Time series LLMs with Reasoning
Many of the signals that matter most in the real world come as numerical time series: heart-rate and ECG traces in hospitals, stock prices and economic indicators in finance, or […]
Nimeesha Chan
PhD Candidate
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About Nimeesha Chan
Nimeesha Chan is a PhD student in the Department of Civil and Systems Engineering (CaSE), the Center for Systems Science and Engineering (CSSE), the Institute for Assured Autonomy (IAA), and the Malone Center for Engineering in Healthcare at Johns Hopkins University. She has served as Secretary for the INFORMS JHU student chapter and as a board member for the Civil and Systems Engineering Graduate Association (CSEGA). Nimeesha is passionate about using machine learning to model patient state from multimodal medical data—across different physiological processes and time scales. Her research has focused on augmenting Large Language Models (LLMs) to analyze diverse data types in the Intensive Care Unit (ICU), with applications to understanding and improving patient-ventilator interactions. She aims to advance the integration of data-driven methods with physiological modeling, particularly in understanding cardiopulmonary interactions and how different patient data sources can reveal finer-grained insights into clinical state.
