Research
Mount Sinai
I currently work on applications of machine learning to neural signal processing, behavioral neuroscience, and functional genomics.1 In parallel, I am building foundation models for genomic DNA sequence-based tasks. Examples of my work in these areas include:
- Domain-generalized deep learning for detecting neuronal spikes in whole-cell patch-clamp postsynaptic current traces
- Forecasting lab animal behavioral events with computer vision and sequence modeling
- Predicting neuroanatomical sources of detailed electrophysiology spike traces2
- Benchmarking of self-supervised learning methods for mammograms
Other
In addition to my work at Mount Sinai, I have some additional research interests:
Functional Bacterial Genomics & Synthetic Microbiology
Building an equivalent of AlphaGenome for bacterial genomes. Can we predict functional properties of microorganisms from their DNA genomes? Can we infer their metabolic reaction networks? And building on this, can we propose genome edits to have desired functional effects?
AI Governance & Legal Frameworks for AI
Copyright law and computational frameworks for dealing with new challenges posed by generative AI. What constitutes fair use? What new paradigms of patent law are enabled by large AI systems?
Brain-Computer Interfacing, Neuroprosthetics, Neurorehabilitation
Neural decoding for robotic actuation and prosthetic control. Closed-loop Sim2Real neural decoding and spinal cord stimulation for restoration of limb control in paralysis.