Universities and research groups

AMERICA, US

MIT

Computer Science and Artificial Intelligence Lab

The Computer Science and Artificial Intelligence Laboratory, known as CSAIL ­– is the largest research laboratory at MIT and one of the world’s most important centers of information technology research.

CSAIL and its members have played a key role in the computer revolution. The Lab’s researchers have been key movers in developments like time sharing, massively parallel computers, public key encryption, the mass commercialization of robots, and much of the technology underlying the ARPANet, Internet and the World Wide Web.

CSAIL members (former and current) have launched more than 100 companies, including 3Com, Lotus Development Corporation, RSA Data Security, Akamai, iRobot, Meraki, ITA Software, and Vertica. The Lab is home to the World Wide Web Consortium (W3C), directed by Tim Berners Lee, inventor of the Web and a CSAIL member.

CSAIL research is focused on developing the architectures and infrastructures of tomorrow’s information technology, and on creating innovations that will yield long term improvements in how people live and work. Lab members conduct research in almost all aspects of computer science, including artificial intelligence, the theory of computation, systems, machine learning, computer graphics, as well as exploring revolutionary new computational methods for advancing healthcare, manufacturing, energy and human productivity.

www.csail.mit.edu

Stanford University

see.stanford.edu

New York City University, CILVR Lab and Center for Data Science

The CILVR Lab (Computational Intelligence, Learning, Vision, and Robotics) regroups three faculty members, research scientists, postdocs, and students working on AI, machine learning, and a wide variety of applications, notably computer perception, robotics, and health care.

cilvr.nyu.edu

cds.nyu.edu

Social Robotics Lab, Yale University

The members of our lab perform research over a diverse collection of topics. Though these projects approach social and developmental research from varied perspectives, they all share common themes. Robots provide an embodied, empirical testbed that allows for repeated validation. Robots also enable the use of social interactions as part of the modeled experimental environment, staying grounded in real world perceptions, and appropriately integrating perceptual, motor, and cognitive skills.

scazlab.yale.edu

Intelligent Interactive Systems Group at Harvard University

Intelligent Interactive Systems are fundamentally hard to design because they require intelligent technology that is well suited for people's abilities, limitations, and preferences; they also require entirely novel interactions that can give the user a predictable and reliable experience despite the fact that the underlying technology is inherently proactive, unpredictable, and occasionally wrong. Thus, design of successful intelligent interactive systems requires intimate knowledge of and ability to innovate in two very disparate areas: human computer interaction and artificial intelligence or machine learning.

Our projects span the full range from formal user studies to statistical machine learning. We have worked on developing new intelligent technologies to enable novel interactions (e.g., SUPPLE system) and on understanding the principles underlying how people interact with intelligent systems (e.g., the project on exploring the design space of adaptive user interfaces). Our Brain Computer Interface project aims at developing a new set of interactions for efficiently controlling complex applications, and we are also interested in building and studying complete applications. One particular area of inteterest is the ability based user interfaces an approach for adapting interactions to the individual abilities of people with impairments or of able bodied people in unusual situations.

iis.seas.harvard.edu

More to be added ...

AMERICA, CANADA

Machine Learning Lab, University of Toronto

Machine Learning @ UofT:

The Department of Computer Science at the University of Toronto has several faculty members working in the area of machine learning, neural networks, statistical pattern recognition, probabilistic planning, and adaptive systems. In addition, many faculty members inside and outside the department whose primary research interests are in other areas have specific research projects involving machine learning in some way.

learning.cs.toronto.edu

learning.cs.toronto.edu

Machine Learning Lab, University of Montreal

The LISA (machine learning lab) aims towards improving our understanding of the principles that give rise to powerful learning and to intelligence, which will be important to make significant progress on learning algorithms and artificial intelligence (AI). Acquiring the kind of complex knowledge necessary for AI requires some form of learning, with the ability to discover hidden relationships and statistical structure that may be highly complex, with many interacting factors of variations explaining the observed high dimensional data that sensors can provide. According to us this is the main challenge for machine learning and AI.

