Sarah M. Preum
|Assistant Professor
Assistant Professor of Computer Science
Technical Associate Director, Dartmouth Center for Precision Health and Artificial Intelligence
Adjunct Faculty, Department of Biomedical Data Science, Geisel School of Medicine
I am a scientist working at the intersection of Natural Language Processing, Human–AI Interaction, and Computational Health. My research explores how AI can become a more effective communication partner in high-stakes settings, ultimately helping people exchange information, navigate uncertainty, and make informed decisions with less cognitive burden. I am particularly interested in socio-technical AI systems that are adaptive, equitable, and grounded in how people communicate and make decisions in the real world.
Contact
Engineer and Comp Science Ctr, Room 019
HB 6211
Department(s)
Biomedical Data Science, Computer Science
Education
- Ph.D. University of Virginia
- M.Sc. University of Virginia
- B.Sc. Bangladesh University of Engineering and Technology
Selected Publications
Joseph Gatto, Omar Sharif, Parker Seegmiller, Sarah M. Preum. Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types. The 63rd Annual Meeting of the Association for Computational Linguistics (ACL) 2025. (Nominated for best paper)
Joseph Gatto, Parker Seegmiller, Timothy Burdick, Inas S. Khayal, Sarah DeLozier, Sarah M. Preum. Follow-up Question Generation For Enhanced Patient-Provider Conversations. The 63rd Annual Meeting of the Association for Computational Linguistics (ACL) 2025.
Parker Seegmiller, Joseph Gatto, Sarah Greer, Ganza Belise Isingizwe, Rohan Ray, Timothy Burdick, Sarah M. Preum. How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL), 2026.
Parker Seegmiller, Sarah M. Preum. Measuring the Effects of Natural Shifts in User Prompt Distribution on Large Language Model Performance. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL), 2026.
Speaking Engagements
When Ground Truth Is a Distribution: New Tasks and Evaluation Paradigms for Asynchronous, High-Stakes Communication. Colloquium at Computational Health Informatics Program (CHIP), Spring 2026, Harvard University.
NLP Seminar, Department of Computer Science, Spring 2026, Columbia University.
Language Technology Institute Colloquium, Spring 2026, Carnegie Mellon University.
From Cognitive Burden to Collaborative Intelligence: Socio-Technical AI for Clinical Communication. Human Computer Interaction Institute Colloquium, Spring 2026, Carnegie Mellon University.
Center for Technology and Behavioral Health Seminar (CTBH) Series, Fall 2025.
SYNERGY Translational and Learning Health System Science Collaborative Seminar Series, Fall 2025, Dartmouth Clinical and Translational Science Institute.
Network Science Institute Colloquium, Department of Computer Science, Fall 2024, Northeastern University.
Unveiling the Journey: Analyzing Information Seeking Events in Online Recovery Discourse. Center for Technology and Behavioral Health Seminar Series, Fall 2023.
Intelligent Assistants for Improved Health: Teaching Machines to Understand Data. Biomedical Data Science Grand Rounds, Geisel School of Medicine, Winter 2022, Dartmouth College.
Preparation for Research through Immersion, Skills, and Mentorship (PRISM) seminar, Department of Computer Science, Spring 2021, University of Toronto
Information Extraction & Fusion for Improving Personal & Public Health Safety, Senseable City Lab Seminar, Spring 2020, Massachusetts Institute of Technology (MIT).
Human-centered Artificial Intelligence Center Seminar, Spring 2020, Stanford University.
Future Directions for Clinical Decision Support, Clinical Decision Support Workshop, Fall 2020, Office of the National Coordinator for Health Information Technology.
Towards a cognitive assistant for emergency response, Public Safety Broadband Stakeholder Meeting by National Institute of Standards and Technology (NIST)