Hello, I’m Mayee!

I am a PhD student in Computer Science at Stanford University, advised by Prof. Christopher Ré and part of the Hazy Research Lab.

I’m interested in exploring fundamental questions behind tools in modern machine learning and using them to develop new, theoretically grounded methods. My current interests revolve around how to encode and evaluate sources of supervision and side information throughout the ML pipeline (e.g. weakly/semi/self-supervised) through both information-theoretic and geometric lenses. In particular, my work in graduate school so far has applied this interest to latent variable graphical models, distribution shift, and representations learned via contrastive losses.

Previously, I graduated summa cum laude from Princeton University in 2019 with a concentration in Operations Research and Financial Engineering (ORFE) and a certificate in Applications of Computing. I worked on my senior thesis on quantum machine learning with Prof. Elad Hazan and completed junior independent work on modeling misinformation in social networks with Prof. Miklos Racz.

Publications and Preprints

• Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision
  Mayee F. Chen*, Daniel Y. Fu*, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Ré. Uncertainty in Artificial Intelligence (UAI), 2022. Oral Presentation.
  paper | slides for Snorkel AI talk

• Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
  Mayee F. Chen*, Daniel Y. Fu*, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, and Christopher Ré. International Conference on Machine Learning (ICML), 2022.
  paper

• TABi: Type-Aware Bi-encoders for End-to-End Entity Retrieval
  Megan E. Leszczynski, Daniel Y. Fu, Mayee F. Chen, and Christopher Ré. To Appear in the Findings of the Association for Computational Linguistics (ACL), 2022.
  paper

• The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning
  Mayee F. Chen*, Daniel Y. Fu*, Michael Zhang, Kayvon Fatahalian, and Christopher Ré. AAAI Workshop on Artificial Intelligence with Biased or Scarce Data, 2022. Best Paper Award.
  paper

• An Adversarial Model of Network Disruption: Maximizing Disagreement and Polarization in Social Networks.
  Mayee F. Chen and Miklos Z. Racz. IEEE Transactions on Network Science and Engineering (TNSE), 2021.
  paper | code

• Mandoline: Model Evaluation under Distribution Shift
  Mayee F. Chen*, Karan Goel*, Nimit Sohoni*, Fait Poms, Kayvon Fatahalian, and Christopher Ré. International Conference on Machine Learning (ICML), 2021.
  paper | code | slides

• Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation.
  Mayee F. Chen*, Benjamin Cohen-Wang*, Steve Mussmann, Frederic Sala, and Christopher Ré. Artificial Intelligence and Statistics (AISTATS), 2021.
  paper | slides

• Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods.
  Daniel Y. Fu*, Mayee F. Chen*, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, and Christopher Ré. International Conference on Machine Learning (ICML), 2020.
  paper | code | video | blog

• Effect of Rotational Grazing on Plant and Animal Production.
  Mayee F. Chen and Junping Shi. Journal of Mathematical Biosciences and Engineering, vol. 15, no. 2. 2018.
  paper | slides

• Efficient GCD Computation for Big Integers on Xeon Phi Coprocessor.
  Jie Chen, William Watson, and Mayee F. Chen. IEEE Conference on Networking, Architecture, and Storage (NAS). 2014.
  paper | slides