PhD ML researcher — grad math, machine learning & scientific comp

Partha T.

I am a Computer Science PhD candidate at UC Riverside, where I specialize in machine learning and the mathematics behind modern AI, including probability, linear algebra, optimization, and diffusion models. I hold a Master of Science in Computer Science with a 3.82 GPA, and I earned A and A+ grades in Machine Learning, Artificial Intelligence, Scientific Computing, GPU and Parallel Programming, and the Design and Analysis of Algorithms. I have also completed the Machine Learning and Deep Learning Specializations from Coursera and have authored machine learning research, including work on diffusion-based generative models.

My teaching experience comes from serving as a teaching assistant at UC Riverside and mentoring students in research and scientific computing. Before beginning my PhD, I spent four years working as a software engineer, so I am comfortable connecting mathematical theory to real, working code rather than treating it as abstract formulas. This lets me help students not only understand the math on paper but also implement it in Python and PyTorch for their assignments and projects.

I tutor graduate and upper-division students in linear algebra, probability and statistics for machine learning, single and multivariable calculus, matrix and vector calculus, machine learning and deep learning, and scientific and numerical computing. My approach is to first find the exact gap that is blocking a student, whether it is a missing definition, a shaky intuition, or a bug in their code, and then rebuild understanding from that point. We work through your actual assignments, derivations, and problem sets together, and my goal is always to make sure you can solve the next problem on your own. Sessions use screen sharing and a live shared whiteboard, so please send me your syllabus or the concept you are stuck on and I will come prepared.