Experience
Graduate Student Researcher
University of California, San Diego
- Building forward probabilistic graphical model for fMRI data representation using a 11,500 subject longitudinal dataset
- Discovering main predictors of mental illness using machine/deep learning in the large-scale multimodal and longitudinal ABCD dataset utilizing behavioral, demographic, clinical measurement, imaging, and biological data
- Working to create a next generation biologically inspired AI neural network with biophysically meaningful parameters tunable for specific learning task
- Built a biophysically realistic stochastic 3D reaction-diffusion model for synaptic transmission using MCell software and Python scripts containing 120 molecular states
- Developed equivalent stochastic Markov chain synapse abstraction in Python with biologically tunable parameters, decreasing runtime by 93% and FLOPs by an order of magnitude for use in artificial neural networks models
- Optimized parameters to match biological conditions using parameter sweep techniques by running models on supercomputer clusters and analyzed subsequent large-scale datasets
Graduate Teaching Assistant
University of California, San Diego
Graduate teaching assistant for BENG 260 graduate level neurodynamics course and two quarters of BENG 1 Introductory Lab course for bioengineering undergraduates.
REU Intern
Tufts University
Working in the Tzanakakis lab on creating a Cell Ensemble Model for stem cell behavior utilizing both deterministic and stochastic aspects to predict events in response to intrinsic and extrinsic changes in the system.
Software Quality Assurance Intern
Hologic, Inc.
Created and executed formalized testing for mammography device software and technology; collaborated with the Software Engineering team