Yuanjun Zhou

Software Engineer

Princeton, New Jersey, United States16 yrs 8 mos experience
Highly Stable

Key Highlights

  • PhD in Physics with strong mathematical background
  • Expertise in first-principles computational methods
  • Proven track record in publishing research findings
Stackforce AI infers this person is a Research Scientist with a focus on computational physics and materials science.

Contact

Skills

Core Skills

First-principles Computational MethodsQuantum Monte CarloFirst-principles ComputationsData Management

Other Skills

quantum Monte Carlo impurity solverelectronic structuresstrongly correlated materialsSTACKING METHODHeisenberg modelTight-binding modelMongoDBPythonLinuxMathematical ModelingC++LaTeXResearchStatisticsMatlab

About

• Physics PhD with excellent academic records and solid background in mathematics/statistics. • Results driven problem solver with sophisticated knowledge of object-oriented programming, algorithm and data structure. • Outstanding communication skills and fast learner.

Experience

16 yrs 8 mos
Total Experience
3 yrs 11 mos
Average Tenure
10 mos
Current Experience

Netflix

Software Engineer

Aug 2025Present · 10 mos

  • Ads Member Experience

Google

Software Engineer

Jul 2021Aug 2025 · 4 yrs 1 mo · New York City Metropolitan Area

  • Instream Video Ads Serving and Quality for large publishers

Bloomberg lp

Software Engineer

Sep 2017Jul 2021 · 3 yrs 10 mos · Greater New York City Area

Columbia university in the city of new york

Postdoctoral Researcher

Oct 2015Sep 2017 · 1 yr 11 mos

  • Design and development of first-principles computational methods, especially the continuous-time quantum Monte Carlo impurity solver.
  • Applications of first-principles calculations onto studies of electronic structures of strongly correlated materials including iron-based superconductors, Mott insulators, and functional materials such as ferroelectrics and other transition-metal oxides.
  • Our results, published on top-tier journals, are not only essential in the discovery of barely studied paradigms in theoretical condensed matter physics, but also useful to explain the latest experiments and make predictions for further studies.
first-principles computational methodsquantum Monte Carlo impurity solverelectronic structuresstrongly correlated materialsquantum Monte Carlo

Rutgers university

2 roles

Research Assistant

Sep 2009Sep 2015 · 6 yrs

  • First-principles computations for structure-relevant electronic properties of materials.
  • Design and development of STACKING METHOD for the determination of superlattice structure.
  • Fit first-principles results to Heisenberg model, Tight-binding model for the understanding of spin-phonon interaction and Berry phase-based physics.
  • Active role in a Materials Genome Initiative project for the search of novel functional materials. Obtain data from Materials Project API. Collection and maintain group computational results using MongoDB.
  • Our results, published on top-tier journals, are important to understand and make predictions to the unknown superlattice and interface systems that are currently most exciting and least understood parts in condensed matter physics, contributing in novel functional materials discoveries.
first-principles computationsSTACKING METHODHeisenberg modelTight-binding modelMongoDBdata management

Teaching Assistant

Sep 2009May 2011 · 1 yr 8 mos

  • Taught undergratudate labs, recitations. Led discussions

Education

Rutgers University

Doctor of Philosophy (PhD) — Condensed Matter Physics

Jan 2009Jan 2015

Nanjing University

Bachelor of Science (BS) — Physics

Jan 2005Jan 2009

Stackforce found 18 more professionals with First-principles Computational Methods & Quantum Monte Carlo

Explore similar profiles based on matching skills and experience