Working Papers

1. Machine learning reveals hidden diagnoses among underserved patients
   D. Shanmugam*, B. Hardy, A. Wang, S. Divakaran, J. Guttag, E. Pierson**, M. Barnett**
   (under review)

2. The Trillion Dollar Algorithm: Lessons from ML for Medicare Advantage Risk Adjustment
   D. Shanmugam*, M. Johnson*, D. Meyers, J. Wiens, E. Pierson
   (under review)

Selected Publications

• Evaluating multiple models using labeled and unlabeled data
  D. Shanmugam*, S. Sadhuka*, M. Raghavan, J. Guttag, B. Berger**, E. Pierson**
  NeurIPS 2025

• Learning disease progression models that capture health disparities
  E. Chiang, D. Shanmugam, A. Beecy, G. Sayer, N. Uriel, D. Estrin, N. Garg, E. Pierson
  CHIL 2025 ⭐️ Best Paper Award for Impact & Society ⭐️ (code)

• Test-time augmentation improves efficiency in conformal prediction
  D. Shanmugam, H. Lu, S. Swaminarayan, J. Guttag
  CVPR 2025

• Generative Artificial Intelligence in Medicine
  D. Shanmugam, M. Agarwal, R. Movva, IY. Chen, M. Ghassemi, M. Jacobs, E. Pierson
  Annual Review of Biomedical Data Science 2025

• Use large language models to promote health equity
  E. Pierson*, D. Shanmugam*, R. Movva*, J. Kleinberg* … & many others!
  NEJM AI 2025.

• Quantifying Inequality in Underreported Medical Conditions
  D. Shanmugam, K. Hou, E. Pierson
  npj Women’s Health 2024.
  (code)

• Coarse race data conceals disparities in clinical risk score performance
  R. Movva*, D. Shanmugam*, K. Hou, P. Pathak, J. Guttag, N. Garg, E. Pierson
  MLHC 2023 ⭐️ Honorable Mention, Best Findings Paper at ML4H 2023 ⭐️ (code)

• A multi-site study of the relationship between photoperiod and ovulation rate using NaturalCycles data
  D. Shanmugam, M. Espinosa, J. Gassen, A. van Lamsweerde, JT Pearson, E. Benhar, S. Hill
  Nature Scientific Reports 2023.

• Kaleidoscope: Semantically-grounded, context-specific ML model evaluation
  H. Suresh, D. Shanmugam, T. Chen, A. Bryan, A. D’Amour, J. Guttag, A. Satyanarayan
  Conference on Human Factors in Computing Systems 2023.

• Learning to Limit Data Collection for Data Minimization via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
  D. Shanmugam, S. Shabanian, F. Diaz, M. Finck, A. Biega
  Fairness, Accountability and Transparency Conference 2022. (supp, code)

• Data Augmentation for Electrocardiograms
  A. Raghu, D. Shanmugam, E. Pomerantsev, J. Guttag, C. Stultz
  Conference on Health, Inference, and Learning 2022. (code)

• Better Aggregation in Test-Time Augmentation
  D. Shanmugam, D. Blalock, G. Balakrishnan, J. Guttag
  International Conference on Computer Vision 2021.
  (supp, slides, code) ⭐️ Oral Presentation ⭐️

• Multiple Instance Learning for ECG Risk Stratification
  D. Shanmugam, D. Blalock, J. Guttag
  Machine Learning in Healthcare Conference 2019.
  (slides) ⭐️ Oral Presentation ⭐️

Preprints

“Nothing stinks like a pile of unpublished writing” - Sylvia Plath, The Bell Jar

• More than One Way to Train a Net: Predicting Ovulation using Multiple Proxies
  D. Shanmugam, S. Hill, J. Guttag

• When and Why Test-Time Augmentation Works
  D. Shanmugam, D. Blalock, G. Balakrishnan, J. Guttag