I’m Bowei (Jacky) Ouyang, a Postdoctoral Research Scientist at the University of Pittsburgh Department of Psychiatry, where I apply machine learning and computational methods to large-scale medical imaging and neuroscience problems. My background spans applied mathematics and statistics, with a PhD from Pitt and hands-on experience building deep learning systems — from Graph Convolutional Networks for disease classification to 3D CNNs for early detection of neurodegenerative risk.
My work sits at the intersection of AI and clinical impact: I care about building models that are not just technically sound but practically deployable, whether that means optimizing real-time MRI inference pipelines or architecting scalable data frameworks for terabyte-scale datasets. I’ve also contributed to transformer-based navigation algorithms, LLM-powered platforms, and interactive visualization tools that make research more efficient and collaborative.
I enjoy bridging the gap between rigorous methodology and real-world application and communicating complex work clearly across technical and non-technical audiences. I’m currently focused on neuroimaging and brain state dynamics, with a strong interest in how AI can accelerate psychiatric research and improve patient outcomes.
