I am a Ph.D. student in Mechanical Engineering at UC Santa Barbara's Computational Applied Science Laboratory (CASL), working under the supervision of Dr. Fredric Gibou and Dr. Jeff Moehlis.
My research develops computational methods for complex biological systems through distinct approaches: stochastic control strategies for neural oscillator networks with applications in Parkinson's disease treatment, Level Set Methods for solving high-dimensional Hamilton-Jacobi equations, and machine learning frameworks for biological systems. This work combines numerical methods, scientific computing, and machine learning to create novel tools for both theoretical understanding and practical applications.
Additionally, I'm pursuing an M.S. in Computer Science, strengthening my expertise in scientific computing and machine learning. Through this interdisciplinary approach, I aim to advance computational tools for understanding and controlling complex biological systems, with applications spanning from neuroscience to biotherapeutics.
Ph.D. in Mechanical Engineering
Duration: Jan. 2022 – Present
Thesis: Advanced Computaional Methods for Biological Systems
Advisors: Dr. Fredric Gibou and Dr. Jeff Moehlis
Focus Areas:
M.S. in Computer Science
Duration: Aug. 2023 – Present
Thesis: AI-Driven Drug Discovery
Advisors: Dr. Fredric Gibou
Focus Areas:
B.S. in Aerospace Engineering
Duration: Sep. 2016 – July 2021
Thesis: Biomedical Applications of Mechanical Micropumps
Advisor: Dr. Kaveh Ghorbanian
Focus Areas:
CASL-ForgeX: An advanced computational framework for solving nonlinear stochastic PDEs, specializing in Hamilton-Jacobi-Bellman equations using level set methods. Applications span neuroscience, engineering, finance, and machine learning.
Developing energy-efficient, event-based control strategies for neural networks using stochastic optimal control. Our approach incorporates system noise into deterministic models, achieving significant network desynchronization while minimizing energy consumption.
Investigating cell separation using active and passive methods, along with drug delivery applications in mechanical micropumps. Research focuses on enhancing separation efficiency and optimizing drug delivery mechanisms in.mechanisms in MEMS devices.
Developing novel computational models for protein aggregation in high-concentration biotherapeutics. Our approach bridges microscopic and macroscopic scales using continuum field representations and level-set methods to predict long-term stability in drug formulations.
Ongoing ResearchDeveloping machine learning-based approaches for adaptive Deep Brain Stimulation (DBS) in Parkinson's disease. Our research utilizes artificial neural networks to predict bursting events and modulate beta band rhythms in the basal ganglia.
Ongoing ResearchImplementing machine learning approaches to accelerate drug discovery pipelines. Focusing on deep learning models for molecular property prediction, protein-ligand interaction analysis, and drug candidate screening.
Ongoing ResearchInstructed intensive Matlab programming course for STEM undergraduates
Quarters: Summer 2024, Spring/Summer 2023, Summer/Fall 2022Instructed numerical simulation for engineering problems and ODEs using Matlab for Mechanical Engineering major undergraduate students
Quarters: Summer/Fall 2024, Fall 2023, Spring 2022Taught engaging lectures in electronics circuits, mentored students, and facilitated group projects.
Quarter: Winter 2023Taught fundamental principles of motion and forces in engineering
Quarter: Summer 2023Provide comprehensive career guidance to physics doctoral students, supporting their navigation through academic and non-academic career paths.
Support first-year and second-year doctoral students from diverse backgrounds, facilitating their academic and professional development.
Mentor female undergraduate STEM students, providing academic support and fostering an inclusive learning environment.