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.
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.
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.
Tomislav completed his bachelor in chemistry in 2015 at the University of Zagreb, Croatia, before moving to the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, where he obtained his masters (2017) and PhD (2021) degrees in chemistry. There, he focused on developing efficient semiclassical methods for computing steady-state, pump-probe, and two-dimensional electronic spectroscopy. He then moved to Pasadena to work with Thomas Miller and Geoffrey Blake at Caltech as an Early Postdoc Mobility Fellow of the Swiss National Science Foundation (SNSF). During this time, he developed tools for simulating two-dimensional vibrational spectra beyond conventional classical molecular dynamics methods. In March 2023, he joined the Chan group, where he will focus on approximate theories for efficient computation of complex quantum systems.
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.
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.
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.
Appointment with Prof. Anima Anandkumar
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.
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.
Appointment with Prof. John Preskill
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.
Fascinated by the world of quantum and statistics, Verena completed a PhD in stochastic quantum chemistry in Cambridge, UK. During her first postdoc at Columbia University, she used and adapted quantum chemistry to tackle problems involving metallic solids. Now, she will continue to work on achieving reliable accuracy in materials simulations as well as looking into the possibilities of tensor networks. She is also very keen on DEI efforts.
Shuoxue graduated from Peking University in China with a BS in chemistry, where he conducted undergraduate research on theoretical methods towards excited states of photoluminiscent materials. He then joined the Chan group in December 2022, and his current research interest is on the development of wavefunction method in Density Matrix Embedding Theory (DMET). Apart from research, he is a deep lover of Peking Opera and Cantonese Opera, an amateur of piano and a rookie of violin.
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.
Chris received his BS from UC Irvine majoring in Physics and Computer Science. He then joined the Chan group at Caltech in 2022. He is generally interested in tensor network methods and quantum information theory. When he's stressed he takes a boba break with friends.
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.
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.