Speaker: Prof. FAN Jinyan (Auburn University)Host: Prof. LIU Yukun (Zhejiang University)Venue: A723, School of Management, Zijingang CampusProfile of the speaker: Jinyan Fan earned a PhD in industrial/organizational psychology from Ohio State University in 2004. Prior to joining Auburn’s faculty, Fan taught for six years at Hofstra University in Long Island, New York. His research interests are in the domains of artificial intelligence, personnel selection, newcomer orientation and socialization, and cross-cultural adjustment and training. His work has appeared in premier outlets such as Journal of Applied Psychology, Journal of Management, Journal of Organizational Behavior, Psychological Assessment, among others. Dr. Fan has received several awards and research funds from SIOP and AoM. He was an Associate Editor at Journal of Vocational Behavior between 2019 and 2021. In addition, Dr. Fan has developed several talent assessment tools, models, and methods and has actively engaged in HR consulting with various organizations in the U.S. and Mainland China.

Speaker: Dr. FANG ZhouVenue: Room 102, Building 2, Haina Court, Zijingang CampusAbstract: Intracellular gene expression systems are inevitably random due to low molecular counts. Consequently, mechanistic models for gene expression should be stochastic, and central to the analysis and inference of such models is solving the Chemical Master Equation (CME), which characterizes the probability evolution of the randomly evolving copy-numbers of the reacting species. While conventional methods such as Monte-Carlo simulations and finite state projections exist for estimating CME solutions, they suffer from the curse of dimensionality, significantly decreasing their efficacy for high-dimensional systems. Here, we propose a new computational method that resolves this issue through a novel divide-and-conquer approach. Our method divides the system into a leader system and several conditionally independent follower subsystems. The solution of the CME is then constructed by combining Monte Carlo estimation for the leader system with stochastic filtering procedures for the follower subsystems. We develop an optimized system decomposition, which ensures the low-dimensionality of the sub-problems, thereby allowing for improved scalability with increasing system dimension. The efficiency and accuracy of the method are demonstrated through several biologically relevant examples in high-dimensional estimation and Bayesian filtering problems. We demonstrate that our method can successfully identify a yeast transcription system at the single-cell resolution, leveraging mRNA time-course microscopy data from real biological experiments, allowing us to rigorously examine the heterogeneity in rate parameters among isogenic cells cultured under identical conditions.Profile of the speaker: Dr. Zhou Fang is currently a postdoctoral researcher at ETH Zurich. He received a B.Sc. degree in Computational Mathematics from Zhejiang University in 2014. Later, he received a Ph.D. degree in Operational Research and Cybernetics from Zhejiang University in 2019. His research interest lies in the interface of mathematical control theory, computational sciences, probability, and biology. His primary goal is to develop computational methods and theoretical principles to unravel emergent phenomena in biology and facilitate the control and rational engineering of living cells.Contact person: Prof. GAO Chuanhou (gaochou@zju.edu.cn)

Speaker: Prof. GENG Weihua (Southern Methodist University)Venue: Room 105, Building 2, Haina Court, Zijingang CampusAbstract: This talk reports our recent development of numerical methods and software for the studies of electrostatic interactions of solvated biomolecules. The adopted mathematical models for the physical description of biomolecular electrostatics are the Poisson-Boltzmann model and the Poisson-Nernst-Planck model at various of details. The solutions to these models are numerically challenging due to long-range pairwise interaction, complex geometry, interface discontinuity, charge singularity, large-scale computing, etc. We designed and developed methods of fast summation, mesh generation, interface treatment, charge regularity, as well as parallel computing and machine learning to address these challenges.Contact person: Prof. ZHANG Qinghai (0015089@zju.edu.cn）

Speaker: Prof. LIU Yi (Peking University)Venue: Room 210, Building 2, Haina Court, Zijingang CampusNote: Please scan the QR code in the picture above to register.

Speaker: Prof. WANG Guozhen (Fudan University)Venue: Room 210, Building 2, Haina Court, Zijingang CampusNote: Please scan the QR code in the picture above to register.

Venue: C100, Building 5, Haina Court, Zijingang Campus

Speaker: Prof. FANG Bohan (Peking University)Venue: Lecture Hall, East Building 7, Zijingang Campus

Speaker: Yuming Zhao (Institute for Quantum Computing and the Department of Pure Mathematics, University of Waterloo, Canada)Location: Dingding Group "2023-Autumn Quantum Theory Group"Abstract: A multivariate polynomial is said to be positive if it takes only non-negative values over reals. Hilbert's 17th problem concerns whether every positive polynomial can be expressed as a sum of squares of other polynomials. In general, we say a noncommutative polynomial is positive (resp. matrix positive) if plugging operators (resp. matrices) always yields a positive operator. Many problems in math and computer science are closely connected to deciding whether a given polynomial is positive and finding certificates (e.g., sum-of-squares) of positivity.In the study of nonlocal games in quantum information, we are interested in tensor product of free algebras. Such an algebra models a physical system with two spatially separated subsystems, where in each subsystem we can make different quantum measurements. The recent and remarkable MIP*=RE result shows that it is undecidable to determine whether a polynomial in a tensor product of free algebras is matrix positive. In this talk, I'll present joint work with Arthur Mehta and William Slofstra, in which we show that it is undecidable to determine positivity in tensor product of free algebras. As a consequence, there is no sum-of-square certificate for positivity in such algebras. I will also discuss relevant topics including self-testing of quantum systems and delegation of quantum computation.Profile of the speaker: Yuming Zhao is a Ph.D. candidate in the Institute for Quantum Computing and the Department of Pure Mathematics at the University of Waterloo, supervised by William Slofstra. Previously, he received his bachelor's degree in Mathematics and Applied Mathematics from Zhejiang University in 2019, under the supervision of Junde Wu. He is broadly interested in the mathematics of quantum information and computation. Recent areas of study include quantum self-testing, delegation of quantum computation, complexity theory in operator algebras, and approximate representation theory.Contact person: Junde Wu (wjd@zju.edu.cn)

Speaker: Prof. YANG WeiHost: Prof. LI LeiVenue: Zijingang Hall, Alumni Building, Zijingang CampusProfile of the speaker: Professor Wei Yang received her doctorate degree from Columbia University in 1991. Her postdoctoral training was at Yale University with Dr. Thomas A. Steitzat, Department of Biochemistry and Molecular Biophysics. She joined the NIH in 1995 as an independent Investigator and currently holds the Distinguished Investigator position in the Laboratory of Molecular Biology, NIDDK, NIH.Dr. Yang was elected a Member of National Academy of Sciences (NAS) in 2013 and Fellow of American Academy of Arts and Sciences (AAAS) in 2015. Among the many prestigious awards, she is the recipient of the Bea Singer Young Investigator Award (GRC, 2002), the Dorothy Crowfoot Hodgkin award (The Protein Society, 2011), and Mildred Cohn Award in Biological Chemistry (ASBMB, 2017). She also held adjunct Professorship at Johns Hopkins University.Her research interests include Mechanisms of DNA repair, replication and recombination, macromolecular assemblies, structure and enzyme catalysis, ATP in molecular motion, recognition and signaling.