About Me
Quantum chemistry is more than just equations and codes—it has the potential to be a transformative tool in fields like energy, pharmaceuticals, and materials. Yet, a gap exists between academic research and real-world applications, and I aim to bridge that gap to drive innovative solutions across these industries.
I am a research scientist at ByteDance Research. I specialize in developing advanced and scalable quantum chemical algorithms rooted in electronic structure theory for ab initio simulations on both high-performance classical and quantum computers. My experience in both creating and utilizing quantum chemical software allows me to translate theoretical concepts into powerful tools that deliver simulations with enhanced precision and efficiency. With deep expertise in electronic structure for chemistry and materials science, and extensive experience applying computational chemistry across key industries—such as quantum technology, energy, pharmaceuticals, chemical sciences, and materials—I bring a unique perspective on how quantum chemistry can enhance fundamental understanding, deliver high-quality data for AI4Science, and drive transformative innovations across industrial sectors.
During my Ph.D. at the University of Minnesota, Twin Cities, under the guidance of Professor Laura Gagliardi, I developed quantum embedding techniques based on density matrix and multiconfigurational methods for both molecules and periodic solids. My postdoctoral research at Columbia University with Professor David Reichman focused on advancing auxiliary-field quantum Monte Carlo techniques in quantum chemistry, aiming to broaden their application beyond conventional methods.
Projects
Scalable Quantum Embedding and Quantum Algorithms for Material Simulation
I have developed novel quantum embedding algorithms (DMET, PET, ASET, etc) designed for accurate and efficient ab initio simulations of materials on classical and quantum computer. My work has broadened quantum chemistry’s industrial impact across various sectors, including surface chemistry, homogeneous catalysis, superconducting materials, and defective systems as qubits.
Precise Quantum Monte Carlo for Quantum Chemistry
Leveraging locality and modern GPUs, I have developed advanced algorithms and applied AFQMC methods to scale quantum chemistry for large-scale, real-world applications while maintaining its predictive power.
Computational chemistry applied to Topological Insulator | Perovskites | MOFs | COFs
I have applied advanced computational techniques — periodic quantum chemistry, Wannier-based tight-binding models, and Grand Canonical Monte Carlo simulations — to study materials like reticular frameworks, topological materials, and photovoltaics. My research identifies new materials with exotic properties for potential industrial use.
Quantum Chemistry with GPU
Fast Quantum Chemistry on GPU
To position quantum chemistry as a key industrial technology, especially beyond widely-used DFT, speed is critical. Leveraging GPUs can accelerate quantum chemistry, making it more practical for real-world applications. I am focused on optimizing advanced methods, like random phase approximation and quantum embedding, to bring quantum chemistry closer to industrial use by harnessing GPU power
Experience
Institute for Computational Science and Technology
Research Assistant
Advisor: Nguyen-Nguyen Pham-Tran
Oct 2011 - Jul 2015
Education
A Little More About Me
I’m passionate about exploring science, especially when it comes to quantum topics and cool technology. I love discussing futuristic visions and staying on top of the latest innovations. On weekends, I recharge at EDM festivals, catching my favorite DJs. I balance being both an introvert and extrovert—focused and ambitious during the workweek, but always ready to relax with good music and great conversations. I’m also active on social media, where I enjoy sharing ideas about science and technology. Whether at a cocktail bar or a coffee shop, a cold brew always keeps me inspired.