Carnegie Mellon University
The ordering on this page is randomized (as opposed to ordering alphabetically).
Read more about biases due to alphabetical ordering.
1 Photo of Maria-Florina Balcan, Machine Learning, CMU

Maria-Florina Balcan

Professor, Computer Science & Machine Learning, School of Computer Science

Research Interests

  • Foundations of Machine Learning
  • Data Driven Algorithm Design
  • Computational and Data-driven approaches in game theory and economics
  • Interactive Learning
  • Lifelong Learning

Dr. Balcan's main research interests are in machine learning and theoretical computer science.
Current research focus includes:
• Developing foundations and principled, practical algorithms for important modern learning paradigms. These include interactive learning, distributed learning, multi-task learning, and never ending learning. Her research formalizes and explicitly addresses all constraints and important challenges of these new settings, including statistical efficiency, computational efficiency, noise tolerance, limited supervision or interaction, privacy, low communication, and incentives.
• Machine learning and game theoretic tools for analyzing the overall behavior of complex systems in which multiple agents with limited information are adapting their behavior based on past experience, both in social and engineered systems contexts.
• Analysis of algorithms beyond the worst case and more generally identifying interesting and realistic models of computation that provide a better alternative to traditional worst-case models in a broad range of optimization problems (including problems of extracting hidden information from data).

2 Photo of Ziv Bar-Joseph, Machine Learning, CMU

Ziv Bar-Joseph

Professor, Computational Biology & Machine Learning, School of Computer Science

Research Interests

  • Computational Biology
  • Graphical Models
  • Time Series Analysis
  • Single Cell

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.

2 Photo of Tianqi Chen, Machine Learning, CMU

Tianqi Chen

(Joining Fall 2020) Assistant Professor, Machine Learning & Computer Science, School of Computer Science

Research Interests

  • Machine Learning Systems
  • Large-scale machine learning
  • Knowledge Transfer

Dr. Chen’s interests lie in the intersection of machine learning and systems. To Tianqi, the real excitement of this area comes from what it can be enabled when bringing advanced learning techniques and systems together. On that end, he is also pushing the direction on deep learning, knowledge transfer, and lifelong learning.
Chen created XGBoost, MXNet, and TVM. He earned his Ph.D. in Computer Science and Engineering at the University of Washington.

3 Photo of William Cohen, Machine Learning, CMU

William Cohen

(ON LEAVE) Professor, Machine Learning & Language Technologies Institute, School of Computer Science

Research Interests

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.

4 Photo of Christos Faloutsos, Machine Learning, CMU

Christos Faloutsos

Professor, Computer Science, School of Computer Science

Research Interests

  • Data Mining
  • Social Networks
  • Anomaly Detection

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.

5 Photo of Katerina Fragkiadaki, Machine Learning, CMU

Katerina Fragkiadaki

Assistant Professor/Director of ML Master's Programs, Machine Learning, School of Computer Science

Research Interests

  • Active Vision
  • Computer Vision

Dr. Fragkiadaki's interests are in learning from videos, unsupervised learning, and learning policies of visual processing. She is currently looking into learning algorithms with weak supervision from videos for extracting geometry and semantics, learning a parsing of a video scene that allows prediction of its future evolution. She likes compositional architectures of visual processing as a means towards better generalization and general-purpose platforms to support multiple skill formation and action in the world.

6 Photo of Rayid Ghani, Machine Learning, CMU

Rayid Ghani

Distinguished Career Professor, Machine Learning, School of Computer Science

Research Interests

  • Machine Learning for Social Good
  • Fairness
  • Bias
  • Equity
  • Interpretability
  • Explainable Machine Learning
  • Public Policy

Dr. Ghani's research interests lie at the intersection of machine learning, public policy, and social sciences. He is interested in solving large-scale and high impact social problems using data-driven and evidence-based methods.

7 Photo of Geoffrey Gordon, Machine Learning, CMU

Geoffrey Gordon

Professor, Machine Learning, School of Computer Science

Research Interests

  • Reinforcement Learning
  • Statistical Machine Learning
  • Optimization
  • Spectral Methods

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.

8 Photo of Matt Gormley, Machine Learning, CMU

Matt Gormley

Assistant Teaching Professor/Director of ML Undergraduate Programs, Machine Learning, School of Computer Science

Research Interests

  • Deep Learning for Structured Prediction
  • Graphical Models
  • NLP

Dr. Gormley's research focuses on machine learning for natural language processing. His interests include global optimization, learning under approximations, hybrids of graphical models and neural networks, and applications where supervised resources are scarce.

