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Fast removal of general multipath for time-of-flight sensors.
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Labs: ATL Israel
This project aims to understand and characterize user behavior in online social networks. Specifically, we posit, analyze, and validate different models of formation and evolution of opinions in social networks. We characterize our models both in terms of stability and goodness in terms of an approximation ratio w.r.t. to a social optimum (e.g., Price of Anarchy, Price of Stability etc).
Site under construction
We work on questions motivated by machine learning, in particular from the theoretical and computational perspectives. Our goals are to mathematically understand the effectiveness of existing learning algorithms and to design new learning algorithms. We combine expertise from diverse fields such as algorithms and complexity, statistics, and convex geometry.
MSR has a strong group of scientists working on algorithm design, analysis, and experimental evaluation, as well as researchers in related areas (e.g., coding theory), but no formal algorithms group. The Virtual Algorithms Center (VIRAL) brings these individuals together. The goals of the center is to enhance collaboration between algorithms researchers and the rest of MSR, provide internal consulting, and give an external view of the algorithms research at MSR.
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Tabular is a Excel add-in that brings the power of model based machine learning to data enthusiasts. It allows the user to write a simple model that explains their data and perform Bayesian inference. Tabular is built on top of Infer.NET.
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Labs: Cambridge
Face In The Crowd examines the social impact of crowdsourcing platforms—cloud-based computational systems that allow the outsourcing of work through open requests—and how they might shape the future of work.
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This is an umbrella project for our activity in machine learning with exploration-exploitation tradeoff. Most of us are at MSR-NYC.
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Labs: New York | Redmond
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Labs: Redmond
Project details
Labs: Redmond
It has become increasingly difficult to process information from the vast amount of data collected and stored in different computer systems all around us. The main challenge is how to design distributed and scalable algorithms and systems to extract useful information from this large quantity of data. The goal of this project is to address various questions related to distributed and scalable processing of big data.
Project details
Labs: Cambridge
The performance of the elliptic curve method (ECM) for integer factorizationplays an important role in the security assessment of RSA-based protocols as a cofactorization tool inside the number field sieve. This webpage gives addition-subtracting chains to optimize Edwards ECM in terms of both performance and memory requirements. See for more details the "ECM at Work" paper.
Rainbow Tangle is a game on a sequence of permutations. The purpose of the game is to create as straight routes of the permutation particles as possible.
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Labs: Redmond
We work on novel sampling techniques to design faster algorithms for solving problems on large data. Algorithmic applications of our methods include dimensionality reduction, clustering, and linear algebraic problems on large matrices.
Project details
Labs: India
In the last 25 years, Elliptic Curve Cryptography (ECC) has become a mainstream primitive for cryptographic protocols and applications. ECC has been standardized for use in key exchange and digital signatures. This project focuses on efficient generation of parameters and implementation of ECC and pairing-based crypto primitives, across architectures and platforms.
Project details
Labs: Redmond
Project details
Labs: Redmond
We introduced a game-theoretic framework for crowdsourcing systems.
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Labs: Cambridge
Counterdog is an automated theorem prover for a counterfactual meta-logic on propositional Datalog. The prover is complete for the logic, and can prove (or disprove) counterfactual statements such as: "if 'p' is false in a Datalog program, but would be true if it contained 'a:-b', then 'b' is true in the program.". Counterdog is useful for reasoning about Datalog-based trust management languages. The theory and implementation were developed by Moritz Y. Becker and Nik Sultana.
Project details
Labs: Cambridge
In recent years, computational challenges have become more and more important to infer biologically relevant information from the vast amount of experimental data available to systems biologists.
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What is the link between how problems are logically described in a formal language, and how they are solved by algorithms?
Project details
Labs: Cambridge
Time Series Foundation (TSF) is a .NET toolset for exploring new algorithms in time series analysis and forecasting. TSF is based on state space model methodology that includes all types of exponential smoothing, some autoregressive algorithms, and innovative algorithms for event detection and calendar event impact prediction. TSF implements an Excel interop layer and offers an Excel add-in that exposes a large subset of TSF functionality through the Excel ribbon UI.
Project details
Labs: Redmond
Automata is a .NET tool kit that provides facilities for manipulating and analyzing regular expressions, symbolic finite automata, and symbolic finite transducers. It supports automata and transducers where input and output alphabets can be fully symbolic. Constraints over the alphabets can be analyzed using Satisfiability Modulo Theory (SMT) solvers. The tool kit provides a particular extension that uses the Microsoft SMT solver Z3.
Project details
Labs: Redmond
It is often the case that mutually distrustful parties need to perform a joint computation but cannot afford to reveal their inputs to each other. This can occur, for example, during auctions, data mining, voting, negotiations and business analytics. Secure multi-party computation (MPC) allows a set of parties, each with a private input, to securely and jointly perform any computation over their inputs.
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Labs: Redmond
Scalable Joins is a new implementation of the Joins concurrency library designed to scale on parallel hardware.
Project details
Labs: Cambridge
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