Scholar
Oliver Hinder
Google Scholar ID: FiBMfBsAAAAJ
Assistant Professor, Industrial Engineering Department, University of Pittsburgh
Citations & Impact
Citations
1,542
H-index
14
i10-index
20
Publications
20
Co-authors
13
list available
Contact
Publications
Resume (English only)
Research Experience
- Visiting postdoctoral researcher at Google in the Optimization and Algorithms group in New York.
- Collaborated with DeepMind to develop state-of-the-art techniques for neural network certification.
- Research on nonconvex interior point methods inspired Hexaly's implementation.
- Work on first-order methods for linear programming is part of the Google OR-Tools package.
- GPU variants of his methods have been developed by NVIDIA and COPT.
Background
- Assistant Professor in the Industrial Engineering Department at the University of Pittsburgh.
- Research focuses on continuous optimization, with a penchant for local optimization methods such as gradient descent.
- Aims to develop reliable and efficient algorithms built on solid mathematical foundations.
- Goal is to build new optimization tools for operations research and machine learning.
- Work spans from fundamental optimization theory to software development, including general-purpose solvers and application-specific solvers for drinking water networks, electric grids, and certifiable deep learning.