8 Photo of Hoda Heidari, Machine Learning, CMU

Hoda Heidari

Assistant Professor, Machine Learning, School of Computer Science

Research Interests

Dr. Heidari's research interests lie in the Societal Aspects of Artificial Intelligence and Machine Learning and Algorithmic Economics.

9 Photo of Robert Kass, Machine Learning, CMU

Robert Kass

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

Research Interests

  • ML in the Brain Sciences
  • Statistics in Neuroscience

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.

10 Photo of Ann Lee, Machine Learning, CMU

Ann Lee

(Associate Professor, Statistics & Machine, Dietrich College of Humanities and Social Sciences & School of Computer Science

Research Interests

  • Statistical Learning
  • Nonparametric Inference
  • Physical Sciences

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.

11 Photo of Yuanzhi Li

Yuanzhi Li

Assistant Professor, Machine Learning, School of Computer Science

Research Interests

Dr. Li's work primarily focuses on machine learning. His goal is to design efficient and provable algorithms for practical machine learning problems. He is also very interested in convex/non-convex optimization.

12 Photo of Zachary Lipton

Zachary Lipton

Assistant Professor of Business Technologies and Machine Learning

Research Interests

  • Machine Learning for Healthcare
  • Robustness under distribution shift
  • Ethics of Technology
  • Deep Learning

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.

13 Photo of Roy Maxion

Roy Maxion

Research Professor, Computer Science, School of Computer Science

Research Interests

  • Biometrics
  • Forensics
  • Keystroke Dynamics
  • Research Methods

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.

14 Photo of Tom Mitchell, Machine Learning, CMU

Tom Mitchell

Founders University Professor/Director of ML PhD Programs, Computer Science & Machine Learning, School of Computer Science

Research Interests

  • Conversational Machine Learning
  • Deep Learning Models of Human Neural Activity

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.

15 Photo of Alan Montgomery

Alan Montgomery

Professor, Marketing, Tepper School of Business

Research Interests

  • Bayesian Methods
  • Gaussian Processes
  • Priors with Domain Knowledge

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.

16 Photo of Barnabás Póczos

Barnabás Póczos

(ON LEAVE) Associate Professor Machine Learning, School of Computer Science

Research Interests

  • Machine Learning in Scientific Applications
  • Optimization
  • Statistical Machine Learning

Dr. Póczos's research interests lie in the theoretical questions of statistics and their applications to machine learning, computer vision, astronomy, and bioinformatics.

17 Photo of Aaditya Ramdas

Aaditya Ramdas

Assistant Professor, Statistics & Machine Learning, Dietrich College of Humanities and Social Sciences & School of Computer Science

Research Interests

  • Multiple Hypothesis Testing
  • Sequential Estimation
  • Interactive Statistics
  • Nonparametric Inference

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.

18 Photo of Pradeep Ravikumar

Pradeep Ravikumar

Associate Professor, Machine Learning, School of Computer Science

Research Interests

  • Explainable, Robust, Graceful AI
  • Statisical Machine Learning
  • Learning Theory

Dr. Ravikumar's research interests are in the area of statistical machine learning broadly. The core problem here has a "comptastical" imperative that combines the statistical imperative of inferring reliable conclusions from limited observations or data, with the computational imperative of doing so with limited computation. His recent research has been on the foundations of such statistical machine learning, with particular emphasis on graphical models, optimization and high-dimensional statistical inference.

19 Photo of Andrej Risteski

Andrej Risteski

Assistant Professor, Machine Learning, School of Computer Science

Research Interests

  • Machine Learning Theory
  • Theory of Unsupervised Learning
  • Theory of Deep Learning

Dr. Risteski's work focuses on the intersection of machine learning and theoretical computer science. The broad goal of my research is theoretically understanding statistical and algorithmic phenomena and problems arising in modern machine learning.

20 Photo of Roni Rosenfeld, Machine Learning, CMU

Roni Rosenfeld

Department Head, Machine Learning Department/Professor, Machine Learning, Language Technologies Institute, Computer Science, Computational Biology, School of Computer Science

Research Interests

  • Forecasting Epidemics

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).

21 Photo of Ruslan Salakhutdinov, Machine Learning, CMU

Ruslan Salakhutdinov

UPMC Professor, Machine Learning, School of Computer Science

Research Interests

Dr. Salakhutdinov's primary interests lie in artificial intelligence, machine learning, deep learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data.

22 Photo of Jeff Schneider

Jeff Schneider

Research Professor, Robotics Institute, School of Computer Science

Research Interests

  • Bayesian Optimization
  • Reinforcement Learning
  • Self-driving Cars

Dr. Schneider's research interests are in machine learning, reinforcement learning, optimization, and decision making. He has applied his methods to business applications ranging from process control, to production scheduling and inventory management, to long range strategic planning.

