Chris
Atkeson
Professor, Robotics Institute & Human-Computer Interaction Institute, School of Computer Science
Bio
Dr. Atkeson's research focuses on the application of machine learning to robotics and intelligent environments. he is interested in getting robots (particularly humanoid robots) to learn from their errors. Another goal is to build environments that learn to understand what people are doing, and learn how to help them more effectively
Alfred
Blumstein
J. Erik Jonsson University Professor Emeritus, Urban Systems & Operations Research, Heinz College of Information Systems and Public Policy
Bio
Dr. Blumstein's research over the past twenty years has covered many aspects of criminal justice phenomena and policy, including crime measurement, criminal careers, sentencing, deterrence and incapacitation, prison populations, flow through the system, demographic trends, juvenile violence and drug-enforcement policy. He is also director of the National Consortium on Violence Research (NCOVR), a multi-university initiative funded by the National Science Foundation and headquartered at the Heinz School.
William
Eddy
John C. Warner Professor Emeritus, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Eddy concentrates on statistical methods for analyzing images, particularly time series of images. His imaging research began with functional magnetic resonance imaging but has expanded to include cDNA microarrays, gel electrophoresis, positron emission tomography, and video.
Max
G'Sell
Assistant Professor, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. G'Sell is interested in the development of statistical methodology, particularly methods that include ideas from optimization and computer science, as well as applications of statistics to the sciences and to sensor or instrument data. Lately, he has been working on inference problems that arise in regularized regression, as well as the application of optimization to assessments of estimator sensitivity and robustness.
Chris
Genovese
Professor/Department Head, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Genovese's research focuses on high and infinite dimensional inference problems in the analysis of large or complex data sets. This includes function and manifold estimation, confidence set construction, and structured estimation. He works extensively on applications in neuroscience and cosmology.
Clark
Glymour
Alumni University Professor, Philosophy, Dietrich College of Humanities and Social Sciences
Bio
Dr. Glymour's current research applies previous work on causal Bayes nets and formal learning theory to a variety of topics. With collaborators at NASA Ames, he works on automated identification of mineral composition from spectra. With the Computational Systems Biology Group, he works on the possibilities and limitations of machine learning procedures for inferring gene regulation from measurements of messenger RNA concentrations. In collaboration with several psychologists he also works on mathematical aspects of the psychology of causal reasoning.His current work also concerns predictions of biosphere events (e.g. forest fires) from satellite measurements of spectra.
Seth
Goldstein
Associate Professor, Computer Science, School of Computer Science
Bio
Dr. Goldstein's research focuses on computing systems and nanotechnology. He believes that the fundamental challenge for computer science in the twenty-first century is how to effectively harness systems which contain billions of potentially faulty components. One of the projects he works on that addresses this issue is the Claytronics project, which is exploring the hardware and software necessary to realize programmable matter.
Joel
Greenhouse
Professor, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Greenhouse has had a long standing interest in the development and application of Bayesian methods for the design and analysis of studies in the biomedical and biobehavioral sciences, particularly clinical trials and meta-analysis. An area of continuing interest has been the use of robust Bayesian methods for sensitivity analysis.
Abhinav
Gupta
Associate Professor, Robotics Institute, School of Computer Science
Bio
Dr. Gupta’s research focuses on building systems that develop a deep understanding of the visual world from images and videos. Specifically, he is interested in exploiting big data for large-scale visual learning, visual data-mining, and learning common sense knowledge. He is also interested in exploring the link between language and vision.
Alex
Hauptmann
Research Professor, Language Technologies Institute, School of Computer Science
Bio
Dr. Hauptmann has done research in speech recognition, speech synthesis, speech interfaces and natural language processing. Dr. Hauptmann's research interests are to utilize large corpora of found data, or other sources of knowledge that are already exist to improve speech and natural language processing by exploiting advantages across different modalities.
Eduard
Hovy
Research Professor, Language Technologies Institute, School of Computer Science
Bio
Dr. Hovy has worked on many aspects of Natural Language Processing, and currently focuses on computational semantics for NLP. This includes not only semantic interpretation of text and numbers, but also collecting and structuring the background knowledge needed to support semantic processing, specifically text mining, information extraction, and ontology creation. A related focus is understanding and recognizing the interpersonal semantics inherent in human dialogue.
Jiashun
Jin
Professor, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Jin is interested in large-scale inference and massive-data analysis, where the data are usually very high-dimensional and one must estimate very large numbers of parameters or test very large numbers of hypotheses simultaneously. The setting is frequently found in many scientific areas, e.g. genomics, astronomy, functional Magnetic Resonance Imaging (fMRI), and image processing. Advances in large-scale inferences enable faster exactration of useful information in various scientific fieds and broaden the scope of theory and methodology in statistics.
Marcel
Just
Director, Center for Cognitive Brain Imaging/ D O Hebb Professor, Psychology, Dietrich College of Humanities and Social Sciences
Bio
Dr. Just's research uses brain imaging (fMRI) to examine how a network of brain areas activates during the performance of language comprehension, spatial thinking and problem-solving tasks. The data consist of a time series of the activation levels of about 20,000 brain voxels, sampled once every second. I work at the Center for Cognitive Brain Imaging. I have a long-standing collaboration with Tom Mitchell which applies machine-learning (pattern-based-classification) approaches to brain activation data in various language-related types of thinking.
