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 using theoretical tools to understand and improve on modern machine learning techniques. Recently, I’ve been focused on data-centric AI, working on understanding the role of data through weak supervision and data selection, in particular for foundation models. I’m also interested in the representation geometry of neural networks and how to induce desirable geometries.

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, where I worked with Prof. Elad Hazan and Prof. Miklos Racz.

Publications and Preprints

• Anomaly Detection with Multiple Reference Datasets
  Mayee F. Chen, Benjamin Nachman, Frederic Sala. Machine Learning and the Physical Sciences (ML4PS) Workshop at NeurIPS, 2022.
  paper | code

• Ask Me Anything: A simple strategy for prompting language models
  Simran Arora*, Avanika Narayan*, Mayee F. Chen, Laurel J. Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré. In submission.
  paper | code

• Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity.
  Khaled Saab, Sarah M. Hooper, Mayee F. Chen, Michael Zhang, Daniel Rubin, Christopher Ré. Machine Learning for Healthcare (MLHC), 2022.
  paper

• 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. Best Student Paper Runner-Up Award, Oral Presentation.
  paper | code slides | blog | Snorkel 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 | code | blog

• 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 | code | blog

• 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 | 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 | MedAI talk

• 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

Older

• 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

• 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