Like the brain, deep learning algorithms are based on several levels of representation and processing, creating several levels of levels of abstraction. Compared to learning algorithms based on shallower architectures, deep learners have the potential to efficiently represent highly complex functions and distributions. We explore various learning algorithms for deep learning, based in particular on unsupervised pre training (e.g., various kinds of Boltzmann machines and auto encoders).

Unsupervised pre training allows to exploit very large quantities of mostly unlabeled examples (such as documents, images, and videos from the web). The learned representations capture the salient factors of variation (and invariances) implicitly present in the data, and can be exploited in the context of several supervised learning tasks (multi task learning, self taught learning, semi supervised learning).

lisa.iro.umontreal.ca

University of Sherbrooke

Intelligence artificielle

Trois équipes oeuvrent dans cet axe de recherche; d'autres projets sont conduits par des chercheurs agissant à titre individuel.

L'équipe de recherche dans le domaine des systèmes tutoriels intelligents ASTUS (Apprentissage par Système Tutoriel de l'Université de Sherbrooke) travaille autour des thèmes suivants: représentation des connaissances, modélisation de l'utilisateur, interactions humain machine, psychologie de l'éducation et sciences cognitives.

L'équipe de recherche dans le domaine du forage de données, Prospectus (Prospection de données à l'Université de Sherbrooke), travaille autour des thèmes suivants: prospection des données, prospection et modélisation des connaissances, reconnaissance de formes, segmentation et classification, méthodes d'intelligence artificielle non symboliques, réseaux de neurones et réseaux bayésiens, détection de structures et comportements latents.

L'équipe de recherche dans le domaine de la planification en intelligence artificielle, PLANIART, travaille autour de thèmes suivant : planification de trajectoires, planification de comportements et reconnaissance de plans dans les jeux vidéo et en robotique mobile. La planification permet de décider quoi faire (décomposition des buts), comment le faire (allocation des ressources) et quand le faire (ordonnancement).

www.usherbrooke.ca

Centre de recherche sur les environnements intelligents

Le Centre de Recherche sur les Environnements Intelligents (CREI) comprend 13 membres réguliers, 11 membres associés et plus d'une soixantaine d'étudiants gradués. Le CREI fédère 7 laboratoires dont les intérêts de recherche portent sur l'imagerie numérique, l’intelligence artificielle, la modélisation validation et l’intelligence ambiante. Les chercheurs du CREI collaborent depuis des années, développant des applications en lien avec les environnements intelligents.

www.usherbrooke.ca

Machine Learning Research Group, University of Laval

Selected Papers

2014

Luc Bégin, Pascal Germain, François Laviolette and Jean Francis Roy. PAC Bayesian Theory for Transductive Learning. International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. [ pdf, supplementary, abstract | Poster | Source code ]

2013

Sébastien Giguère, François Laviolette, Mario Marchand, Denise Tremblay, Sylvain Moineau, Éric Biron and Jacques Corbeil. Improved design and screening of high bioactivity peptides for drug discovery. Under Review. [ pdf | Source Code ]

Sébastien Giguère, Alexandre Drouin, Alexandre Lacoste, Mario Marchand, Jacques Corbeil, François Laviolette. MHC NP: Predicting Peptides Naturally Processed by the MHC. Journal of Immunological Methods, 2013, vol. 400, p. 30 36. [ pdf ]

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. A PAC Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. In ICML 2013. [ bib | pdf | Source Code ]

Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla. Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. In ICML 2013. [ bib | pdf ]

Maxime Latulippe, Alexandre Drouin, Philippe Giguere, and François Laviolette. Accelerated Robust Point Cloud Registration in Natural Environments through Positive and Unlabeled Learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2013) 2013. [ pdf ]

Sébastien Giguère, Mario Marchand, François Laviolette, Jacques Corbeil, and Alexandre Drouin. Learning a Peptide Protein Binding Affinity Predictor with Kernel Ridge Regression. BMC Bioinformatics, 2013, vol. 14, no 1, p. 82. [ bib | pdf ]

graal.ift.ulaval.ca

Artificial Intelligence Research Groups, University of British Columbia

Research Groups

Computer Vision and Robotics: This is one of the most influential vision and robotics groups in the world. It is this group that created RoboCup and the celebrated SIFT features. The students in this group have won most of the AAAI Semantic Robot Challenges. The group has four active faculty: David Lowe, Jim Little, Alan Mackworth and Bob Woodham.

