IEOR - Designing a More Efficient World

Machine Learning and Data Science Research

Data plays a critical role in all areas of IEOR, from theoretical developments in optimization and stochastics to applications in automation, logistics, health care, energy, finance, and other areas. Much of the recent interest in data science and machine learning has been spurred by the growing ability to apply vast computational power to large scale datasets in nearly every application domain. Faculty and students in the UC Berkeley IEOR department are engaged in cutting edge and interdisciplinary research in ML/DS, including topics like developing scalable and memory-efficient learning algorithms, integrating prediction and optimization models, sparse learning models, addressing fairness concerns, reinforcement learning and control, clustering and learning with network data, as well as applications of ML/DS to various domains.


Anil Aswani

Associate Professor

Alper Atamturk

Department Chair

Ken Goldberg


Paul Grigas

Assistant Professor

Dorit Hochbaum

Head Graduate Advisor

Javad Lavaei

Associate Professor

Barna Saha

Assistant Professor

Zeyu Zheng

Assistant Professor

Laurent El Ghaoui

Joint Faculty, EECS

Michael Jordan

Joint Faculty, EECS

Selected Publications

DART: Noise Injection for Robust Imitation Learning

Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg. “DART: Noise Injection for Robust Imitation Learning“. 1st Conference on Robot Learning (CoRL). Mt. View, CA. Nov 2017. Proceedings of Machine Learning Research volume 78

Smart “Predict, then Optimize”

Adam N. Elmachtoub, Paul Grigas. Smart “Predict, then Optimize”. Management Science, forthcoming.

Sparse Computation for Large-Scale Data Mining

D.S. Hochbaum and P. Baumann. Sparse Computation for Large-Scale Data Mining. IEEE Transactions on Big Data, Vol 2, Issue 2, 151-174, 2016.

A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives

Robert M. Freund, Paul Grigas, Rahul Mazumder. “A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives”.