Ziv Bar-Joseph

Associate Professor

Website: http://www.cs.cmu.edu/~zivbj/

Dr. Bar-Joseph's work focuses on the analysis of high throughput biological data. His group is using machine learning, statistical algorithms and signal processing techniques to address problems ranging from experimental design to data analysis, pattern recognition and systems biology. Specifically they have focused on integrating multiple biological data sources to infer dynamic regulatory networks and other interaction networks in the cell.

William Cohen

Research Professor

Website: http://www.cs.cmu.edu/~wcohen/

Dr. Cohen's research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 100 publications.

Christos Faloutsos

Professor

Website: http://www.cs.cmu.edu/~christos/

Dr. Faloutsos is working in databases. His research interests include data mining for streams and sensors; pattern discovery in large graphs, and indexing methods for multimedia and biological databases.

Stephen Fienberg

Professor

Website: http://www.stat.cmu.edu/cmu-stats/GSS/fienberg.html

Dr. Fienberg's principal research interests lie in the development of statistical methodology, especially for problems involving large scale data and categorical variables. He is currently working on a number of different aspects of data disclosure limitation (privacy protection) and the analysis of network data, using both traditional categorical data models and those involving forms of mixed membership.  He is also co-director of the Living Analytics Research Centre, a joint center between Carnegie Mellon University and Singapore Management University, bringing together i) machine learning, ii) statistics, iii) social and behavior science, and iv) management science, to enable people to live their lives in social network-centric worlds.

Zoubin Ghahramani

Associate Research Professor

Website: http://learning.eng.cam.ac.uk/zoubin/

Dr. Ghahramani is interested in all aspects of statistical machine learning, including graphical models, Bayesian statistics, non-parametric and kernel methods, semi-supervised learning, and decision making under uncertainty. He has worked in computational neuroscience focusing on human sensorimotor control, and on applications of machine learning to bioinformatics and information retrieval. He also has an appointment at the University of Cambridge.

Geoffrey Gordon

Associate Research Professor, Co-Director of ML Graduate Programs

Website: http://www.cs.cmu.edu/~ggordon/

Dr. Gordon is interested in multiagent learning and planning, statistical models of difficult data (examples include natural-language text and maps of a robot's surroundings), game theory, and computational learning theory.

Carlos Guestrin

Associate Professor, Currently on Leave of Absence

Website: http://www-2.cs.cmu.edu/~guestrin/

Dr. Guestrin's research focuses on developing efficient methods for control, inference and learning in large-scale problems, such as sensor networks, multiagent systems and relational data analysis. His research aims to combine techniques from artificial intelligence, machine learning, optimization theory and statistics to tackle highly uncertain complex problems.

Robert Kass

Professor, Co-Director of ML Graduate Programs

Website: http://www.stat.cmu.edu/~kass/

Dr. Kass has long-standing interests in the Bayesian approach to statistical inference, and has contributed to the development of Bayesian methods and their computational implementation. Over the past 10 years he has focused on statistical problems in neuroscience, especially in the analysis of signals coming from single neurons and from multiple neurons recorded simultaneously.

Ann Lee

Associate Professor

Website: http://www.stat.cmu.edu/~annlee/

Dr. Lee's research interests are pattern analysis and high-dimensional inference. She is currently developing statistical methods and models for analyzing and representing low-dimensional structures embedded in high dimensions with noise. Her work includes spectral data analysis and multi-scale methods with applications in population genetics, astrostatistics and vision.

Roy Maxion

Research Professor

Website: http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/maxion/www/index.html

Dr. Maxion's research is centered on learning decision models based on system and user behaviors.  His current focus is on keystroke forensics -- a type of biometric -- in which a user's typing is monitored, and a model of the typing style, or rhythm, is learned.  Based on this rhythm, users can be placed into different classes (like blood types), and users can be discriminated from one another, or even identified uniquely.  The results are used in two-factor and continuous authentication, authorship attribution, and tracking/tracing cyber-criminals across the Internet.

Tom Mitchell

Professor, Department Head

Website: http://www-2.cs.cmu.edu/~tom/

Dr. Mitchell works on new learning algorithms, such as methods for learning from labeled and unlabeled data. Much of his research is driven by applications of machine learning such as understanding natural language text, and analyzing fMRI brain image data to model human cognition.

Alan Montgomery

Associate Professor

Website: http://public.tepper.cmu.edu/facultydirectory/FacultyDirectoryProfile.aspx?id=100

Dr. Montgomery works on the application of data mining and statistical analysis to solve marketing problems. His research has focused on developing price and promotional strategies from purchase transaction data and the analysis of clickstream data to predict consumer behavior in online environments.

Robert Murphy

Professor

Website: http://murphylab.web.cmu.edu/

Dr. Murphy's principal interest is in computational biology, the application of computers to solve problems in biology. In particular, he is interested in the application of machine learning methods to biological images (especially microscope images depicting subcellular location), the application of active learning methods for analyzing and modeling complex biological phenoma, and the development of knowledge bases relating to protein properties from both text and images in online sources.

Roni Rosenfeld

Professor

Website: http://www.cs.cmu.edu/~roni/

Dr. Rosenfeld’s ML-related work focuses on modeling the evolution of viral epidemics.  He uses machine learning, large scale simulations, network analysis and stochastic process theory to try to answer research questions such as: 

            1.  How, and to what extent, can the evolution of infectious diseases like Influenza be predicted?

            2.  How, and to what extent, is the evolution of viral disease like Influenza affected by public health interventions such as vaccination, antiviral drug use, school closures and travel restrictions?

Dr. Rosenfeld models the spread of epidemics in the population as well as the evolution of the virus itself, such as changes in its virulence, pathogenicity, drug resistance, or antigenicity (immune escape).

Aarti Singh

Assistant Professor

Website: http://www.cs.cmu.edu/~aarti/

Dr. Singh's research interests lie at the intersection of signal processing and statistical machine learning. She is interested in developing techniques that can adaptively exploit the information structure inherent in complex and large-scale systems for efficient inference. The primary thrust of her research is on bridging the gap between theoretically optimal and practically useful methods, for diverse applications ranging from the Internet, wireless and sensor networks, to bioinformatics and brain imaging.

Larry Wasserman

Professor

Website: http://www.stat.cmu.edu/~larry/

Dr. Wasserman's research interests include nonparametric inference, multiple testing, asymptotic theory, causality, and applications to astrophysics and genetics.

Eric Xing

Associate Professor

Website: http://www-2.cs.cmu.edu/~epxing/

Dr. Xing's principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional and dynamic possible worlds; and for building quantitative models and predictive understandings of natural and built systems. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) application of statistical learning in social networks, data mining, computer vision.

Yiming Yang

Website: http://www-2.cs.cmu.edu/~yiming/

Yiming Yang is a professor in the Language Technologies Institute and the Machine Learning Department in the School of Computer Science at Carnegie Mellon University.  Her research has centered on statistical learning methods and their applications to a variety of challenging problems, including text categorization, utility (relevance and novelty) based information distillation from temporally ordered documents, learning to order interrelated prediction tasks, modeling non-deterministic user interactions in multi-session information filtering, personalized active learning for collaborative filtering, personalized email prioritization using social network analysis, cancer prediction based protein/gene expressions in micro-array data, and protein identification from tandem mass spectra.