Empirical Algorithmics: Led by Holger Hoos and Kevin Leyton Brown, this research group studies the empirical behaviour of algorithms and develops automated methods for improving algorithmic performance. Work by the empirical algorithmics group at UBC/CS has lead to substantial improvements in the state of the art in solving a wide range of prominent problems, including SAT, AI Planning and Mixed Integer Programming, and won numerous awards.

Game Theory and Decision Theory: With Kevin Leyton Brown in the lead, this group has made significant contributions to algorithmic game theory, multiagent systems and mechanism design. David Poole also contributes to this group with his work on decision processes and planning. The research problems attacked by this group are therefore of great importance to e commerce, auctions and advertising.

Intelligent User Interfaces: With Cristina Conati and Giuseppe Carenini this group's goal is to investigate principles and techniques for preference modeling and elicitation, interactive decision making, user adaptive information visualization and visual interfaces for text analysis.

Knowledge Representation and Reasoning: David Poole leads this group with his foundational work on probabilistic first order logic and semantic science. This work on logical and probabilistic reasoning has been of profound and broad impact in the field of artificial intelligence (AI). Holger Hoos is also an important member of this group with his work on satisfiability (SAT) and planning, which has won numerous awards and competitions.

Machine Learning: With the guidance of Nando de Freitas and Kevin Murphy, this group's vision is to advance the frontier of knowledge in Bayesian inference, Monte Carlo algorithms, probabilistic graphical models, neural computation, personalization, mining web scale datasets, prediction and optimal decision making.

Natural Language Processing: Under the leadership of Giuseppe Carenini and Raymond Ng (Data Management and Mining Lab) this group's vision is to further our understanding of abstactive summarization, mining conversations and evaluative text, natural language generation.

www.cs.ubc.ca

More to come …

EUROPE, UK

University College London

The Centre for Computational Statistics and Machine Learning (CSML) spans three departments at University College London, Computer Science, Statistical Science, and the Gatsby Computational Neuroscience Unit.

The Centre will pioneer an emerging field that brings together statistics, the recent extensive advances in theoretically well founded machine learning, and links with a broad range of application areas drawn from across the college, including neuroscience, astrophysics, biological sciences, complexity science, etc. There is a deliberate intention to maintain and cultivate a plurality of approaches within the centre including Bayesian, frequentist, online, statistical, etc.

www.csml.ucl.ac.uk

Oxford University

The Machine Learning Research Group is a sub group within Information Engineering (Robotics Research Group) in the Department of Engineering Science of the University of Oxford.

We are interested in probabilistic reasoning applied to problems in science, engineering and computing. We use the tools of statistical, and in particular Bayesian, inference to deal rationally with uncertainty and information in a number of domains including astronomy, biology, finance, image & signal processing and multi agent systems, as well as researching the theory of Bayesian modelling and inference.

www.robots.ox.ac.uk

Imperial College

The Data Science Institute at Imperial College is being established to conduct research on the foundations of data science by developing advanced theory, technology and systems that will contribute to the state of the art in data science and big data, and support data driven research at Imperial and beyond. The Institute will empower Imperial and its partners to collaborate in the pursuit of world class data driven innovation.

www3.imperial.ac.uk

The University of Edinburgh, Institute for Adaptive and Neural Computation

www.anc.ed.ac.uk

More to be added ...

EUROPE, FRANCE

Sierra Team, Ecole Normale Superieure, CNRS, INRIA

SIERRA is based in the Laboratoire d'Informatique de l'École Normale Superiéure (CNRS/ENS/INRIA UMR 8548) and is a joint research team between INRIA Rocquencourt, École Normale Supérieure de Paris and Centre National de la Recherche Scientifique.

We follow four main research directions:

Supervised learning:

This part of our research focuses on methods where, given a set of examples of input/output pairs, the goal is to predict the output for a new input, with research on kernel methods, calibration methods, structured prediction, and multi task learning.

Unsupervised learning:

We focus here on methods where no output is given and the goal is to find structure of certain known types (e.g., discrete or low dimensional) in the data, with a focus on matrix factorization, statistical tests, dimension reduction, and semi supervised learning.

