The machine learning community at Columbia University spans multiple departments, schools, and institutes. We have interest and expertise in a broad range of machine learning topics and related areas.
Seminar
Since Fall 2025, there is a (roughly) bi-weekly Machine Learning and AI Seminar. See the seminar webpage for more information.
Mailing list
We maintain a low-volume mailing list to announce talks and events going on at Columbia that are relevant to machine learning. To subscribe, send an email to .
Faculty
- Anish Agarwal (IEOR)
- causal inference, econometrics, online/reinforcement learning, high-dimensional statistics
- Shipra Agrawal (IEOR)
- multi-armed bandits, reinforcement learning, online learning and optimization, sequential decision making
- Mohammed AlQuraishi (SysBio)
- computational biology, biomolecular modeling, geometric deep learning, physically-informed learning, generative models, dynamical systems
- James Anderson (EE)
- optimal control, multi-agent reinforcement learning, distributed learning
- Alexandr Andoni (CS)
- algorithms for massive data, nearest neighbor search, high-dimensional computational geometry, learning theory
- Marco Avella (Stats)
- Eric Balkanski (IEOR)
- combinatorial optimization, data-driven algorithm design, mechanism design, game theory
- Elias Bareinboim (CS)
- causal inference, decision-making, explainability
- David Blei (CS, Stats)
- probabilistic machine learning and applications, approximate Bayesian inference, causal inference
- Adam Block (CS)
- learning theory, reinforcement learning, language models
- Adam Cannon (CS)
- statistical learning theory, supervised learning
- Shih-Fu Chang (CS, EE)
- computer vision, multimedia knowledge extraction, meta learning and few shot learning, large-scale visual search
- John Cunningham (Stats)
- deep generative models, approximate inference, state space models, gaussian processes, computational neuroscience
- Bianca Dumitrascu (Stats, IICD)
- representation learning, interpretability, mechanistic and spatio-temporal modeling,applications to single cell biology for regenerative biology and development
- Micah Goldblum (EE)
- applied deep learning, AI safety, natural language processing, automated data science, mathematical foundations of deep learning
- Vineet Goyal (IEOR)
- online learning and optimization and sequential decision making, multi-armed bandits
- Yuqi Gu (Stats)
- latent variable models, graphical models, deep generative models, causal discovery and causal representation learning, psychometrics, high-dimensional statistics, spectral and tensor methods
- Christopher Harshaw (Stats)
- causal inference, randomized experiments, algorithm design
- John Hewitt (CS)
- natural language processing, language modeling, representation learning, interpretability, AI alignment
- Daniel Hsu (CS)
- algorithmic statistics, learning theory
- Garud Iyengar (IEOR)
- Shalmali Joshi (DBMI)
- machine learning, deep learning, observational causal inference, reinforcement learning, algorithmic fairness for health and medicine
- Samory Kpotufe (Stats)
- statistical learning theory, nonparametrics and high-dimensional statistics, minimally supervised learning
- Christian Kroer (IEOR)
- online learning and optimization, game theory, sequential decision making
- Yunzhu Li (CS)
- robotics, embodied intelligence, machine learning, computer vision
- Hod Lipson (MechE)
- robotics, automated scientific discovery
- Arian Maleki (Stats)
- Vishal Misra (CS)
- causal inference, Bayesian inference, and large language models
- Hongseok Namkoong (DRO)
- statistical learning, stochastic optimization, reliable decision-making, and distributional robustness
- John Paisley (EE)
- Bayesian models and inference
- Liam Paninski (Stats)
- Toniann Pitassi (CS)
- algorithmic fairness, privacy, theory of machine learning
- Cynthia Rush (Stats)
- high-dimensional statistics, sparse learning, information theory, statistical signal and image processing, learning theory
- Daniel Russo (DRO)
- Ansaf Salleb-Aouissi (CS)
- Silvia Sellán (CS)
- geometry processing, 3D computer graphics
- Rocco Servedio (CS)
- computational learning theory
- Nakul Verma (CS)
- learning theory, metric learning, dimensionality reduction and embeddings, manifold learning, topological data analysis, fairness
- Carl Vondrick (CS)
- computer vision, unsupervised learning
- Kaizheng Wang (IEOR)
- statistical learning, stochastic optimization, data integration problems
- Chris Wiggins (APAM)
- computational biology, network data analysis, bandit problems, variational inference, statistics
- John Wright (EE)
- optimization, sparse and low-dimensional models, imaging
- Lily Xu (IEOR)
- online learning, reinforcement learning, decision-making under uncertainty, AI for social good, biodiversity and sustainability
- Assaf Zeevi (DRO)
- bandit problems, statistical learning theory, reinforcement learning, stopping problems and sequential analysis, model predictive control
- Richard Zemel (CS)
- representation learning, probabilistic and causal models, meta-learning and few-shot learning, algorithmic fairness
- Zhengbo Zou (CEEM)
- robotics, reinforcement learning