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 […]
Kimia Ghobadi
CSSE Associate Director
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About Kimia Ghobadi
John C. Malone Assistant Professor of Civil and Systems Engineering
Kimia Ghobadi is a John C. Malone Assistant Professor of Civil and Systems Engineering and Malone Center for Engineering in Healthcare at the Whiting School of Engineering. Prior to that, she was a postdoctoral fellow at MIT Sloan School of Management, and received her PhD in Industrial Engineering at the University of Toronto. She obtained her Bachelor in Mathematics at Sharif University and her Masters’ in Mathematics and Computational Engineering and Science at McMaster University. Kimia is a recipient of INFORMS Judith Liebman award and Canada’s Natural Sciences and Engineering Research Council’s graduate and postdoctoral scholarships.
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Research Projects
Patient State Modeling in Critical Care: LLMs and Continuous State Estimation
Precise modeling of patient states in critical care–essential for timely intervention and outcome prediction–remains constrained by two fundamental challenges: 1) existing models struggle to integrate heterogeneous data streams (e.g. physiological […]
The Impact of AI Scribe on Primary Care Workload
Primary care clinicians spend large amounts of their working hours, both in- and outside of the clinic, updating electronic health record (EHR) documentation for patients. In response, LLM-based AI tools […]
News Stories
Kimia Ghobadi Awarded Johns Hopkins AITC Funding
A team including John C. Malone Assistant Professor of Civil and Systems Engineering Kimia Ghobadi has been selected to receive support from the third round of grant funding from the Johns Hopkins Artificial […]
Wait Watchers: A New Data-Driven Interface Shows Potential in Slimming Down Hospital Waits
With patient loads increasing, a new optimization framework and interface could help improve hospital capacity management
Johns Hopkins Systems Engineers Uncover New Insights in COVID-19 Mortality Patterns
Using a novel, comprehensive methodology, the team analyzed mortality-influencing factors to inform future pandemic public health strategies