Chris Atkeson

Chris Atkeson

Professor, Robotics Institute & Human-Computer Interaction Institute, School of Computer Science

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

Virginia Smith

Virginia Smith

Assistant Professor

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Dr. Smith's interests lie at the intersection of machine learning, optimization, and computer systems. A unifying theme of her research is to develop machine learning methods and theory that effectively leverage prior knowledge and account for practical constraints (e.g., hardware capabilities, network capacity, statistical structure). Specific topics include: distributed optimization, large-scale machine learning, resource-constrained learning, multi-task learning, transfer learning, and data augmentation.

Aaditya Ramdas

Aaditya Ramdas

Tenure Track Assistant Professor, Department of Statistics and Data Science, Dietrich College of Humanities and Social Sciences

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Aaditya's research spans theory, algorithms and applications in machine learning and statistical inference. One line of recent work focuses on the theme of reproducibility in science and technology (multiple hypothesis testing, selective inference) by designing new algorithms for controlling false discoveries in static and dynamic settings. Another line of work involves active sequential experimentation (interactive testing, multi-armed bandits), by designing algorithms that work in online or streaming data settings.

Robert Murphy

Robert Murphy

Department Head, Computational Biology/Ray and Stephanie Lane Professor, Computational Biology/Professor, Biological Sciences & Biomedical Engineering & Machine Learning, Mellon College of Science & School of Computer Science

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

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.

Eduard Hovy

Eduard Hovy

Research Professor, Language Technologies Institute, School of Computer Science

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.  

Leman Akoglu

Leman Akoglu

Assistant Professor, Information Systems, Heinz College of Information Systems and Public Policy

Dr. Akoglu’s research interests span a wide range of data mining and machine learning subjects with a focus on algorithmic and computational problems arising in graph mining, pattern discovery, social and information network analysis, and especially anomaly mining; outlier, fraud, and event detection. Akoglu directs the Data Analytics Techniques Algorithms (DATA) Lab at the Heinz College.

Drew Bagnell

Drew Bagnell

Associate Professor, Robotics Institute, School of Computer Science

Dr. Bagnell is interested in "closing the loop" on complex systems; that is, designing algorithms that allow systems to observe their own operation and improve performance. He is currently focused on applications of learning and decision making applied to mobile robotics and developing rich, structured models that are appropriate for both making and learning decisions.

Sivaraman Balakrishnan

Sivaraman Balakrishnan

Assistant Professor, Statistics, Dietrich College of Humanities and Social Sciences

Dr. Balakrishnan is broadly interested in statistical machine learning. Most recently he has been working on understanding algorithms for solving non-convex estimation problems. He is a member of the CMU Topological Statistics group.

Jaime Carbonell

Jaime Carbonell

Director, Language Technologies Institute/Newell University Professor, Language Technologies Institute & Computer Science, School of Computer Science

Dr. Carbonell's research interests span several areas of artificial intelligence and data mining, including: inductive machine learning methods, natural language processing, machine translation, information retrieval, fact extraction from the web and from free text, and automated summarization (where he invented MMR search-diversity technology).

Kathleen Carley

Kathleen Carley

Professor, Institute for Software Research, School of Computer Science

Dr. Carley's research is in the area of social and dynamic network science and agent-based modeling. She is currently developing and applying these methodologies to complex socio-technical issues such as fake news, social media information diffusion, health care, counter-terrorism and law enforcement. She is the developer of ORA and is interested in new scalable techniques for extracting, analyzing and visualizing high dimensional social networks.

George Chen

George Chen

Assistant Professor, Information Systems, Heinz College of Information Systems and Public Policy

Dr. Chen is an assistant professor of information systems at Heinz College and an affiliated faculty member of the Machine Learning Department. He works on machine learning for healthcare and for information systems in developing countries. In these applications, his work revolves around forecasting, such as predicting how long a patient will stay in a hospital, or when and where farmers in rural India should sell their crops. To produce forecasts, George typically uses nonparametric methods that, instead of specifying a model for the data in advance, let the data decide on what model to use, essentially through an election-like process where each data point casts a vote. Since these methods inform interventions that can be costly and affect people’s well-being, ensuring that predictions are reliable and interpretable is essential. To this end, in addition to developing nonparametric predictors, George also produces theory for when and why they work, and identifies forecast evidence that would be helpful to practitioners for decision making.

