About me

I am an Assistant Professor in the Industrial Engineering Department at the University of Pittsburgh. Before joining the University of Pittsburgh, I was a visiting postdoctoral researcher at Google in the Optimization and Algorithms group in New York. In 2019, I received my Ph.D. from the Department of Management Science and Engineering at Stanford University where Professor Yinyu Ye was my advisor.



My research focuses on continuous optimization, with a penchant for local optimization methods such as gradient descent. I aim 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.

You can contact me at ohinder at pitt dot edu.

Please see my scholar page for an up to date publications list.

My Ph.D. thesis was Principled Algorithms for Finding Local Minimizers.

Papers and talks

Grouped by topic

First order methods for convex optimization

Structured nonconvex optimization

The complexity of finding stationary points of nonconvex functions

Slides from my 2019 ICCOPT talk summarizing this body of work.

Machine scheduling and integer programming