David Ellis Hershkowitz

Hi there!

I am a fourth year PhD student at Carnegie Mellon's School of Computer Science where I am fortunate to be advised by Bernhard Haeupler.

I enjoy algorithmic and combinatorial aspects of theoretical computer science with a graph-theoretic bent. More specifically, I have worked in approximation algorithms for graph problems, distributed graph algorithms and theoretical aspects of machine learning, especially abstraction in reinforcement learning.

News

• I presented our paper Computation-Aware Data Aggregation at Innovations in Theoretical Computer Science (ITCS) 2020. I also took notes at many of the talks; see here.
• I attended the International Symposium on Distributed Computing (DISC) 2019 and took notes; see here.
• I attended the Workshop on Advances in Distributed Graph Algorithms (ADGA) 2019 and took notes; see here.
• I attended APPROX/RANDOM 2019 and took notes at the talks I attended; see here.
• I won the DISC 2019 Best Review Award. Thanks DISC!
• I'll be presenting our paper "Prepare for the Expected Worst: Algorithms for Reconfigurable Resources Under Uncertainty" at APPROX 2019.
• Our paper "Erasure Correction for Noisy Radio Networks" was accepted to DISC 2019.
• An NSF grant I helped write---Distributed Optimization Beyond Worst Case Topologies---was funded.