Asim is a biomedical engineer and data scientist interested in digital health technologies. During his PhD, Asim designed models and algorithms that improve the quantification of stress using wearable sensors for digital health. His methods map changes in cardiovascular, respiratory, and sweat signals to changes in underlying autonomic nervous system state.
As a Schmidt Science Fellow, Asim will join the Statistical Reinforcement Learning Lab at Harvard University to work with Professor Susan Murphy. Asim will study the design and optimization of sequential decision-making algorithms for digital health. These algorithms take information on an individual’s state and suggest interventions in a timely manner that are personalized to the user. Asim aims to combine his PhD expertise with intervention design and optimization knowledge to create fully closed-loop digital health systems that can sense and react during everyday life.
During Asim’s undergraduate years, two of his closest relatives were admitted to psychiatric facilities due to struggles with mental illness. What pained him the most was his inability to recognize their anguish and do anything to support them. He has since devoted himself to exploring how technology can be used to monitor and care for mental health during everyday life. His ultimate goal is to address the critical gap in care that exists before individuals seek help and after patients leave the clinic.