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.

## 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

- 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 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)
- 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

- 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

- 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)
- 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

- 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

## 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 .