Led research projects on foundation models of multimodal neural signals for disorders of consciousness.
Analyzed large-scale neural data such as LFP and spike signals and developed reproducible data processing pipelines.
Neuroscience Research Fellow
Albert Einstein College of Medicine, Bronx, NY
Trained neuroscience-inspired AI models of communication between brain areas.
Mechanistic interpretation of computer vision models with low-rank optimization.
Algorithms Research Intern
Enable Medicine, Menlo Park, CA
Applied AI methods to clinical genomics data, working towards drug discovery and patient outcome models at an early-stage startup.
Developed statistical and LLM-based algorithms for analyzing single-cell and spatial transcriptomics and proteomics data.
Computational Neuroscience Researcher (PhD)
Cold Spring Harbor Laboratory
Invented an unsupervised AI method for neuroscience data to classify sleep states.
Analyzed time-series and spatiotemporal data (EEG, LFP, calcium imaging) and theory for probabilistic deep learning models.
Managed SQL databases, group-wide codebases, and GPU servers; mentored interns and organized coding lectures.
Computational Neuroscience Research Intern
Cold Spring Harbor Laboratory
Computational Neuroscience Researcher
Stanford University, Stanford, CA
Trained and evaluated convolutional neural networks (CNNs) of the retina in TensorFlow and PyTorch.
Software Engineering Practicum Intern
Google, Mountain View, CA
Systems software engineering for deep learning model evaluation in C++ and Python.
Contributed to a production-scale end-to-end ML platform in TensorFlow Extended (TFX).
Education
PhD Computational Biology
Cold Spring Harbor Laboratory
PhD Candidate in Tatiana Engel lab (https://engel-lab.princeton.edu/). My thesis was on characterizing brain states with interpretable deep representation learning methods.