Yuejie Chi

Yuejie Chi

Associate Professor, Electrical & Computer Engineering, College of Engineering

Dr. Chi's research is motivated by the challenge of efficiently extracting information embedded in a large amount of data, as well as collecting data efficiently to gather actionable information. She is interested in the mathematics of data representation that take advantage of structures and geometry to minimize complexity and improve performance. Specific topics include mathematical and statistical signal processing, machine learning, large-scale optimization, sampling and information theory, with applications in sensing, imaging and big data.

Roger Dannenberg

Roger Dannenberg

Professor, Computer Science, School of Computer Science

Dr. Dannenberg's research is in computer music with an emphasis on sound synthesis, interactive performance systems, music representation and music understanding. He is currently working on artificial musicians that can "sit in" and perform with human musicians. He also is interested in the problems of constructing reliable, modular and intelligent interactive systems.

Fernando De la Torre

Fernando De la Torre

(ON-LEAVE) Associate Research Professor, Robotics Institute, School of Computer Science

Dr. De la Torre is interested in developing machine learning algorithms to recognize and characterize human behavior (e.g., activity detection, emotion recognition) from multimodal data.  In particular, he has interest in time series analysis and kernel methods to  discover structure on high-dimensional temporal data. He is also interested in visual learning (e.g., learning representations of images) and facial image analysis (e.g., facial feature tracking, face recognition, facial expression analysis).

Artur Dubrawski

Artur Dubrawski

Research Professor, Robotics Institute, School of Computer Science

Dr. Dubrawski's research interests are in autonomous systems that work, are useful and make economic sense, and in finding ways to effectively build and deploy them.

William Eddy

William Eddy

John C. Warner Professor Emeritus, Statistics, Dietrich College of Humanities and Social Sciences

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.

Fei Fang

Fei Fang

Assistant Professor, Institute for Software Research, School of Computer Science

Dr. Fang's research lies in the field of multi-agent systems, focusing on computational game theory and mechanism design integrated with machine learning methods. She has interests in learning from sparse and noisy data, multi-agent reinforcement learning, equilibrium analysis, spatio-temporal models, bi-level optimization and human behavior modeling, with applications to security, sustainability, and mobility domains.

Clark Glymour

Clark Glymour

Alumni University Professor, Philosophy, Dietrich College of Humanities and Social Sciences

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.

Chris Genovese

Chris Genovese

Professor/Department Head, Statistics, Dietrich College of Humanities and Social Sciences

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.

Seth Goldstein

Seth Goldstein

Associate Professor, Computer Science, School of Computer Science

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.

Max G'Sell

Max G'Sell

Assistant Professor, Statistics, Dietrich College of Humanities and Social Sciences

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.

Abhinav Gupta

Abhinav Gupta

Associate Professor, Robotics Institute, School of Computer Science

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.

David Held

David Held

Assistant Professor, Robotics Institute, School of Computer Science

Dr. Held's research lies at the intersection of robotics, machine learning, and computer vision. He is interested in developing learning methods for robotic perception, planning, and control for object manipulation tasks. David is currently working on object manipulation in cluttered environments, multi-task learning, and robot safety. David is also interested in learning for dynamic environments, such as perception in crowded urban settings or learning robotics tasks from human demonstrations.

Jiashun Jin

Jiashun Jin

Professor, Statistics, Dietrich College of Humanities and Social Sciences

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.

Charles Kemp

Charles Kemp

Associate Professor, Psychology, Dietrich College of Humanities and Social Sciences

Dr. Kemp works on statistical models of human learning and cognitive development. His interests include concept learning, common-sense reasoning, and other problems that are readily solved by people but difficult for machines to handle.

Seyoung Kim

Seyoung Kim

Assistant Professor, Computational Biology, School of Computer Science

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

Ken Koedinger

Hillman Professor, Human-Computer Interaction Institute, School of Computer Science

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.

Zico Kolter

Zico Kolter

Assistant Professor, Computer Science, School of Computer Science

Dr. Kolter's research focuses on computational approached to sustainable energy domains, and core challenges arising in machine learning, optimization, and control in these areas. On the application side, his interests range from improving the efficiency of generation, controlling power in smart grids, and analyzing energy consumption in homes and buildings. He focuses on techniques from machine learning, reinforcement learning, time series prediction, approximate inference, and convex optimization, amongst others to attack these problems.

