Ke Liao
I build neural-network solvers for quantum many-body physics, writing high-performance JAX code that runs on GPUs to make ab initio simulation of strongly correlated electrons tractable. I am a Postdoctoral Research Associate at Yale University, working with Prof. Tianyu Zhu.
My recent work centers on neural quantum states and transcorrelation theory for solving high-dimensional electronic Schrödinger equations, and on quantum embedding to bridge electronic structure with realistic materials modelling. I lead the development of PyTC, a JAX-based framework for transcorrelated methods optimized for GPU acceleration (to be open-sourced soon), and contribute to neural-network ansätze that learn compact representations of strongly correlated wavefunctions.
My early exposure to industrial machine learning came as an external consultant at ByteDance, working at the intersection of quantum chemistry and ML — an experience that has shaped what I build today. I earned my Ph.D. in Quantum Chemistry from the Max Planck Institute for Solid State Research (Stuttgart) under Profs. Ali Alavi and Andreas Grüneis, and was subsequently a postdoctoral researcher at the California Institute of Technology with Prof. Garnet Chan. My work bridges high-accuracy electronic structure theory (Coupled Cluster, DMRG, transcorrelation) with modern ML frameworks, GPU computing, and the engineering discipline of building performant scientific software.
Selected work
- PyTC — JAX-based, GPU-accelerated framework for transcorrelated electronic structure methods. (in development, open source soon)
- TC-NQS — Neural quantum states combined with transcorrelation for strongly correlated systems. [preview repo]
- PyMES — Python framework for many-electron simulations; my long-running open-source electronic structure toolkit. [repo]
I am open to research and applied science roles in AI for science, ML research engineering, and scientific machine learning. Feel free to reach out via email.