Students

Stanford PhD students: I expect to take on one or two PhD students in 2023. Send me an email or drop by my office hours if you're interested in working with me. Potential projects include
- low rank and structured optimization,
- learning faster optimizers for combinatorial problems
- causal, interpretable, and/or automated machine learning,
- elegant solutions to practical data challenges: missing values, outliers, distribution shift, expensive objective functions
- if you have your own project in mind, come tell me about it!

Stanford masters and undergraduate students: Get in touch if you've got a strong computational background, have taken or are taking one of my classes, are willing to work on research for at least ten hours a week for at least a year, and are interested in one of the problems above; and mention you've read this. Unfortunately I do not have funding for masters and undergraduate students.

Students at other universities: If you'd like to work with me, please apply to the MS&E or ICME PhD program at Stanford! I'm not able to reply individually to emails from prospective students, but I'm glad to chat once you're accepted.

Current Research Group

Stanford PhD students:

Zachary Frangella, MS&E. Randomized numerical linear algebra.
Mike Van Ness, PhD student in MS&E. Prediction with missing data.
Ali Teshnizi, MS&E. LLMs for optimization modeling.
Pratik Rathore, EE. Randomized algorithms for optimization.
Tomas Bosschieter, ICME. (Co-advised with Kilian Pohl.) Interpretable machine learning.
Ya-Chi Chu, Math. Large-scale interior point methods.
Naomi Sagan, EE (rotation). Signal processing for echolocation.
Wenzhi Gao, ICME (rotation). Optimization.

Stanford Masters students:

Weimu Lei, ICME. Physics-informed neural networks.
Yasu Yoshida, MS&E. LLMs for optimization modeling.
Jingruo Sun, MS&E. Stochastic proximal preconditioning.

Stanford undergraduate students:

Abbie Maemoto, CS. Algorithmic fairness.
Jayna Huang, MS&E. LLMs for optimization.

Alumni

(Parentheses below enclose first employer after degree, if known.)

Cornell PhD students:

Shipu Zhao, PhD student in Systems Engineering (Amazon). Automated analysis of algorithms.
Chengrun Yang, PhD student in ECE (Google Brain). Automated machine learning.
Yuxuan Zhao, PhD student in Statistics (Two Sigma). Imputing missing data.
Lijun Ding, PhD student in ORIE (UW). (Co-advised with Yudong Chen.) Large scale semidefinite programming: simplicity, conditioning, and an efficient algorithm.
Xiaojie Mao, PhD student in Statistics (Tsinghua). (Co-advised with Nathan Kallus.) Machine Learning Methods for Data-driven Decision Making: Contextual Optimization, Causal Inference, and Algorithmic Fairness.
Yiming Sun, PhD student in Statistics (Amazon). Co-advised with Sumanta Basu. High Dimensional Data Analysis with Dependency and Under Limited Memory.

Cornell undergraduates:

Yingxi Li, undergraduate in ORIE (PhD at Stanford). Efficient algorithms for combinatorial optimization.
Kathy (Ja Young) Byun, undergraduate in ORIE (PhD at Chicago Booth). Population segmentation for health system management.
Raye Liu, undergraduate in Math. Population segmentation for health system management.
Eliot Shekhtman, undergraduate in CS. Clustering mixed data.
Kevin Cushing, undergraduate in CS. Distribution shift.
Aparna Calambur, undergraduate in CS (CNA Research). Interpretable clustering.
Chris Qian, undergraduate in Math (PhD in Statistics at UIUC). Fairness in ML.
Eric Landgrebe, undergraduate in Math and CS (Facebook). Imputing missing data.
Brian Liu, undergraduate in ORIE (Microsoft, PhD at MIT). Interpretable machine learning.
Nick Bagley, undergraduate in Applied Math (PhD in Applied Math at University of Arizona). Learning PDEs.
Yuji Akimoto, undergraduate in CS (LA Dodgers Analytics). Automatic machine learning.
Charlene Luo, undergraduate in ORIE (Masters in Data Science at Columbia). Tensor factorization.
Yang Guo, undergraduate in Statistics (PhD in CS at UW Madison). Tensor factorization.
Dae Won Kim, undergraduate and MEng student in ORIE (Munich Reinsurance). Automatic machine learning.
Ahaan Nachane, undergraduate in CS (Braze). Data Infrastructure for Distributed Machine Learning.
Anya Chopra, undergraduate in CS. Low rank modeling in Python.
Patrick Nicholson, undergraduate in CS. Identifying gerrymandering.
Mihir Paradkar, undergraduate in BEE (Yelp). Data imputation for health care.
Zachary Rosenof, undergraduate in ORIE (McKinsey). Designing urban transport systems.

Cornell masters students:

Haoyue Yang: MS student in Statistics. Feature importance in tree ensembles.
Jason (Zuhao) Hua, MEng student in Information Science (American Express). Learning PDEs.
Ziwei Gu, MS in CS (Lyft). Population segmentation for health system management.
Ziyang Wu, MS in CS (PhD in CS at UIUC). Automated machine learning.
Nandini Nayar, MS in CS. Resource allocation in AutoML.
Mi Zhou, MS in CS. Neural PDEs.
Murali Thimma Selvan Babu, Jibran Gilani, Patchara Suensilpong, Yi Yao, MEng students in ORIE. Consumer segmentation for financial marketing.
Brandon Kates, MEng student in CS. Learning combinatorial algorithms.
Jianqiu Liu, Kyle Wadell, Jamie Wong, Michael Yuan, MEng students in ORIE. Unsupervised learning for cybersecurity.
Vidita Gawade, Caroline Troude, Juan Felipe Gonzalez Rodriguez, Yikun Cai, MEng students in ORIE. Forecasting wine harvest volume.
Qin Lu, MEng student in ORIE (Amazon). Interpretable machine learning.
Fan Liu, MEng student in ORIE (Goldman Sachs). Interpretable machine learning.
Fan Liu, David Lee, Soo Hyun Lee, Junrui Ye, and Srishti Sarawat, MEng students in ORIE. Forecasting grape ripeness.
Ishaan Jain and Darpan Kalra, MS students in CS. Deep learning for image classification.

Google summer of code:

Ayush Pandey, undergraduate in Mathematics and CS at IIT Kharagpur (ZemantaOutbrain). Optimization with complex variables./
Ramchandran Muthukumar, Masters student in Mathematics and undergraduate in CS at BITS Pilani. Faster presolve for linear programming and beyond.

Exchange students:

Huichen Li, undergraduate in CS at Shanghai Jiao Tong University (PhD in CS at UIUC). Learning low-rank tensors.
Sam (Song) Zhou, undergraduate in mathematics at Tsinghua (PhD in ORIE at Cornell). Convex optimization over combinatorial structures.
Ramchandran Muthukumar, masters student in Math and undergraduate in CS at BITS Pilani (PhD in CS at Johns Hopkins). PDE-constrained optimization.

Postdocs and visitors:

Theo Diamandis, PhD student in EECS at MIT. (Advised by Alan Edelman.) Large-scale optimization.
Luiz-Rafael (Rafa) Santos, Professor of Applied Mathematics and Optimization, Federal University of Santa Catarina, Brazil
Palma London, postdoctoral fellow.
Iddo Drori, visiting associate professor (MIT CS).
Yuqian Zhang, postdoctoral fellow (Rutgers ECE).
Jicong Fan, postdoctoral associate (CUHK Shenzhen Data and Decision Analytics).