Tai Lee

Tai Sing Lee

Professor, Computer Science & Center for the Neural Basis of Cognition, School of Computer Science & Dietrich College of Humanities and Social Sciences

Dr. Lee uses both statistical/machine learning techniques as well as physiological techniques to study neural processing in biological visual systems. Research topics include adaptive neural processing, neural representation of 3D scenes, information encoding and decoding in neurons, and hierarchical Bayesian inference in the cortex.

Zachary Lipton

Zachary Lipton

Assistant Professor, Tepper School of Business

Dr. Lipton primarily investigates deep learning, especially recurrent neural networks, deep reinforcement learning, and generative models. His work spans both core methodological challenges and the application of these techniques to problems in clinical healthcare. He is also interested in critical study of the societal impacts of machine learning. This includes the study of algorithmic bias, model interpretability, and the economic impacts of machine learning. 

Simon Lucey

Simon Lucey

Associate Research Professor, Robotics Institute, School of Computer Science

Dr. Lucey is motivated from a passion for discovering the “why?” behind “how?” with respect to core problems in Artificial Intelligence, Computer Vision and Machine Learning. He currently leads the CI2CV laboratory, at Carnegie Mellon University (appointments in RI and MLD) in Pittsburgh, PA, USA where we are attempting to make theoretical and technological advancements in these topics. Of particular focus in my group is: (i) 3D model based vision & learning (ii) mobile/embedded computer vision, (iii) the role of geometry & compressibilty in deep learning. 

Jian Ma

Jian Ma

Associate Professor, Computational Biology, School of Computer Science

Dr. Ma's research mainly focuses on computational genomics. His group develops algorithms, including machine learning techniques, to study the human genome organization and function as well as molecular mechanisms of cancer development and progression.

Gary Miller

Gary Miller

Professor, Computer Science, School of Computer Science

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.

Louis-Philippe  Morency

Louis-Philippe Morency

Assistant Professor, Language Technologies Institute, School of Computer Science

Dr. Morency is Assistant Professor in the Language Technology Institute at Carnegie Mellon University where he leads the Multimodal Communication and Machine Learning Laboratory (MultiComp Lab). He was formerly a research assistant professor in the Computer Sciences Department at University of Southern California and research scientist at USC Institute for Creative Technologies. Dr. Morency received his Ph.D. and Master degrees from MIT Computer Science and Artificial Intelligence Laboratory. His research focuses on building the computational foundations to enable computers with the abilities to analyze, recognize and predict subtle human communicative behaviors during social interactions. In particular, Dr. Morency was lead co-investigator for the multi-institution effort that created SimSensei and MultiSense, two technologies to automatically assess nonverbal behavior indicators of psychological distress. He is currently chair of the advisory committee for ACM International Conference on Multimodal Interaction and associate editor at IEEE Transactions on Affective Computing.

RESEARCH INTERESTS:

  • Human Communication Dynamics
    - Analyze, recognize and predict subtle human communicative behaviors during social interactions.
  • Multimodal Machine Learning
    - Probabilistic modeling of acoustic, visual and verbal modalities
    - Learning the temporal contingency between modalities
  • Health Behavior Informatics
    - Technologies to support clinical practice during diagnosis and treatment of mental health disorders

 

Benjamin Moseley

Benjamin Moseley

Assistant Professor, Operations Research, Tepper School of Business

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.

Daniel Neill

Daniel Neill

(ON-LEAVE) Associate Professor, Information Systems, Heinz College of Information Systems and Public Policy

Dr. Neill's research interests are in statistical machine learning, data mining, and pattern detection. He is particularly interested in developing new statistical and computational methods for the early, automatic detection of emerging public health threats ranging from avian influenza to bioterrorism. He is also investigating a variety of other large-scale anomaly detection problems related to medicine, public health, and homeland security.

