H. Satyam Verma

PhD Candidate in Operations Management

Tepper School of Business, Carnegie Mellon University

Healthcare Operations · Sequential Decision-Making Under Uncertainty · Optimization and Machine Learning · Healthcare Markets and Pricing · Business Analytics

Curriculum Vitae Google Scholar Email
Portrait of H. Satyam Verma

About

I am a PhD candidate in Operations Management at the Tepper School of Business, Carnegie Mellon University, advised by Sridhar R. Tayur. My research spans healthcare operations, from individual treatment decisions to hospital–insurer markets, drawing on optimization, machine learning, and game theory. Recent work uses Bayesian optimization and generative modeling for sequential decisions under uncertainty, applied to settings where data is scarce, such as personalizing drug dosing from limited patient histories. I also study healthcare markets and pricing, combining game-theoretic modeling with empirical analysis.

Before CMU, I earned an M.Sc. in Operations Research from IIT Bombay and worked as a Data Scientist on the optimization team at Delhivery, India's largest fully integrated logistics company.

I will be on the academic job market in Fall 2026, seeking faculty positions beginning Fall 2027 in Operations Management, Business Analytics, and Healthcare Operations.

Research

Working Papers

GenEx: Generative Augmentation of Scarce Data through Expert Guidance for Personalized Dosing [SSRN]

H. S. Verma, H. Wiberg, S. (Woody) Zhu, and S. R. Tayur

Manufacturing & Service Operations Management (M&SOM) · Major Revision

Runner-Up, INFORMS Health Applications Society Best Student Paper Competition (2025)

Personalized treatment optimization is constrained by data scarcity, especially for rare patient profiles, limiting individualized and safe dosing recommendations. We introduce GenEx, a hybrid framework integrating preference-based Bayesian optimization with a generative model fine-tuned via expert feedback. A trust-gated component proposes synthetic data in uncertain regions, while pairwise expert rankings guide updates to both surrogate and generator. A retrospective tacrolimus study in kidney transplant recipients demonstrates a mean 25.9% improvement in treatment utility, with gains retained under noisy feedback. GenEx is humans augmenting AI as much as it is AI augmenting humans.

Price Transparency and Hospital–Insurer Bargaining: A Three-Channel Model with Evidence from Negotiated Rate Data

H. S. Verma, D. Agrawal, A. Hosseininasab, and S. R. Tayur

Working Paper

Published Journal & Conference Articles

Multi-page Menu Recommendation in Cascade Model with Externalities [DOI]

R. Dhingra, H. S. Verma, A. Reiffers-Masson, and V. Kavitha

60th IEEE Conference on Decision and Control (CDC) · 2021

Scalable Multi-Product Inventory Control with Lead Time Constraints Using Reinforcement Learning [DOI]

H. Meisheri et al., including H. S. Verma

Neural Computing and Applications · 2022

Selected Presentations

  • GenEx: Generative Augmentation of Scarce Data through Expert Guidance for Personalized Dosing
    INFORMS Healthcare Conference (2026) · NeurIPS ML × OR Workshop, poster (2025) · INFORMS Annual Meeting, Atlanta (2025) · INFORMS Workshop on Data Science (2025) · Cornell ORIE Young Researchers Workshop (2025) · POMS Conference (2025)
    Earlier versions of this work were presented in 2025 under the titles “Data to Dose” and “Improved Personalized Drug Dosing through Data Generation and Expert Feedback.”
  • Price Transparency and Hospital–Insurer Bargaining: A Three-Channel Model with Evidence from Negotiated Rate Data
    INFORMS Healthcare Conference (2026) · POMS Conference (2026)
  • Multi-page Menu Recommendation in Cascade Model with Externalities
    IEEE Conference on Decision and Control, CDC (2021)

Teaching

Teaching Assistant

  • Operations Management MBA & Undergraduate Core
    Carnegie Mellon University · Fall 2024, Spring 2025, Fall 2025
  • Operations and Supply Chain Analytics MBA & Undergraduate Elective
    Carnegie Mellon University · Spring 2024, Spring 2026
  • Supply Chain Management MBA & Undergraduate Elective
    Carnegie Mellon University · Spring 2026
  • Service Management: Go-to-Market Strategy MBA Elective
    Carnegie Mellon University · Fall 2025
  • Probability and Stochastic Models I & Stochastic Processes II M.Sc. Operations Research Core
    IIT Bombay · Fall 2020, Spring 2021

Teaching Interests

Operations Management · Healthcare Operations · Stochastic Models · Machine Learning for Business · Data Analytics & Optimization

Honors & Awards

  • 2025 Runner-Up, INFORMS Health Applications Society Best Student Paper Competition
  • 2024 Generative AI Fellowship, Center for Intelligent Business, Tepper School of Business
  • 2022 William Larimer Mellon Fellowship, Tepper School of Business
  • 2018 All-India Rank 8, Joint Admission to M.Sc. (JAM) Mathematics

Service

Contact

Outside of research, I play guitar, piano, and drums, and I write and compose music.