TL;DR

I am a fifth (and final) year PhD student in Bioengineering at the University of Pennsylvania, advised by Dr. Flavia Vitale, in the Center for Neuroengineering and Therapeutics. During my PhD, I have obtained extensive experience working with big datasets and developing machine/deep learning frameworks with applications in computational neuroscience and brain computer interfaces. My work has been published at top-tier AI conferences (NeurIPS) and journals (Science Translational Medicine).

Current Projects

Multi-patient neural decoding from sEEG. I am currently developing methods to enable cross patient neural decoding from stereotactic EEG data. Currently, neural decoding based on sEEG is designed to be patient-specific, due to the challenge of integrating the heterogenous data across patients, where each is implanted with a variable number of electrodes placed at distinct locations in their brain. Those single-patient approaches, however, are cumbersome, expensive, and don't scale. To combat this, I am leveraging the versatility of transformers to create unified models for sEEG decoding that generalize across patients, despite the number and placement of electrodes in each patient's brain. This approach enables me to combine datasets collected across multiple patients and medical centers, to train more expressive models on orders of magnitude more data. The goal of this work is to enable robust, high-performance neural decoding from sEEG in a scalable way. [Project Page]

Energy efficient neural decoding from microelectrode arrays. I am developing energy efficient neural decoding methods for microelectrode array based brain computer interfaces (BCIs). BCIs have revolutionized the quality of life of people suffering from neurological disorders by enabling them to control computers with their thoughts, ultimately restoring their digital freedom. While extremely helpful, most BCIs rely on artificial neural networks (ANNs) that are power hungry, requiring users to recharge their BCI every few hours, which is impractical and frustrating. To combat this, I am developing neural decoders based on spiking neural networks, which are very energy efficient compared to ANNs when deployed on neuromorphic hardware. The goal of this work is to enable high-performance, energy efficient neural decoding from microelectrode arrays, to enable day long BCI use without the frustrating need for frequent recharging.

Looking for full-time opportunities

I am looking for full-time opportunities starting September 2025. I enjoy tackling multidisciplinary challenges and aspire to work in the space of AI/ML with applications in neural interfaces, AR/VR, and/or finance.