Parsimony:

The concept of parsimony is central to many areas of science. In the context of statistical machine learning, this takes the form of variable or feature selection. The team focuses primarily on structured sparsity, with theoretical and algorithmic contributions.

Optimization:

Optimization in all its forms is central to machine learning, as many of its theoretical frameworks are based at least in part on empirical risk minimization. The team focuses primarily on convex and bandit optimization.

www.di.ens.fr

ENS Ecole Normale Superieure

The Computer Science Department of ENS (DI ENS) is both a teaching department and a research laboratory affiliated with CNRS and INRIA (UMR 8548).

On the teaching side, the DI ENS trains students through its Pre doctoral program and the Masters program (MPRI).

On the research side, the research is structured into research groups. The DI ENS is member of the Fondation Sciences Mathématiques de Paris.

The Computer Services (SPI) and the Mathematics and Computer Science Library are common to the DI ENS and the Department of Mathematics and Applications (DMA).

Teams of the Computer Science Department at École normale supérieure

Antique, Static analysis by abstract interpretation (head: Xavier Rival)

Cascade, Cryptography (head: David Pointcheval)

Data, Signal Processing and Classification (head: Stéphane Mallat)

Dyogene, Dynamics of Geometric Networks (head: Marc Lelarge)

Parkas, Parallelism of Synchronous Kahn Networks (head: Marc Pouzet)

Sierra, Machine Learning (head: Francis Bach)

Talgo, Theory, Algorithms, topoLogy, Graphs, and Optimization (head: Claire Mathieu)

Willow, Artificial Vision (head: Jean Ponce)

www.di.ens.fr

More to be added ...

EUROPE, GERMANY

Max Planck Institute for Intelligent Systems, Tübingen site

Intelligent systems can optimise their structure and properties in order to successfully function within a complex, partially changing environment. Three sub areas, perception, learning and action, can be differentiated here. The scientists at the Max Planck Institute for Intelligent Systems are carrying out basic research and development of intelligent systems in all three sub areas. Research expertise in the areas of computer science, material science and biology is brought together in one Institute, at two different sites. Machine learning, image recognition, robotics and biological systems will be investigated in Tübingen, while so called learning material systems, micro and nanorobitics, as well as self organisation will be explored in Stuttgart. Although the focus is on basic research, the Institute has a high potential for practical applications in, among other areas, robotics, medical technology, and innovative technologies based on new materials.

www.mpg.de

More to be added ...

EUROPE, SWITZERLAND

EPFL Ecole Polytechnique Federale de Lausanne

Artificial Intelligence & Machine Learning

The modern world is full of artificial, abstract environments that challenge our natural intelligence. The goal of our research is to develop Artificial Intelligence that gives people the capability to master these challenges, ranging from formal methods for automated reasoning to interaction techniques that stimulate truthful elicitation of preferences and opinions. Another aspect is characterizing human intelligence and cognitive science, with applications in human computer interaction and computer animation.

Machine Learning aims to automate the statistical analysis of large complex datasets by adaptive computing. A core strategy to meet growing demands of science and applications, it provides a data driven basis for automated decision making and probabilistic reasoning. Machine learning applications at EPFL range from natural language and image processing to scientific imaging as well as computational neuroscience.

ic.epfl.ch

EUROPE, POLAND

University of Warsaw, Dept. of Mathematics, Informatics and Mechanics

Algorithms group

Our research

The research of our group focuses on several branches of modern algorithmics and the underlying fields of discrete mathematics. The latter include combinatorics on words and on ordered sets, graph theory, formal languages, computational geometry, information theory, foundation of cryptography. The research on algorithms covers parallel and distributed algorithms, large scale algorithms, approximation and randomized algorithms, fixed parameter and exponential time algorithms, dynamic algorithms, radio algorithms, multi party computations, and cryptographic protocols.

zaa.mimuw.edu.pl

ASIA, INDIA

Indian Institute of Science

Machine Learning and Learning Theory Group

Our research group focuses on the design and analysis of machine learning algorithms, and on understanding the mathematical and statistical properties of solutions to machine learning problems.

