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.
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.
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.
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.
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.
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.
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.
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.
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.
Website: http://www.cs.cmu.edu/~bapoczos/Dr. Póczos' research interests lie in the theoretical questions of statistics and their applications to machine learning, computer vision,astronomy, and bioinformatics.
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).
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.
Website: http://alex.smola.org/My primary research interest covers the following four areas:
- Scalability of algorithms. This means pushing algorithms to internet scale, distributing them on many (faulty) machines, showing convergence, and modifying models to fit these requirements. For instance, randomized techniques are quite promising in this context. In other words, I'm interested in big data.
- Kernels methods are quite an effective means of making linear methods nonlinear and nonparametric. My research interests include support vector Machines, gaussian processes, and conditional random fields. Kernels are very useful also for the representation of distributions, that is two-sample tests, independence tests and many applications to unsupervised learning.
- Statistical modeling, primarily with Bayesian Nonparametrics is a great way of addressing many modeling problems. Quite often, the techniques overlap with kernel methods and scalability in rather delightful ways.
Website: http://www.stat.cmu.edu/~ryantibs/Dr. Tibshirani's research is mainly focused on statistical methods that exploit sparsity and sparse structures, from both the applied and theoretical perspectives. His research interests also include more "traditional" statistics topics (like degrees of freedom and cross-validation), algorithms and optimization, and the statistics of sports and games.
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.
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.
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.