23 Photo of Nihar Shah, Machine Learning, CMU

Nihar Shah

Assistant Professor, Machine Learning & Computer Science, School of Computer Science

Research Interests

  • Fairness
  • Game Theory
  • Learning Theory

Dr. Shah's research interests lie in the areas of statistical learning, game theory, and information theory. His current focus is on problems in crowdsourcing and learning from people.

24 Photo of Cosma Shalizi, Machine Learning, CMU

Cosma Shalizi

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

Research Interests

  • Causal Inference
  • Network Analysis
  • Time Series

Dr. Shalizi has research interests in Nonparametric model discovery of state-space/hidden Markov models and stochastic automata; dynamical-systems analysis of learning processes; applications of information theory, large deviations and ergodic theory in statistical inference; complex network models; heavy-tailed distributions.

25 Photo of Aarti Singh

Aarti Singh

Associate Professor, Machine Learning, School of Computer Science

Research Interests

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.

26 Photo of Virginia Smith

Virginia Smith

Assistant Professor, Machine Learning, School of Computer Science/ECE: Electrical & Computer Engineering

Research Interests

  • Large Scale Machine Learning
  • Optimization
  • Robust Machine Learning
  • Privacy-preserving ML

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.

27 Photo of Ameet Talwalkar

Ameet Talwalkar

Assistant Professor, Machine Learning, School of Computer Science

Research Interests

  • Explainable ML
  • Auto ML
  • Meta Learning
  • Fairness
  • Parallel and Distributed Machine Learning
  • Systems and Machine Learning

Dr. Talwalkar’s primary interests are in the field of statistical machine learning, including problems at the intersection of systems and learning, and applications in computational genomics. His current work is motivated by the goal of democratizing machine learning, with a focus on topics related to the scalability, automation, and interpretability of learning algorithms and systems.

28 Photo of Ryan Tibshirani, Machine Learning, CMU

Ryan Tibshirani

Associate Department Head for Faculty and Climate, Machine Learning/Associate Professor, Statistics & Machine Learning, Dietrich College of Humanities and Social Sciences & School of Computer Science

Research Interests

  • Convex Optimization
  • Forecasting Epidemics
  • High-dimensional Statistics
  • Nonparametric Statistics

Dr. Tibshirani's research interests lie broadly in statistics, machine learning, and optimization. More specifically, he is interested in high-dimensional statistics, post-selection inference, nonparametric estimation, convex optimization, and convex geometry. He likes to think about problems from different angles: applied, theoretical, and computational. He is also interested in developing methods for epidemiological forecasting, particularly flu forecasting.

29 Photo of Manuela Veloso

Manuela Veloso

(ON LEAVE) Herbert A. Simon University Professor, Computer Science & Machine Learning, School of Computer Science

Research Interests

  • AI Planning and Learning
  • Multiagent Systems

Dr. Veloso works in the field of artificial intelligence, including robotics and learning. Her long-term research goal is the effective construction of teams of intelligent physical agents where cognition, perception, and action are integrated to address planning, execution, and learning tasks.

30 Photo of Pat Virtue

Pat Virtue

Assistant Teaching Professor, School of Computer Science

Research Interests

  • Active Learning Teaching Methods
  • ML/AI Curriculum Development
  • AI Educational Outreach

Pat Virtue focuses on teaching techniques for artificial intelligence, machine learning, and computer science. His interests include active learning teaching methods, effective instruction for large classes, building inclusive learning environments, and AI/ML curriculum development.

31 Photo of Larry Wasserman

Larry Wasserman

UPMC Professor, Statistics & Machine Learning, Dietrich College of Humanities and Social Sciences & School of Computer Science

Research Interests

  • Nonparametric Inference
  • Statistical Learning

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

32 Photo of Leila Wehbe

Leila Wehbe

Assistant Professor, Machine Learning, School of Computer Science

Research Interests

  • Computational Cognitive Neuroscience
  • Natural Language Processing
  • ML for Science

Dr. Wehbe's research is focused on computational modeling of the brain representation of language and other high-level tasks. She uses machine learning and neuroimaging -- fMRI and MEG -- to study how the brain represents information during complex naturalistic tasks such as reading a book or holding a conversation. Her research is at the interface between natural language processing, machine learning and cognitive neuroscience.

33 Photo of Eric Xing

Eric Xing

Professor, Machine Learning & Language Technologies Institute & Computational Biology/Associate Head for Research, Machine Learning, School of Computer Science

Research Interests

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.

34 Photo of Yiming Yang

Yiming Yang

Professor, Language Technologies Institute & Machine Learning, School of Computer Science

Research Interests

  • Graph-based Learning
  • Deep Representation Learning/Architecture Search
  • Time Series

Dr. 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.