Mohammad Arvan

As a computer scientist, my research centers on the intersection of performance and reliability in machine learning. I specialize in the foundational elements of robust AI, including High-Performance Computing (HPC), system optimization, and scalable model design. These technical pursuits are guided by a primary focus on rigorous evaluation and scientific reproducibility, which I consider essential for advancing the field.

I translate these technical standards into the applied domain of healthcare. My work in algorithmic phenotyping has led to models that close care gaps in cancer screening, streamline hospital discharge planning, and shorten the diagnostic odyssey for patients with rare diseases like hEDS. These projects are the concrete result of applying robust computational standards to real-world problems.

I earned my Ph.D. in Computer Science from the University of Illinois at Chicago, where I was mentored by Dr. Natalie Parde.