Principal Investigator

Bren Professor in Chemistry

Garnet Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, and is concerned with quantum many-particle phenomena and the numerical methods to simulate them.

Staff Scientists

Xing obtained a B.S. from Nanjing University in 2011. He received his Ph.D. in 2016 studying under Prof. John M. Herbert, followed by postdoctoral work with Prof. Emily A. Carter. He Joined the Chan group in 2019, working on the development of PySCF.

Postdoctoral Scholars

Johnnie attended the University of Bristol for undergrad studies, before completing a PhD at University College London on a variety of quantum things supervised by Sougato Bose. He moved to the Chan group at Caltech in early 2020 to develop nice tensor network tools and methods for use across all sorts of areas.


Zuxin obtained his B.S. in physics from Tsinghua University, and received his doctorate in physical chemistry from University of Pennsylvania, under the supervision of Prof. Joseph Subotnik. His PhD research focused on molecular nonadiabatic dynamics near metal surfaces. He joined Prof. Garnet Chan's group in September 2021 as a postdoc and plans to works on quantum dynamics and nonequilibrium transport in molecular junctions.


Huanchen graduated with BSc in Physics (2015) from Shandong University, China. He received MSc (2017) and PhD in Chemistry (2019) from UCLA, in the group of Anastassia Alexandrova. His PhD research focused on developing structure and pathway global optimization methods for heterogeneous catalysis. He joined Chan group in December 2019 to work on extending Density Matrix Renormalization Group (DMRG) methods.


Seunghoon received his B.S. in Chemistry (2014) and PhD in Physical Chemistry (2019) from Seoul National University. In his PhD, he has developed a new linear-response spin-flip (SF)-TDDFT method and its analytic energy gradient using spinor-like open-shell orbitals for eliminating a spin-contamination problem of SF-TDDFT as well as studying photochemical properties of open-shell systems. He joined Prof. Garnet Chan’s group at Caltech as a postdoc in December 2019 and plans to work on developing electronic structure methods for simulating metallic clusters.


Johannes Tölle studied chemistry at the University of Münster.  During this time he spent six months in the group of Michele Pavanello at Rutgers University. He received his doctorate in the group of Johannes Neugebauer at Münster University in September 2021 for his work on subsystem-based modeling of photo-induced processes.
In the middle of March 2022 he joined the Chan group.


Kasra received his PhD in physics from UC Santa Barbara in 2021. During his graduate studies, his research was mainly focused on condensed matter theory and many body physics, with an emphasis on the physics of low dimensional quantum systems and nonequilibrium many body phenomena. In the final years of his PhD, he gradually changed his focus to problems at the interface of quantum information and condensed matter, such as the physics of quantum chaotic systems and random matrix theory, tensor network computations and quantum simulation of electronic properties of many body systems. He joined Garnet Chan’s group as a postdoc in January 2022, where he will be mainly working on quantum and classical computational methods for many body systems.


Wenyuan received PhD in physics from the University of Science and Technology of China in 2017. After that he was a postdoctoral scholar in Hong Kong, mainly mentored by Prof. Zheng-Cheng Gu. From the very beginning of his PhD period, he has been dedicated to developing efficient tensor network methods for simulating strongly correlated many-body systems. After a long time of exploration, he put forward tensor networks combining variational Monte Carlo sampling is an efficient and powerful approach. Such an approach is shown to provide very strong answers to some long-standing quantum many-body problems, giving solid results beyond DMRG for 2D systems. He joined Garnet Chan’s group in 2022, where he will be mainly working on tensor network related topics and simulating physical models of interest.


Or graduated from the Hebrew University of Jerusalem with a B.Sc. in physics, mathematics, and computer science, before completing his Ph.D. in machine learning under the supervision of Prof. Amnon Shashua. His work ranges from fundamental theoretical questions in machine learning, to applications in various domains, including computer vision and natural language understanding. In 2021 he joined Caltech, working jointly with Prof. Garnet Chan and Prof. Anima Anandkumar on methods at the intersection of machine learning and quantum many-body problems.

