Oliver Hinder

Assistant Professor
Website Google Scholar Industrial Engineering


Dr. Oliver Hinder's research focuses on continuous optimization, with a penchant for local optimization methods such as gradient descent. Dr. Hinder aims to develop reliable and efficient algorithms built on solid mathematical foundations. This research is motivated by applications in network optimization and machine learning that push the limits of current computational capabilities. Before joining the University of Pittsburgh, Oliver was a visiting postdoctoral researcher at Google in the Optimization and Algorithms group in New York. In 2019, he received his Ph.D. from the Department of Management Science and Engineering at Stanford University under the supervision of Professor Yinyu Ye. Oliver teaches courses on data science (IE 2064), and advanced nonlinear optimization (IE 3080), and will soon be offering a new course "introduction to optimization for machine learning".


PhD, Management Science and Engineering, Stanford University, 2019

Bachelor of Engineering with First Class Honors, Operations Research, University of Auckland, 2013