Machine Learning at Columbia

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