About Me
Quantum chemistry isn’t just equations and code — it’s a gateway to revolutionize energy, pharmaceuticals, and materials. I’m passionate about closing the gap between academic research and real-world impact, crafting innovative tools and solutions that transform industries.
I am a research scientist at ByteDance Research, where I design cutting-edge quantum chemical algorithms grounded in electronic structure theory. My work delivers scalable, high-precision ab initio simulations on both classical high-performance systems and emerging quantum computers. With a knack for turning complex theory into practical software, I’ve optimized simulations—like scaling computations to over 20,000 orbitals and achieving 10–100x speed-ups—pushing the boundaries of efficiency and accuracy. My expertise spans computational chemistry and materials science, with applications in quantum technology, renewable energy, pharmaceuticals, and beyond, driving high-quality data for AI+Science and sparking transformative industrial innovations. During my Ph.D. at the University of Minnesota, Twin Cities, under Professor Laura Gagliardi, I pioneered quantum embedding techniques using density matrix and multiconfigurational methods for molecules and periodic solids. As a postdoctoral researcher at Columbia University with Professor David Reichman, I advanced auxiliary-field quantum Monte Carlo methods, expanding their reach with novel algorithmic breakthroughs.
Years of tackling computational science have forged my diverse skill set in software development, mathematical modeling, convex optimization, and data analysis, sharpened on HPC and cloud platforms. I’ve built numerical models and simulations for quantum chemistry, wielding both deterministic and stochastic algorithms to span classical and quantum realms—a journey that’s honed my research prowess and given me a wide-angle view of complex systems. This versatile toolkit, rooted in rigorous problem-solving, seamlessly transfers to software engineering, data science, and quantitative research, where I’m poised to drive innovation and deliver results.
Projects
Applied Quantum + AI
From Science to Finance
I’m boldly diving into AI/ML applications, leveraging techniques such as classical ML, deep learning, and large language models to revolutionize materials discovery through high-fidelity quantum chemical data, while also exploring their potential to address FinTech challenges in portfolio optimization, risk management, and financial modeling. Through independent side projects, I’m honing a dynamic skill set in computational science, numerical simulations, high-performance computing, and statistical algorithms. This expertise is primed for quantum technologies, data science, and AI-driven innovation across industries.
Scalable Quantum Embedding and Quantum Algorithms for Material Simulation
I have contributed to developing quantum embedding algorithms for efficient, 1 kcal/mol-accurate ab initio simulations of strongly correlated and metallic systems on classical and quantum platforms, advancing quantum chemistry applications in areas like catalysis, superconductors, and quantum computing. My work has honed a versatile skill set, including mathematical modeling (with expertise in linear algebra), scientific computing, and software engineering—spanning theory to production code in Python, C/C++, and legacy Fortran. These skills, bridging theoretical insights and practical implementation, are highly transferable to quantum technologies and data science, enabling impactful solutions across diverse challenges.
Large-scale Quantum Monte Carlo
Leveraging locality and modern GPUs, I have contributed to developing advanced algorithms and applying AFQMC methods, enabling scalable, chemically accurate quantum chemistry simulations for large systems, including strongly correlated molecules and metallic surfaces. This work has expanded my expertise into stochastic methods, particularly Monte Carlo simulations, which are fundamentally distinct from deterministic quantum chemistry approaches.
Computational chemistry applied to Topological Insulator | Perovskites | MOFs | COFs
I have applied advanced and customized computational techniques and analysis based on quantum chemistry, including periodic quantum chemistry and DFT, Wannier-based tight-binding models, and Grand Canonical Monte Carlo simulations, to investigate materials such as reticular frameworks, topological systems, and photovoltaics, identifying novel materials with unique properties for industrial applications. This work has strengthened my versatile skill set in bringing theories to actual problems where innovative solutions are needed to solve these fundamental application challenges.
GPU-Accelerated Quantum Chemistry
Fast Quantum Chemistry on GPU
To elevate quantum chemistry as a vital industrial technology beyond standard DFT, computational speed is essential. I have contributed to accelerating quantum chemistry by leveraging GPUs, enhancing its practicality for real-world applications. My efforts focus on optimizing advanced methods, such as random phase approximation and quantum embedding, harnessing GPU power to drive industrial adoption.
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.