PNNL

Dr. Gosink’s research focuses on developing scalable statistical methods and visualization techniques that can characterize, detect, and predict complex events in large, high-throughput data. He has successfully applied these techniques across numerous application spaces including power systems, protein modeling, computer vision, subsurface modeling, and high-intensity physics experiments (e.g., to detect sub-atomic particles in the Belle II iTOP detector). His current research is focused on developing novel methods for making artificial intelligence systems more explainable, domain aware, and autonomous.

• Computer Science, PhD 2009 – University of California, Davis
• Chemistry, BS 1996 – University of California, Davis