcing and Human Computation
- Recurrent Neural Networks for Language Processing
- Big Data Analytics
- ECM at Work
- Rainbow Tangle
- Sampling and Spectral Techniques for Faster Algorithms
- Elliptic Curve Cryptography
- LIQUi|>: Language-Integrated Quantum Operations
- Crowdsourcing Contests
- Computational Biology
- Declarative Problem Solving
- Sparse Reflections Analysis: Sensor Data from ECCV 2014 Paper
- Jacobian Coordinates on Genus 2 Curves
- Microsoft Research Automata Tool Kit