Appointment with Prof. Anima Anandkumar

Chenghan received his B.S. with honors in Chemistry from University of Science and Technology of China in 2016. He completed his Ph.D. in Physical Chemistry from the University of Chicago (UChicago) in 2021, under the supervision of Prof. Gregory A. Voth. At UChicago, he worked on various fields of computational chemistry problems, such as model developments of reactive molecular dynamics, enhanced sampling techniques, and applications to proton transport and solvation in aqueous, materials, and biomolecular systems. He joined Garnet Chan's group in 2021 and plans to develop and apply electronic structure methods in reaction dynamics of complicated condensed-phase systems.


Ke Liao completed his bachelor in physics at Wuhan university, along with an exchange year at King's College London. He then went to Max Planck Institute for Solid State Research in Stuttgart, Germany, to further pursue his masters degree in physics under the supervision from Prof. Andreas Grüneis. He obtained his PhD degree in chemistry at the same institute in 2021 under the joint supervision from Prof. Ali Alavi and Prof. Andreas Grüneis. Ke Liao's main research interests lie in the development and application of theories, such as coupled cluster, transcorrelation and QMC methods, for periodic solids. He joined Chan's group as a postdoc in May 2022, where he is going to study strongly correlated systems like high-T superconductors and develop novel wavefunction Ansätze. In spare time, he likes hiking, biking, playing badminton and travel, and he enjoys 2 cups of coffee each day.   


Yu Tong received his Ph.D. in Applied Mathematics from UC Berkeley, advised by Prof. Lin Lin. Before that he received his B.Sc. in Computational Mathematics from Peking University. He is interested in quantum algorithms, quantum information theory, and classical algorithms for quantum simulation, such as tensor network methods and quantum embedding theories. He joined Caltech in 2022 as an IQIM Postdoctoral Scholar.

Appointment with Prof. John Preskill

Zhihao received his BSc in Materials Chemistry from Peking University, China in July 2017. His undergraduate research included screened hybrid density functional and first-principles simulation of materials. Currently he is interested in strongly correlated materials and relevant electronic structure methods.


Junjie graduated from Nanjing University in China with a BS degree in chemistry, and spent two years in the University of Oklahoma for graduate research under the supervision of Prof. Yihan Shao. He then moved to Caltech and joined the Chan lab in September 2021 as a graduate student. His main interest is the electronic structure methods in the high temperature superconductors. He is an amateur astronomy who also enjoys outdoor hiking.


Matt graduated from Princeton in 2017 with a degree in Physics. A few months later he joined the Chan group at Caltech, deciding he had not yet had enough time to scribble excessively on blackboards. He loves running but hates walking, and he almost always wears Crocs to accompany his daily attire of slacks and a button-down shirt. Staying true to his contradictory nature, his research is focused on developing new high-accuracy quantum chemistry methods that don't rely on huge sets of traditional basis functions. To replace these trusty standbys in situations when accuracy is of the utmost importance, Matt seeks to harness the power of 2- and 3-dimensional tensor networks to do his bidding.

Graduate Students

In deep love with the Iowa cornfield, Linqing graduated from Grinnell College with a degree in chemistry and physics in 2019. She then moved to Caltech and subsequently joined the Chan group in December 2019. She is interested in strongly correlated materials and is adapting the Density Matrix Embedding Theory (DMET) framework to develop an accurate embedding method with correlated environment. While dedicated to theory development, she also enjoys running "chemistry experiments" by cooking and contact with realistic nature through hiking.


Gunhee graduated from the University of Cambridge in 2020 with a BA and MSc in Physics and joined the Chan group in March 2022. He is interested in developing tensor network methods for quantum dynamics. Outside of the lab, he enjoys lifting weights in the gym and cooking food.


Ruojing graduated with a BS in Chemistry from the Ohio State University in May 2019 and joined the Chan group in December. She is working on coupled-cluster related topics.


Rui obtained his BS in Chemistry from Zhejiang University, where he worked on semi-classical methods in quantum mechanics. Before joining the Chan Group, he was a student in the Miller Group. He never thought that group theory and tensors would light his motivation for exploring science again. Now he is excited about having some GPU implementation of the tensor network. Although he enjoys photography, he barely has any good photos to represent himself - a photographer cannot photograph himself anyway.


Rohit graduated from Princeton in 2019 with a degree in physics, and jumped to chemistry after joining the Chan group in 2021. His research is focused on studying bilayer graphene using the Density Matrix Embedding Theory framework. When he isn't doing research, he enjoys martial arts and never says no to a game of pool.

Administrative Coordinator