Shiva Kaul - AI Scientist

About me

I am a senior AI scientist at Optum. Previously, I was a research fellow at the Center for AI and Biomedical Informatics in the Department of Population Medicine at HPHCI / Harvard Medical School. I graduated from the Ph.D. program at Carnegie Mellon's Computer Science Department. My advisor was Geoff Gordon. Earlier, I earned an M.S. in CS under Mahadev Satyanarayanan, and spent time at Microsoft Research under Denny Zhou.

Selected papers

The Verbose Context Problem in Medical Records By Shiva Kaul, Min-Gyu Kim, Anjum Khurshid, and Sriram Vishwanath. ICML SD4H 2026. Selected for Spotlight Presentation.
Position: Medical AI Neglects Real Treatment Outcomes By Shiva Kaul and Anjum Khurshid. ICML 2026.
Meta-Analysis with Untrusted Data By Shiva Kaul and Geoffrey J. Gordon. ML4H 2024. Full arXiv version
Classical Improvements to Modern Machine Learning Ph.D. Thesis, CMU-CS-24-137
Linear Dynamical Systems as a Core Computational Primitive By Shiva Kaul. NeurIPS 2020. Selected for Spotlight Presentation. Code
Measuring the Sympathetic Response to Intense Exercise in a Practical Setting By Shiva Kaul, Anthony Falco, and Karianne Anthes. MLHC 2019
Margins and Opportunity By Shiva Kaul. AAAI/AIES 2018. Selected for Doctoral Consortium

Selected talks

What is Population Health AI? Dept. of Population Medicine, HPHCI / Harvard Medical School, October 2025
Meta-Analysis with Untrusted Data Sunlab, University of Illinois, September 2024
Classical Improvements to Modern Machine Learning Ph.D. Thesis Defense, August 2024. Full PPT
Linear Dynamical Systems as a Core Computational Primitive NeurIPS 2020 Spotlight. Poster and Video
Trusting Modern Machine Learning Allergan Scientific Series, January 2020
Symmetric Tensor Rank SELECT LAB, Carnegie Mellon, Spring 2013
Semidefinite Programming Hierarchies for Polynomial Programs 10-725 Optimization Lecture, Carnegie Mellon, Fall 2012
Conic Duality 10-725 Optimization Recitation, Carnegie Mellon, Fall 2012
Anticoncentration Regularizers for Stochastic Combinatorial Problems NIPS 2011 Workshop on Computational Trade-offs in Statistical Learning
Electronic Structure Theory, Quickly SELECT LAB, Carnegie Mellon, January 2011

Open source

neopatient is an open-source software package for language-controlled generation of artificial patient records. Longitudinal patient records are useful for training and evaluating AI, but real ones are encumbered. Just write out (in natural language) what you do/do not want the patients to be like. neopatient handles sampling, chunking, batching, structuring, and verification. It cost-effectively generates lots (tens of thousands) of records, each up to 100K+ tokens, in the MEDS format.

Teaching

I was a teaching assistant for following courses at Carnegie Mellon:

10-725 Optimization, a graduate-level course in optimization for machine learning researchers. Taught by Geoff Gordon and Ryan Tibshirani; assisted by myself, Wooyoung Lee, Aaditya Ramdas, and Kevin Waugh.
15-359 Probability and Computing, an undergraduate-level course in probability and stochastic processes. It was taught by Mor Harchol-Balter and Klaus Sutner.

I was the lead instructor for the following courses:

IDEA MATH: Playing Prediction Games, a volunteer course taught at a summer camp for talented high school students.
98-085 Ruby on Rails, a student-led, twice-offered undergraduate course on the (at the time) brand-new, highly-productive web application framework.

I created some concise measure-theoretic probability flashcards, while reading an assortment of books and taking a couple courses on the subject.

Personal

I have participated in distance running, CrossFit, and (these days) powerlifting / barbell training. I enjoy cooking and taking care of Arktos, my Samoyed dog.

Contact

Email is preferred.