Research
Research, in all forms, scratches my brain in the best way. I have had the opportunity to explore unique problems and contribute to novel solutions, and you can find some of what I’ve worked on below.
Data analysis of mobile health clinics in the U.S.
Mobile Health Map, The Family Van, Harvard Medical School
mobile clinics, public health, mobile healthcare, health equity
Mobile Health Map is a Harvard-based research initiative that maintains the largest national database of mobile clinics in the United States (https://www.mobilehealthmap.org). It is designed to evaluate, connect, and expand mobile clinics as community-based models of care. Mobile clinics bridge medicine and public health by bringing clinical and preventive services directly to local and underserved populations. The initiative seeks to quantify the impact of mobility in healthcare delivery and inform broader discussions about equity, cost-effectiveness, and policy design by compiling operational, demographic, and outcome data from hundreds of programs nationwide.
My contribution to the Mobile Health Map is analyzing patterns within this dataset to characterize how mobile clinics function across regions and populations, and to quantitatively and qualitatively ascertain their impact. I explore longitudinal trends in clinic activity and interpret relationships between funding, services, and patient outcomes. This analysis supports an ongoing publication examining the evolving role of mobile health delivery in the U.S., especially in relation to its effect on the access of underserved populations to care. This work contributes to a broader effort to define what mobility means in modern healthcare and how bringing services to communities reshapes traditional ideas of infrastructure, patient engagement, and clinical responsibility. Insights from these analyses inform outreach strategies for new programs and generate metrics that policymakers and funders can use to evaluate impact, positioning mobile health as both a research domain and a practical framework for equitable care delivery.
Enabling real-time monitoring of recombinant carbonic anhydrase for optimized CO2 capture in bioreactor systems
Biotechnology Process Engineering Center, MIT
bioprocess engineering, sustainable chemical engineering, sensor integration
This project focuses on enabling real-time monitoring of recombinant carbonic anhydrase (rCA), an enzyme used in CO₂ capture systems. Enzyme-based approaches to CO₂ capture are often limited by production cost and process instability, so improving upstream control during fermentation is useful for the yield, consistency, and scalability of the entire workflow. CA accelerates the conversion of CO₂ to bicarbonate, which is especially useful for mineral carbonation processes, and therefore has applications in cement production, soil treatment, and carbon sequestration. So, the project aims to support carbon reduction and sustainable carbon sourcing.
My project involves integrating a GFP-linked fluorescence sensor into our fermentation workflow, using GFP as a proxy for rCA expression. The rCA used in our system was recombinantly expressed as a fusion protein with GFP, which allowed me to track production in real time via the sensor without relying on post-hoc assays. I began by first calibrating the CST PX2+ fluorescence sensor using purified GFP standards, then moved on to develop a more realistic calibration method using culture-based dilutions to account for optical noise present in the culture during fermentation. These refinements made the sensor increasingly viable for real-time use inside a live bioreactor environment. Once calibrated, I ran a series of fermentation experiments using GFP-expressing auto-induction strains and tested different conditions (culture density, mixing speed, probe positioning) to assess how the sensor responded under realistic growth dynamics, aiming to produce a reliable signal that could track GFP expression trends with enough resolution to inform fermentation optimization decisions.
To extend the system’s usefulness beyond manual monitoring, we began collaborating with a startup focused on lab automation and bioreactor integration. We discussed how to progress the project towards a feedback-driven control system that would allow the sensor to both observe enzyme production in real time and actively modulate process parameters (i.e. temperature, media flow rate) to optimize yield with minimal human intervention. Having built a system that measures expression dynamics in real time, this begins setting the stage for adaptive control and scalability, which is a step toward a plug-and-play solution for industrial bio-manufacturing.
An example of a 1.5L bioreactor experimental run
Inflammatory profiling for wearable sweat sensors
Sen-Jam Pharmaceutical
inflammatory biomarkers, wearable biosensors, translational research
Sen-Jam Pharmaceutical is a biotech company developing anti-inflammatory therapeutics through novel formulations and delivery systems. My work there centers on identifying and validating inflammatory biomarkers measurable through sweat for integration into a wearable sensing platform, with the goal of enabling continuous, noninvasive tracking of inflammation and complementing Sen-Jam’s broader goal of improving patient outcomes through better real-time physiological monitoring. I analyze existing literature and clinical biochemical data to determine which biomarkers most accurately reflect systemic inflammation in peripheral fluids. This includes evaluating key cytokines based on their detectability in sweat, relevance to inflammatory pathways, and potential for integration with electrochemical and optical sensor modalities. This allows the synthesis of findings from literature and datasets to establish benchmark concentration ranges and detection thresholds, while also highlighting the necessity of signal-processing for reliable data.
Continuous inflammatory profiling could support early detection of disease flare-ups, monitor recovery after treatment or injury, and provide clinicians with biomarker data that is otherwise limited to periodic blood draws. The same technology could enable users to track their own inflammatory states in real time, allowing for tailored interventions in response to stress, fatigue, or chronic conditions. Beyond diagnostics, the framework developed here could inform future therapeutics and digital health platforms that respond intelligently to a body’s biochemical signals, pushing medicine toward a more responsive and individualized model. I designed a succinct demo illustrating this concept, which can be found by clicking the “Conceptual Demo” button on this page.
Implantable bioelectronic fabric for gut-brain axis modulation
Bioelectronics Group, MIT
bioelectronics, neuroengineering, medical devices, optogenetics
The Bioelectronics Lab has pioneered the use of thermally drawn multifunctional fibers to interface with point populations of neurons in the brain. This project aimed to apply that technology to the gastrointestinal tract (GIT), which houses the enteric nervous system (ENS) and is particularly interesting for neural recording and stimulation experiments. Tissue is soft and dynamic in nature, so a neural interface device has to be: a) soft and flexible enough to not cause organ damage and scarring, b) functionalized with electrodes and LEDs along a two-dimensional surface, c) manufactured in a highly versatile approach that enables scalability and flexible shape configurations. These would allow for non-damaging stimulation of neurons involved in digestion and satiety. I focused on developing the fibers that make up the fabric, aiming to optogenetically modulate specific neural activity via stretch neurons in the GIT, which are connected to brain satiety centers through the gut-brain axis..
I spent a lot of time troubleshooting and iterating to figure out what worked best, and over time, some of the fibers I made were used in experiments to test stimulation setups and refine the design. It was a mix of mechanical, electrical, and biological work, and I got to work fairly independently after the first few months, which helped me get comfortable moving between tools, protocols, and ideas in the lab setting.
This was my introduction to academic research, so it holds a special place in my memory. I became comfortable using protocols and learned how to think through technical problems with a medical goal in mind. Looking back, this was where I started to see how engineering and medicine overlapped. I’m especially grateful to Rajib Mondal and Prof. Polina Anikeeva for their guidance and support.
The fiber (lit, and unlit), wrapped around a mouse stomach model
An early depiction of the fiber, under microscope