About Me:
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.
Education
University of California, Santa Barbara
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:
- Mathematical Modeling and Simulation
- Scientific Software Development
- Nonlinear Dynamics and Conrol
University of California, Santa Barbara
M.S. in Computer Science
Duration: Aug. 2023 – Present
Thesis: AI-Driven Drug Discovery
Advisors: Dr. Fredric Gibou
Focus Areas:
- AI for Drug Discovery
- ML for Signal Processing
- Data Driven Control Design
Sharif University of Technology
B.S. in Aerospace Engineering
Duration: Sep. 2016 – July 2021
Thesis: Biomedical Applications of Mechanical Micropumps
Advisor: Dr. Kaveh Ghorbanian
Focus Areas:
- Micro-Electro-Mechanical Systems(MEMS)
- Microfluidics
- Computational Fluid Dynamics
Research
Scientific Software Development
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.
Nonlinear Dynamics and Control for Neuroscience
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.
Micro-Fluidics and Computation
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.
Protein Aggregation Modeling
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 ResearchData-driven Control for Neuroscience
Developing 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 ResearchAI in Drug Discovery
Implementing 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 ResearchTeaching and Mentoring
Teaching
Introduction to Programming
Instructed intensive Matlab programming course for STEM undergraduates
Quarters: Summer 2024, Spring/Summer 2023, Summer/Fall 2022Mathematics of Engineering
Instructed numerical simulation for engineering problems and ODEs using Matlab for Mechanical Engineering major undergraduate students
Quarters: Summer/Fall 2024, Fall 2023, Spring 2022Basic Electronics and Circuits
Taught engaging lectures in electronics circuits, mentored students, and facilitated group projects.
Quarter: Winter 2023Dynamics
Taught fundamental principles of motion and forces in engineering
Quarter: Summer 2023Mentoring Positions
Career Mentor Fellow
Provide comprehensive career guidance to physics doctoral students, supporting their navigation through academic and non-academic career paths.
Graduate Division Mentor
Support first-year and second-year doctoral students from diverse backgrounds, facilitating their academic and professional development.
Women in STEM Organization Mentor
Mentor female undergraduate STEM students, providing academic support and fostering an inclusive learning environment.
Publications
Journal Publications
- F. Rajabi, F. Gibou, and J. Moehlis, "Optimal Control for Stochastic Neural Oscillators," PLOS Computational Biology, 2024. [Link]
Conference Publications
- Z. Rostami, F. Rajabi, and A. Shamloo, "Cell Separation by Using Active and Passive Methods Together," 4th International Conference on Innovative Technologies in Science, Engineering and Technology, Istanbul, Turkey, November 12, 2020.
- F. Rajabi, A. Bakhshi, and G. Kazemi, "Drug Delivery Applications of Mechanical Micropumps," International Conference on Applied Researches in Science & Engineering, Amsterdam, Netherlands, January 10, 2021.
Papers Under Review
- F. Rajabi, J. Fingerman, A. Wang, J. Moehlis, and F. Gibou, "CASL-ForgeX: A Comprehensive Guide to Solving Deterministic and Stochastic Hamilton-Jacobi Equations," Under review at Computer Physics Communications.
Presentations
- F. Rajabi, "A Level-Set Method Approach to Optimally Control Stochastic Neural Oscillators," Poster presented at the AAAS Pacific Division Conference 2024. [Poster]
