(4fk) Advancing Engineering, Biology, and Medicine through Cutting-Edge Computational Methods and Machine Learning

My future research group will develop and utilize state-of-the-art computational methods and machine learning techniques to advance the applications of microfluidic and nanofluidic systems in engineering, biology, and medicine. We will use a variety of computational tools such as computational fluid dynamics, Brownian dynamics, and molecular dynamics, and machine learning techniques such as graph neural networks and reinforcement learning to explore and leverage such complex systems. Furthermore, we often design and perform experiments for proof of concept and to bring our theoretical and computational understanding to life.

We will develop a group of precise techniques to accurately and efficiently move, orient, and place colloids and macromolecules to targeted locations in multidimensional domains. We will design and perform experiments to demonstrate important applications of our techniques in cargo loading and release, and separation of fine charged colloids from liquids (e.g., oil from water). We will also use our techniques for high-throughput enrichment of macromolecules, with notable applications in early cancer detection and development of vaccines and antiviral drugs. The key driving mechanism of our innovative techniques is time-varying electric fields, and can revolutionize the study of microscopic particles in fields such as biology, physics, and nanotechnology.

Additionally, we will focus on the theory and applications of nonlinear dynamical systems. Notably, we will leverage the temporal ratchets induced by unbiased vibrations in mechanical and stochastic systems to develop techniques for separation of materials based on mass and size, and to design and control micro-robots and nano-robots for drug delivery purposes.

Teaching Interests

I am deeply passionate about teaching and continuously strive to evolve as an educator, committed to nurturing the next generation of innovative scholars. I prioritize creating an engaging and inclusive classroom environment while employing diverse teaching methodologies to enhance student learning.

With extensive teaching experience, I currently serve as an assistant professor in the Department of Applied Mathematics at the University of Notre Dame, where I teach Methods of Applied Mathematics. In my previous appointment as an assistant professor at NYU Courant Institute, I taught courses including Introduction to Mathematical Modeling, Numerical Analysis, and Calculus. Additionally, throughout my academic journey from undergraduate to doctoral studies, I served as a teaching assistant for numerous courses in chemical engineering and mathematics.

I am qualified and willing to teach a wide range of engineering and math courses including mathematical modeling, engineering mathematics, transport phenomena, fluid mechanics, colloidal dispersions, thermodynamics, methods of applied mathematics, ordinary and partial differential equations, numerical analysis, computational methods, nonlinear systems, and complex plane analysis. Drawing from my rich background, I am dedicated to leveraging my expertise and skills to inspire and empower students, fostering a deep understanding of STEM concepts and their practical applications.