Currently, I'm a Ph.D. student and graduate research assistant in the school of mechanical, aerospace and manufacturing engineering at UConn. My research is focused on the prognostics and health management (PHM) of complex engineering systems like lithium-ion batteries through physics-informed machine learning.
My journey into this field started with robotics, where I first got hooked on programming and problem-solving. Later, during my master’s program, I was drawn to operations research and mathematical modeling. Discovering gradient descent led me to machine learning—and now here I am, diving deep into it!
These days, I’m hands-on with Python, MATLAB, and a range of data science and simulation frameworks to tackle complex problems and optimize solutions. Outside of research, you’ll find me climbing rocks, exploring coffee spots, or catching up on the latest in tech podcasts!
📫 Reach out at sina.navidi@uconn.edu for collaborations, questions, or just to connect!
Check out my latest work on Google Scholar.