Gil Goldshlager

  • Program Year: 1
  • Academic Institution: University of California, Berkeley
  • Field of Study: Applied Mathematics
  • Academic Advisor: Lin Lin
  • Practicum(s): Practicum Not Yet Completed
  • Degree(s):
    B.S. Mathematics with Computer Science, Massachusetts Institute of Technology, 2017

Summary of Research

I am interested in developing computational methods for electronic structure, quantum chemistry, and the quantum many-body problem. I am especially excited about developing better quantum embedding algorithms and applying neural networks as ansatzes in variational Monte Carlo methods. I hope to develop methods that that can one day (hopefully in the not-too-far future) be leveraged to develop new materials and technologies that can help society more quickly and more cheaply bring climate change to a halt.

Publications

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation. Jeffmin Lin, Gil Goldshlager, and Lin Lin, December 2021, arxiv:2112.03491.

Faucet: streaming de novo assembly graph construction. Roye Rozov, Gil Goldshlager, Eran Halperin, Ron Shamir. Bioinformatics, Volume 34, Issue 1, 01 January 2018, Pages 147-154, https://doi.org/10.1093/bioinformatics/btx471.

Approximating kCSP for large alphabets. Gil Goldshlager and Dana Moshkovitz, 2015. Technical report available at http://people.csail.mit.edu/dmoshkov/papers/Approximating%20MAX%20kCSP.pdf.

Awards

Phi Beta Kappa recipient; 2017
Morais and Rosenblum Award for outstanding undergraduate research; May 2014
Research Science Institute (RSI) attendee for mathematics; Summer 2012
USA Math Olympiad Summer Program (MOP) attendee; Summer 2009
USA Junior Math Olympiad (USAJMO) winner; Spring 2009