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.

Faculty

Anil Aswani

Associate Professor

Alper Atamturk

Professor
Department Chair

Ken Goldberg

Professor

Paul Grigas

Assistant Professor

Dorit Hochbaum

Professor
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

Conic Optimization for Quadratic Regression Under Sparse Noise

Molybog, Ramtin Madani, and Javad Lavaei. Conic Optimization for Quadratic Regression Under Sparse Noise. Journal of Machine Learning Research. https://www.jmlr.org/papers/v21/18-881.html.

When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment

Feng Zhu, Zeyu Zheng. When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment. International Conference on Machine Learning (ICML) 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3637905.

On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification

Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, and Michael I. Jordan. On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. https://arxiv.org/abs/2006.12301.

Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training

Paul Grigas, Alfonso Lobos, Nathan Vermeersch. “Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training”. arXiv:1906.03580

 

Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery

Richard Zhang, Somayeh Sojoudi, and Javad Lavaei. Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery. Journal of Machine Learning Research, 2019. https://jmlr.org/papers/v20/19-020.html.

A comparative study of the leading machine learning techniques and two new optimization algorithms

Philip Baumann, Dorit S. Hochbaum and Yan T. Yang. A comparative study of the leading machine learning techniques and two new optimization algorithms. European Journal of Operational Research, Volume 272, Issue 3, 1 February 2019, Pages 1041-1057. Online version

Machine Learning and Data Mining with Combinatorial Optimization Algorithms

Dorit S. Hochbaum. Machine Learning and Data Mining with Combinatorial Optimization Algorithms. Tutorials in Operations Research, pages 109-129. INFORMS, 2018. Available online.

Sparse and Smooth Signal Estimation: Convexification of L0 Formulations

Alper Atamturk, Andres Gomez and Shaoning Han. “Sparse and Smooth Signal Estimation: Convexification of L0 Formulations”. Journal of Machine Learning Research. 

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

M. Zhou, Y. Fukuoka, Y. Mintz, K. Goldberg, P. Kaminsky, E. Flowers, and A. Aswani (2018), Evaluating machine learning-based automated personalized daily step goals delivered through a mobile phone app: Randomized controlled trial, JMIR Mhealth & Uhealth, vol. 6, no. 1: e28.

Spectral Algorithms for Computing Fair Support Vector Machines

M. Olfat and A. Aswani (2018), Spectral algorithms for computing fair support vector machines, In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS): 1933-1942.