Jay
Kadane
Leonard J. Savage Professor Emeritus, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Kadane's research interests include both foundations of statistical inference and applications. His foundational work (joint with Mark Schervish and Teddy Seidenfeld) centers on understanding the consequences of extending the usual countably additive version of probability to allow merely finitely additive probabilities as well, and on finding an adequate theory of optimal group decision-making under uncertainty. His current applied work touches on law, medicine, internet security, marketing, physics and phylogenetics.
Seyoung
Kim
Assistant Professor, Computational Biology, School of Computer Science
Bio
Dr. Kim's research interests are in machine learning, statistical genetics, and computational genomics. Given the high-dimensional nature of genome-scale data such as genome sequences, transcriptome, proteome, and epigenome, her work involves developing statistical machine learning techniques for discovering the genetic basis of diseases and disease-related biological processes with the ultimate goal of personalized medicine.
Ken
Koedinger
Hillman Professor, Human-Computer Interaction Institute, School of Computer Science
Bio
Dr. Koedinger is interested in the use and advancement of machine learning as a tool for modeling human learning, for creating simulated students, for accelerating development of intelligent tutoring systems, and data mining of student interactions in e-learning environments.
Ramayya
Krishnan
Professor & Dean, Engineering & Public Policy, Heinz College of Information Systems and Public Policy
Bio
Dr. Krishnan's research interests are in large Scale Network Analysis, Social Media and Analytics, Optimization.
Gary
Miller
Professor, Computer Science, School of Computer Science
Bio
Dr. Miller's research interests are in sequential and parallel algorithm design. Of particular interest are problems that arise in scientific computation and image processing. He has been working on three classes of problems; Mesh Generation, Spectral Graph and Image Processing. His work is both more theoretical yet more practical since we require two important properties of our algorithms: they should be both be fast and have strong guarantees of quality, size, and speed.
Benjamin
Moseley
Assistant Professor, Operations Research, Tepper School of Business
Bio
Dr. Moseley's interests are broadly focused on algorithm design. In this area, he works on the theoretical foundations of machine learning, scalable machine learning and decision making under uncertainty. His work focuses on discovering provable worst-case guarantees on machine learning algorithms as well as using theoretical models to guide the development of fast scalable algorithms. He is working on clustering, active learning, active search and scalable deep learning.
Alessandro
Rinaldo
Associate Professor, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Rinaldo's research focuses on the theoretical properties of high-dimensional statistical methods with a specific interest in modeling discrete data.
Mark
Schervish
Professor, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Schervish has interests in Statistical theory, methodology, and application. Some of his interests include foundations of statistical reasoning, Bayesian nonparametrics, modeling contaminant concentrations in drinking water, and path planning for robots to search for landmines.
Teddy
Seidenfeld
Herbert A. Simon University Professor, Philosophy, Dietrich College of Humanities and Social Sciences
Bio
Dr. Seidenfeld works at the interface between philosophy and statistics, often concerning myself with problems that involve multiple decision makers. For example, in collaboration with Mark Schervish and Jay Kadane (of CMU's Stats. Dept), we have relaxed the norms of Bayesian theory to permit a unified standard, both for individuals acting as separate decision makers and collectively, in forming a cooperative "group" agent. By contrast, this is an impossibility for strict Bayesian theory.
Dave
Touretzky
Research Professor, Computer Science & Center for the Neural Basis of Cognition, School of Computer Science
Bio
Dr. Touretzky studies the representation of space and direction in the rodent brain, by constructing computational models guided by behavioral and neurophysiological data. He also investigates cognitive models of animal learning and their implementation on mobile robots.
Isabella
Verdinelli
Professor in Residence, Statistics, Dietrich College of Humanities and Social Sciences
Bio
Dr. Verdinelli's research interests include Bayesian experimental design; Monte Carlo Marcov Chains simulations; hypothesis testing procedures with the Bayes factor; nonparametric inference, both Bayesian and frequentist; experimetal design for medical and engineering applications; multiple testing theory (FDR); clusters and filaments identification for complex data sets from random fields; manifolds theory and estimation; rate of convergence of manifold estimators; and identification of minimax rate of convergence.
Alex
Waibel
Professor, Language Technologies Institute, School of Computer Science
Bio
Dr. Waibel is a Professor of Computer Science at Carnegie Mellon University, Pittsburgh and at the University of Karlsruhe (Germany). He directs the Interactive Systems Laboratories at both Universities with research emphasis in speech recognition, handwriting recognition, language processing, speech translation, machine learning and multimodal and multimedia interfaces.
Jeremy
Weiss
Assistant Professor, Health Informatics, Heinz College of Information Systems and Public Policy
Bio
Jeremy Weiss develops machine learning algorithms for predictive modeling in medicine, focusing on temporal, relational, and causal learning. In addition to a computer science PhD he holds a medical degree and applies both to machine learning applications in clinical practice.
Joel
Welling
Senior Scientific Specialist, Pittsburgh Supercomputing Center
Bio
Dr. Welling's research interests include parallel computing and large scale scientific computing, and in particular statistical analysis of large computational datasets. Much of his work in this area has dealt with functional brain imaging data and astrophysical simulations.