Graham Neubig

Graham Neubig

Assistant Professor, Language Technology Institute, School of Computer Science

Dr. Neubig's research studies machine learning methods for natural language processing, with a focus on machine translation and semantics. This includes development of new algorithms for structured prediction, or unsupervised and semi-supervised learning of structure from unlabeled data. His models particularly focus on neural networks and deep learning, as well as Bayesian methods. He is also interested in methods to improve the efficiency of training and inference, and is a main developer of the DyNet neural network toolkit, which is designed to make it possible to easily and efficiently implement the sorts of complicated models that are used in these tasks.

Ariel Procaccia

Ariel Procaccia

Associate Professor, Computer Science, School of Computer Science

Dr. Procaccia's research focuses on topics at the intersection of computer science and economics. In particular, he is interested in (computational) fair division, (computational) social choice, and (computational) game theory. Machine learning connections include computing optimal game-theoretic strategies in the face of uncertainty, aggregating noisy votes, and designing manipulation-resistant learning algorithms.

Bhiksha Raj

Bhiksha Raj

Professor, Language Technologies Institute, School of Computer Science

Dr. Raj's research interests include computer audition, machine learning for signal processing, speech and natural language processing, privacy preserving signal processing and sparse estimation.

Alessandro Rinaldo

Alessandro Rinaldo

Associate Professor, Statistics, Dietrich College of Humanities and Social Sciences

Dr. Rinaldo's research focuses on the theoretical properties of high-dimensional statistical methods with a specific interest in modeling discrete data.

Tuomas Sandholm

Tuomas Sandholm

Professor, Computer Science, School of Computer Science

Dr. Sandholm's research interests are in active learning, stochastic optimization, electronic commerce; game theory; mechanism design; artificial intelligence; multiagent systems; auctions and exchanges; automated negotiation and contracting; voting; coalition formation; safe exchange; search, integer programming and combinatorial optimization; preference elicitation; normative models of bounded rationality; resource-bounded reasoning; privacy; multiagent reinforcement learning; game solving; equilibrium finding, kidney exchange; poker algorithms. 

Richard Scheines

Richard Scheines

Dean, Dietrich College/Professor, Philosophy, Dietrich College of Humanities & Social Sciences

Dr. Scheines' research interests focus on causal inference from statistical data. He is particularly interested in improving upon the reliability of regression in detecting causation, and in automatically constructing causal models that involve latent, or unobserved variables.

Russell Schwartz

Russell Schwartz

Professor, Biological Sciences, Mellon College of Science

Dr. Schwartz's research is broadly in the area of computational biology. One major focus is phylogenetics and genetic variation analysis, where he is looking at how one can make inferences of ancestry from many forms of genetic data and apply them to problems in medicine and basic research. Another focus is developing more realistic models of biological self-assembly processes and their interactions with the cellular environment. Schwartz is also involved in a variety of collaborative projects involving modeling different kinds of complex systems in biology.

Teddy Seidenfeld

Teddy Seidenfeld

Herbert A. Simon University Professor, Philosophy, Dietrich College of Humanities and Social Sciences

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.

Reid Simmons

Reid Simmons

Research Professor, Robotics Institute & Computer Science, School of Computer Science

Dr. Simmons's research focuses on the creation of mobile robot systems that are self-reliant enough for long-term, autonomous operation and that can readily adapt to new tasks and new environments. He is also interested in multi-agent coordination and human-robot social interaction.

Katia Sycara

Katia Sycara

Research Professor, Robotics Institute, School of Computer Science

Dr. Sycara's research interests lie in the area of artificial intelligence, in particular Case based Reasoning and machine learning in agents and multiagent systems, including both machine agents and humans.

Jeremy Weiss

Jeremy Weiss

Assistant Professor, Health Informatics, Heinz College of Information Systems and Public Policy

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.

Byron Yu

Byron Yu

Associate Professor, Electrical & Computer Engineering & Biomedical Engineering, College of Engineering

Dr. Yu's research is at the intersection of machine learning and neuroscience. He develops statistical methods for analyzing large-scale neural recordings, as well as algorithms used in biomedical devices that interface with large neural populations.

Kun Zhang

Kun Zhang

Assistant Professor, Philosophy, Dietrich College of Humanities and Social Sciences

Dr. Zhang's research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery from various kinds of data, and investigate learning problems including transfer learning and deep learning from a causal view. On the application side, Zhang is interested in neuroscience, computational finance, and climate analysis.