Carnegie Mellon University

The ordering on this page is randomized (as opposed to ordering alphabetically).
Read more about biases due to alphabetical ordering.

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

(ON-LEAVE) 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.

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.

Vincent Conitzer

Vincent Conitzer

Incoming Professor, Computer Science, School of Computer Science

Director of the Foundations of Cooperative AI Lab (FOCAL), whose goal is to create foundations of game theory appropriate for advanced, autonomous AI agents – with a focus on achieving cooperation. This also requires understanding the ways in which AI agents can be fundamentally different from human agents, which leads to certain specific, technical philosophical questions. More broadly, I am interested in many topics at the intersection of CS and economic theory, as well as topics in AI, ethics, and society.

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.

Artur Dubrawski

Artur Dubrawski

Alumni Research Professor of Computer Science, 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.

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.

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.

Zico Kolter

Zico Kolter

Associate 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.

Jian Ma

Jian Ma

Ray and Stephanie Lane 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.

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

 

Robert Murphy

Robert Murphy

Lane Professor of Computational Biology, 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.

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.

Aditi Raghunathan

Aditi Raghunathan

Incoming Assistant Professor, Computer Science, School of Computer Science

I work broadly in machine learning and my goal is to make machine learning more reliable and robust. My work spans both theory and practice, and leverages tools and concepts from statistics, convex optimization, and algorithms to improve the robustness of modern systems based on deep learning. I am currently excited about leveraging massive self-supervision, human interaction, multimodal learning and reinforcement learning to build reliable ML systems. 

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.

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 and Head, Computational Biology Department Professor, Department of Biological Sciences Carnegie Mellon University

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.

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.

Michael Tarr

Michael Tarr

Professor & Department Head, Psychology, Dietrich College of Humanities and Social Sciences

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Dr. Tarr's research interests include the neural representation of visual information in the human cortex and computationally-inspired models of visual object and face processing, representation, and recognition in biological systems. Much of this work relies on advancing the designs and analyses used in functional neuroimaging.

Conrad Tucker

Conrad Tucker

Arthur Hamerschlag Career Development Professor, Mechanical Engineering

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Dr. Tucker focuses on the design and optimization of systems through the acquisition, integration, and mining of large scale, disparate data. Previously, he was a faculty member at The Pennsylvania State University and directed the Design Analysis Technology Advancement (D.A.T.A) Laboratory.

Valerie Ventura

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Steven Wu

Steven Wu

Assistant Professor, Institute for Software Research

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Dr. Wu works on algorithms and machine learning. His recent work focuses on (1) how to make machine learning better aligned with societal values, especially privacy and fairness, and (2) how social and economic interactions influence machine learning. His studies these questions using methods and models from machine learning, statistics, optimization, differential privacy, game theory, and mechanism design.

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.

Jun-Yan  Zhu

Jun-Yan Zhu

Assistant Professor, Robotics Institute, School of Computer Science

Address
5000 Forbes Avenue
Pittsburgh, PA 15213

Dr. Zhu's research interests lie at the intersection of computer vision, computer graphics, and machine learning. His ultimate goal is to build intelligent machines, capable of recreating our visual world. Jun-Yan studies machine learning models that can learn to automatically synthesize visual content and virtual worlds by understanding and modeling millions of images and videos. Along the way, the learned models will also understand why our world looks the way it does, discovering knowledge useful for recognition and reasoning tasks.