Asim H. Gazi

I am a postdoctoral fellow in computer science and statistics at Harvard University, advised by Susan A. Murphy. I completed my Ph.D. in electrical engineering in 2023 from the Georgia Institute of Technology, advised by Omer T. Inan and Christopher J. Rozell.

I am currently funded by a NIH K99/R00 Pathway to Independence Award from the National Institute of Biomedical Imaging and Bioengineering, which will fund the remainder of my postdoc and up to $747k over my first three years as an assistant professor at whichever institution I join. Previously, I was supported by Schmidt Sciences, in partnership with the Rhodes Trust, as one of 32 Schmidt Science Fellows selected from around the world in 2023. My PhD was funded by a National Science Foundation Graduate Research Fellowship.

Outside of research, I am a Pathfinder for Boston Partners in Education. If you are interested in having me visit your middle or high school class, please feel free to reach out! You can read about one of my past visits here. I am also the founder and owner of Internet of Tutors LLC.

Research Summary

To help prevent and treat chronic diseases, I work on the next generation of intelligent mobile health (mHealth) interventions that provide personalized healthcare support during everyday life, leveraging wearable sensors and other ubiquitous sensor technologies for real-time feedback. My research addresses the need to tailor healthcare support to the dynamics of daily life – support that is currently left unaddressed by traditional healthcare systems that rely on infrequent, synchronous clinical support (e.g., weekly psychotherapy that accounts for less than 1% of one’s life).

The key premise of my work is that precision medicine should not just entail tailoring interventions to an individual’s genetics or other characteristics that differ from person to person but are constant over time. Healthcare interventions should be also tailored to an individual’s biobehavioral states such as their physiological stress or social context that change over time. A quite remarkable (and challenging) aspect of health care delivery is that the same intervention can produce vastly different outcomes when delivered to the exact same individual in a different biobehavioral state.

As an engineer with interdisciplinary research training, my use-inspired research focuses specifically on the data analytics, models, and algorithms that enable closed-loop control for mHealth interventions – sensor-informed decision-making to help adaptively tailor mHealth support to a person’s dynamically changing biobehavioral state. My core technical contributions have centered mainly around methods to manage uncertainty when inferring changes in biobehavioral state or personalizing sensor-driven interventions. Sources of uncertainty include intermittent corruptions of wearable sensor data, where signal quality assessment is paramount; fallible predictions of biobehavioral states, where uncertainty-informed decision making is vital; and the lack of quantitative mechanistic models to inform decision making for nascent mHealth interventions such as non-invasive vagus nerve stimulation, where hybrid modeling and digital twins are important to inform control design and system deployment.

Through collaborations with clinician scientists, my translational research efforts have led to clinical impact. Collaborative research with the Emory School of Medicine formed the scientific basis for US Food and Drug Administration Breakthrough Designation in 2022 of non-invasive vagus nerve stimulation for patients with posttraumatic stress disorder.