Madeleine Udell

  • Cornell PhD students: I will be taking on a few PhD students this year. Send me an email or drop by my office if you're interested in working with me. Potential projects include

    • fast semidefinite programming,

    • automatic analysis of optimization methods,

    • causal inference with big messy data,

    • recommender systems for revenue management,

    • automatic machine learning,

    • private machine learning,

    • PDE-constrained optimization,

    • if you have your own project in mind, come tell me about it!

  • Cornell 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.

  • Students at other universities: If you'd like to work with me, please apply to a PhD program at Cornell! I advise students in many different PhD programs: ORIE, Stats, CS, ECE, and CAM. Take a look at the core course requirements and faculty research profiles for each and see which one fits your interests best.

Current Research Group

PhD students:

  • Lijun Ding, PhD student in ORIE. (Co-advised with Yudong Chen.) Fast methods for low rank convex optimization.

  • Chengrun Yang, PhD student in ECE. Automatic machine learning.

  • Yiming Sun, PhD student in Statistics. (Co-advised with Sumanta Basu.) Interpretable machine learning.

  • Xiaojie Mao, PhD student in Statistics. (Co-advised with Nathan Kallus.) Causal inference with missing covariates.

  • Matthew Zalesak, PhD student in ORIE. (Co-advised with Samitha Samaranayake.) Designing urban transport systems.

Undergraduate students:

  • Yuji Akimoto, undergraduate in CS. Automatic machine learning.

Other research affiliates:

  • Jicong Fan, postdoctoral associate.

  • Yuqian Zhang, postdoctoral fellow.

  • Ramchandran Muthukumar, predoctoral researcher. PDE-constrained optimization.


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

Cornell undergraduates:

  • 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. 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:

  • 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 (Researcher in ORIE at Cornell). PDE-constrained optimization.