Education

Ph.D. in Physics

University of California, Berkeley

Focus area: condensed matter theory, many-body theory, strongly correlated systems

Advisor: Prof. Ashvin Vishwanath

2011 - 2017, GPA: 4.0/4.0

Dissertation: “Quantum Phenomena in Interacting Many-Body Systems: Topological Protection, Localization, and Non-Fermi Liquids.”

B.A. in Physics, B.A. in Mathematics

University of California, Berkeley

Graduated with highest honors (summa cum laude)

Valedictorian of physics department (Awarded to one graduating student for highest achievement in courses and research)

Thesis: “Scaling Analysis of Quantum Spin Chains”

2006 - 2010, GPA: 4.0/4.0

Selected Honors & Recognition

• Les Houches School of Physics, Invited Lecturer, 2022 

• Rising Stars Award in EECS (UC Berkeley), 2020

• NSF Graduate Research Fellowship (Physics), 2012 - 2015

• Highest scores on entrance exams, Physics Dept. Entering Class, 2011

• Departmental Citation Award, Physics, UC Berkeley, 2010

Recognition in press: Quanta magazine article, Simons Foundation Article 

Experience / Employment

• Research Scientist, Google Research (Brain Team), 2019 - present

• Visitor in Program on Foundations of Deep Learning (Summer 2019), Simons Institute for the Theory of Computing (UC Berkeley)

• Google Research, Brain Residency Program, 2017 - 2018

• Doctoral Research, Condensed Matter Theory (CMT) Group, UC Berkeley Physics. Advised by Prof. Ashvin Vishwanath, 2012 - 2017.

• Undergraduate Honors Thesis Research, CMT Group, UC Berkeley. Advised by Prof. Joel Moore, 2009 - 2010.

Selected Seminars & Workshops

In machine learning programs:

• Seminar, MIT EECS Department. Mar 2022.

• Yale Institute for Network Science Seminar. Oct 2021.

• Mathematics of Machine Learning Conference, Max-Planck Institute for Mathematics in the Sciences. Aug 2021.

• CMU Scientific Machine Learning Seminar series. Jul 2021.

• 10th World Congress on Probability and Statistics, invited session on deep learning. Jul 2021.

• TRI Machine Learning Seminar. Jun 2021.

• Caltech CS 159 (Advanced Topics in ML) Guest Lecture. May 2021.

• Flatiron Institute (NYC) Computational Methods Seminar. Feb 2021.

• UCLA Department of Computer Science Seminar. Jan 2021.

• Mathematical Machine Learning Seminar (Max Planck Institute & UCLA). Invited seminar. Nov 2020.

• UIUC, Institute for Data Science and Dynamical Systems. Invited seminar. Nov 2020.

• Machine Learning and Science Forum at UC Berkeley. Invited seminar. Oct 2020.

• Harvard University, Institute for Applied and Computational Science. Invited seminar. Sep 2020.

• Simons Institute for the Theory of Computing (UC Berkeley), Deep Learning Reunion. Invited speaker. Aug 2020.

• First Workshop on Scientific-Driven Deep Learning. Invited speaker. Jul 2020.

• NeurIPS 2019, Science Meets Engineering of Deep Learning Workshop. Invited speaker and panelist. Dec 2019.

• NeurIPS 2019, Machine Learning and the Physical Sciences Workshop. Invited speaker. Dec 2019.

• Conference on the Mathematical Theory of Deep Neural Networks (DeepMath), NYC. Invited speaker. Oct 2019.

• Simons Institute for the Theory of Computing (UC Berkeley), Workshop ‘‘Frontiers of Deep Learning Workshop." Invited speaker. Jul 2019.

• Harvard University Physics of Living Systems, NSF Workshop. Invited speaker. Dec 2018.

• Fermi National Accelerator Laboratory. Invited seminar. May 2018.

• Toyota Technological Institute at Chicago (TTIC). Invited seminar. May 2018.

• UC Berkeley, Redwood Center for Theoretical Neuroscience. Invited seminar. Apr 2018.

• Google Research NYC, Machine Learning Seminar. Aug 2016.

In physics programs:

• Invited Speaker, APS March Meeting (Upcoming Mar 2023).

• Kid's CMT Seminar, Harvard Department of Physics. (Upcoming Nov 2022).

• Colloquium, NSF Institute for AI and Fundamental Interactions. Nov 2021.

• UC Berkeley Department of Physics. Condensed Matter 290K seminar. Sep 2021.

• Brown University Department of Physics. Invited colloquium. Oct 2020.

• UC Berkeley Department of Physics. Annual Chern-Simons Workshop. Invited speaker. Mar 2020.

• Perimeter Institute for Theoretical Physics. Invited seminar. Mar 2020.

• Kavli Institute for Theoretical Physics (KITP) Conference, At the Crossroad of Physics and Machine Learning. Invited speaker. Feb 2019.

• KITP Teacher's Conference. Invited speaker. Feb 2019.

• Aspen Center for Physics, Theoretical Physics for Machine Learning Conference. Invited speaker. Jan 2019.

• Harvard University Department of Physics. Invited seminar. Dec 2018.

• Harvard University Department of Physics. Invited seminar. Jun 2017.

• American Physical Society March Meeting (multiple contributed talks), 2013, 2014, 2015.

• Quantum Computation and Information Center, UC Berkeley. Invited seminar. Oct 2014.

• Workshop participant, “Topological Mechanics: from Metamaterials to Robots.” Lorentz Center for Theoretical Physics, Leiden University. Oct 2014.

• Workshop participant, “Many-body Localization and Associated Theory.” Princeton Center for Theoretical Science. Apr 2014.

• Summer school participant (by admission). “Disorder and Dynamics in Quantum Systems.” Boulder Summer School for Condensed Matter and Materials Physics, Jul 2013.

• Conference participant, “Bose-Einstein Condensation: Frontiers in Quantum Gases.” Sant Feliu de Guixols, Spain. Sep 2013.

Teaching & Mentorship

I've served as teaching assistant for the following courses at Berkeley:

• Physics 212 (Advanced Graduate), Phase Transitions and Renormalization Group

• Physics 216 (Advanced Graduate), Quantum Many-Body Physics

• Physics 211 (Graduate), Equilibrium Statistical Mechanics

• Physics 8A (Undergraduate non-major), Introductory Mechanics

Mentorship & invited lectures:

• Invited Lecturer, 2022 Summer School through NSF Institute for Artificial Intelligence and Fundamental Interactions

• Research internship mentor (Gal Kaplun, Ph.D. candidate at Harvard)

• Research internship co-mentor (Jiri Hron, Ph.D. candidate at Cambridge University)

• Mentor, Depth First Learning Program & OpenAI Scholars Program

Selected Service

• Co-organizer, KITP program on "Deep Learning from the Perspective of Physics & Neuroscience," Winter 2023.

• Co-organizer, Workshop on ‘‘Overparameterization: Pitfalls & Opportunities," ICML 2021.

• Co-organizer, Workshop on ‘‘Theoretical Physics for Deep Learning," ICML 2019.

• Lecturer, Kavli Institute for Theoretical Physics (KITP) Teacher's Conference 

• Reviewer for NeurIPS, ICLR, ICML, Physical Review Letters, Physical Review B, Nature Machine Intelligence