Members of the group have strong backgrounds in several areas including probability, linear algebra, convex analysis, optimization, spectral graph theory, and others, enabling us to explore problems from a variety of different viewpoints. Our emphasis is on developing a strong fundamental understanding of various problems of current interest in machine learning and statistical learning theory.

Some of our current research directions include designing and analyzing algorithms for problems such as ranking and various types of structured prediction tasks, understanding statistical consistency properties for such problems, exploring new issues in machine learning such as those related to privacy, and selected applications of machine learning in computational biology and medicine.

drona.csa.iisc.ernet.in

Indian Institute of Technology of Kanpur

www.google.com

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ASIA, RUSSIA

Moscow State University

www.msu.ru

ASIA, CHINA

Peking University

School of Electronics Engineering and Computer Science

We have built strong cooperation with many famous academic organizations, e.g., University of California at Berkeley, University of California at Los Angeles, Stanford University, University of Illinois at Urbana Champaign, Oxford University, University of Edinburgh, Paris High Division, University of Tokyo, Waseda University.

These cooperation cover most of our research directions: from electronic communication, optical communication, to quantum communication; from computer hardware, software, to network; from micro electromechanical system to nano techniques; from machine perception to machine intelligence.

Center for Information Science

Main Research Areas

鈻� Machine Vision Image processing, image and video compression, pattern recognition and machine learning, biometrics, 3d visual informational processing.

鈻� Machine Audition Computational auditory models, speech signal processing, spoken language processing, natural language processing, intelligent human machine interaction.

鈻� Intelligent Information Systems Computational intelligence, multimedia resource organization and management, data mining and content oriented massive information integration, analysis, processing and service.

鈻� Physiology and Psychology for Machine Perception Electro physiology, psychophysics and neurophysiology of vision and audition, theories and methods of hearing rehabilitation.

www.cis.pku.edu.cn

eecs.pku.edu.cn

Institute of Computational Linguistics

Main Research Areas

鈻� Comprehensive Language Knowledge Databases, including large scale word level information database for the Chinese language.

鈻� Corpus based NLP, including large scale corpus processing and statistical models and theories.

鈻� Domain Knowledge Construction, including computational terminology and term database construction.

鈻� Multilingual Semantic Lexicons, focusing on the study of a Chinese concept dictionary.

鈻� Computer aided Translation, focusing on translation methods for technical documents.

鈻� Information Retrieval, Extraction and Summarization, including various levels of docu ment processing such as document retrieval, topic extraction, summarization, and question answering.

eecs.pku.edu.cn

eecs.pku.edu.cn

PKU Real course online

www.grids.cn

Beijing University of Technology

Beijing Key Lab of Multimedia and Intelligent Software Technology

Artificial Intelligence and Knowledge Engineering

The research fields in this direction include fundamental research of Knowledge Science and Knowledge Engineering, research and application of Data Mining and Machine Learning, and Knowledge Based Computer Aided Animation Generation. In those fields, the laboratory has performed 8 programs from National Natural Science Foundation (including 1 subprogram of major research program of National Natural Science Foundation), 1 program from Key Programs in the National Science & Technology Pillar Program, 5 programs from 863 High Tech Programs, 3 programs from Beijing Natural Science Foundation, and won the second prize Advanced Science & Technology Award of Beijing twice.

bjut.edu.cn

University of Science and Technology of China, USTC

en.wikipedia.org

Nanjing University

Lamda Group

LAMDA is affiliated with the National Key Laboratory for Novel Software Technology and the Department of Computer Science & Technology, Nanjing University, China. It locates at Computer Science and Technology Building in the Xianlin campus of Nanjing University, mainly in Rm910. The Founding Director of LAMDA is Prof. Zhi Hua Zhou.

"LAMDA" means "Learning And Mining from DatA". The main research interests of LAMDA include machine learning, data mining, pattern recognition, information retrieval, evolutionary computation, neural computation, and some other related areas. Currently our research mainly involves: ensemble learning, semi supervised and active learning, multi instance and multi label learning, cost sensitive and class imbalance learning, metric learning, dimensionality reduction and feature selection, structure learning and clustering, theoretical foundations of evolutionary computation, improving comprehensibility, content based image retrieval, web search and mining, face recognition, computer aided medical diagnosis, bioinformatics, etc.

lamda.nju.edu.cn

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