Reza Akbarian Bafghi
PhD Candidate, University of Colorado Boulder
I am a PhD candidate in Computer Science at the University of Colorado Boulder, advised by Prof. Maziar Raissi. My research focuses on scientific machine learning and computer vision, particularly physics-informed modeling, efficient model adaptation, and self-supervised learning, with applications to physical and biomedical systems. I also work on LLM alignment and agentic AI systems for scientific computing and robotics. My work has been published in TMLR, Medical Image Analysis, CVPR, WACV, NAACL, and ICCAS, as well as ICLR and NeurIPS workshops.
I have interned at Cruise (autonomous driving perception) and AngioInsight (medical AI for coronary hemodynamics), and collaborated with Ford (automotive engineering) and Vinci4D (physics AI).
selected publications
selected open-source projects
PINNs-Torch
Physics-informed neural networks implemented in PyTorch, with a focus on speed and usability.
PINNs-TF2
A fast, user-friendly TensorFlow 2 implementation of physics-informed neural networks.
PINNs-JAX
A JAX implementation of physics-informed neural networks for composable scientific computing.
PEFT-ViT
Parameter-efficient fine-tuning of self-supervised vision transformers without catastrophic forgetting.
VizWiz Classification
A test set of images taken by blind people for evaluating ImageNet classification robustness.
Spotify Recommender
A music recommendation system built using the Spotify Million Playlist Dataset.