INDUSTRIAL ENGINEERING
Oliver Hinder, PhD
1033 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261
Assistant Professor
Biographical Sketch Oliver Hinder joined the Industrial Engineering Department in the fall of 2020. Prior to that he was a visiting postdoctoral researcher at Google in the Optimization and Algorithms group in New York. In 2019, he received his PhD from the Department of Management Science and Engineering at Stanford University under the supervision of Professor Yinyu Ye. Oliver is has won the PACCAR Inc fellowship, the Dantzig-Lieberman fellowship, and Mascaro Center for Sustainability faculty fellowship.
ohinder@pitt.edu
Research Overview Dr. Hinder aims to develop reliable and efficient optimization 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. Dr. Hinder’s work ranges from fundamental theoretical questions on the computational limits of optimization algorithms to the development of practical software for machine learning and operations research.
Research Projects • Making stochastic gradient descent more user friendly. The basis for modern machine learning is stochastic gradient descent. Unfortunately, stochastic gradient descent is not very user friendly: users must tune its learning rate which can be a painful and time-consuming process. The goal of this work is to develop automatic methods for tuning the learning rate of SGD. • Algorithms for the control of modern electric grids. As our electricity grids involve more and more renewable energy, energy supply becomes less predictable and controllable. These changes warrant the development of new algorithms that are better able to handle uncertainty and long planning horizons than traditional methods. • Neural network certification. It is well-known that neural networks are susceptible to small perturbations to their input leading to wildly incorrect predictions. While heuristics have been developed to reduce this susceptibility, it difficult to certify that the resulting networks are indeed robust to perturbations. This work develops new techniques for certifying neural networks. • First-order methods for large-scale linear programming. Linear programming is a key tool for operations research, finding uses in supply chains, blending materials, airline operation, network management and more. Classical methods for linear programming (Simplex and interior point methods) are based on factorizations which can run out of memory in large-scale instances. Our methods basic operation is matrix-vector multiplication which can scale to larger problems.
Industry Impact Oliver’s research has had direct impact on industry: his research on nonconvex IPM inspired LocalSolver’s own implementation, his work on first-order methods for linear programming is soon to be part of the Google Operation Research tools package, and his work with DeepMind has led to state-of-the-art techniques for neural network certification.
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DEPARTMENT OF INDUSTRIAL ENGINEERING
