Mithil Guru
I'm a data analyst at Fighting Illini Athletics and a researcher with the Human-Computer Integration Lab at the University of Chicago.
Through my work, I explore how to integrate new sensing and feedback modalities to enhance daily living. My research interests lie in human-computer interaction, health sensing, and haptics.
Experience
Research Collaborator
Human Computer Integration Lab - University of Chicago
Exploring novel sensing and feedback systems with muscle stimulation for physical skill acquisition and assistance. Advisor: Professor Pedro Lopes.
Data Analyst
Fighting Illini Athletics
Unifying athlete performance, health, and operations into systems that help teams make better decisions.
Research Assistant
Center for Health Informatics - University of Illinois at Urbana-Champaign
Mapped public health information spread across World Health Organization social media networks to advise on communication strategy. Advisors: Dr. Ian Brooks, WHO Communications.
Data Analyst Intern
SHIELD Illinois
Used epidemiological modeling to support campus testing frequency decisions for COVID-19 response.
Research Assistant
Kinesmetrics Lab - University of Illinois at Urbana-Champaign
Modeled clinical health data to classify osteoporosis risk and surface predictors of skeletal decline in underrepresented populations.
Publications
Mithil Guruvugari, Romain Nith, and Pedro Lopes
In Proceedings of CHI 2026
Siya Choudhary, Romain Nith, Jas Brooks, Yun Ho, Mithil Guruvugari, and Pedro Lopes
In Proceedings of CHI 2025
Projects
Logged biometric data from fitness tracker (activity, sleep, vitals), meal data (macronutrients, micronutrients), mood data (RULER mood meter), and productivity data (task completion, time blocking). Trained a regressor to predict mood and energy levels, enabling behavior change for improved focus.
Analyzed multi-phase force plate data across reactivity and isometric strength tests to identify novel overtraining indicators. Applied correlation analysis and regression modeling in Python to select variables with the strongest influence on target performance metrics.