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**
   (pdf, code, under review)

2. A Roadmap for Responsibly Removing Race from Clinical Algorithms
   D. Shanmugam*, S. Balachandar, A. Chouldechova, J. Diao, K. Ferryman, A. Manrai, S. Pfohl, N. Powe, I. D. Raji, E. Pierson
   (under review)

3. Improving Spontaneous Labor Prediction with Electronic Health Record Data
   M. Krishnamoorthy*, D. Shanmugam*, D. Tjandra, A. Peahl, C. Pancaro, E. Ziedan, A. Kowalski, J.Wiens
   (in preparation)

4. 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

See Google Scholar for a complete list.

• 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)

• 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)

• 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