Mehrdad Salimitari

Bio:

I am an AI/ML research scientist and engineer working at the intersection of deep learning, computer vision, and medical imaging, with a background spanning medical AI, autonomous driving, energy systems, and communication systems.

Currently, I am a Postdoctoral Fellow at the University of Maryland School of Medicine, in the Trauma Radiology Artificial Intelligence Lab (TRAIL), advised by David Dreizin, MD. I built and deployed a patented, end-to-end two-stage deep learning pipeline (DeepLabV3+ with mask-guided attention + Vision Transformer) for traumatic hemorrhage detection in 3D CT — now in production at the R Adams Cowley Shock Trauma Center. I also developed a fine-grained vision–language model for anatomy-level CT interpretation (0.86 mean F1, 0.88 AUC across 95 injuries) and a production LLM pipeline processing 8,700+ radiology reports, training at scale with PyTorch DDP and NVIDIA DALI on multi-GPU H100 servers.

Previously, as a Postdoctoral Researcher at the University of Denver, I designed multimodal deep learning models for grid-induced wildfire risk prediction and virtual power plants, fusing satellite imagery, weather, and grid data.

I received my Ph.D. in Computer Science from the University of Central Florida (UCF) under Prof. Yaser Fallah, focusing on computer vision and deep learning for autonomous driving and cooperative sensor perception. During my studies I also collaborated with the Center for Research in Computer Vision (CRCV) at UCF under Prof. Nazanin Rahnavard, developing sample/feature-selection algorithms for efficient deep learning.