He is interested in probability and combinatorics including randomized algorithms, random graphs and random tree, in particular: Galton-Watson trees; binary search trees and split trees; random directed graphs; peer-to-peer computer networks; and graph coloring. He also likes programming and using computers to experiment, prove, and teach mathematics. He has taught courses in applied mathematics and computer science, such as calculus, combinatorics, discrete mathematics, and algorithms. Cai has an M.Sc. and a Ph.D. from McGill University, Montreal. Before joining Duke Kunshan, he was a postdoctoral researcher at Uppsala University, Sweden, from 2016-20.

His research focuses on mathematical modeling and computation in quantitative finance and efficient numerical algorithms for partial differential equations and integral equations arising in science and engineering. Chen has a B.Sc. in financial mathematics from the University of Michigan, Ann Arbor, and a Ph.D. in applied mathematics from the University of North Carolina, Chapel Hill. He held postdoctoral positions at the University of California, Berkeley, from 2017 to 2021.

Dr. Song Gao received his B.S. in Materials Science (Chemistry Track, with Honors) at the University of Science and Technology of China (USTC), Ph.D. in Analytical/Environmental chemistry at the University of Washington – Seattle, and then received postdoctoral training in Atmospheric Chemistry at the California Institute of Technology (Caltech).  Prior to joining DKU, he had served as a chemistry faculty member in Hong Kong University of Science and Technology (HKUST) as well as Stetson University in the US. His current research interests include: Molecular-level atmospheric chemistry, especially secondary aerosol formation and ozone chemistry; Particle nucleation involving organics using numerical methods such as DFT; Regional air pollution mechanisms and control measures; Water remediation and mud removal technologies; Science-based climate mitigation strategies.

Pengzhan Guo’s research project covers methodology and applications in machine learning and data mining. He is especially interested in parallel computing, human resource management and mobile computing.His teaching interests at Duke Kunshan include linear algebra and machine learning. He has published papers in refereed journals and conference proceedings such as IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Intelligent Systems and Technology (TIST), and the IEEE International Conference on Data Mining (ICDM). He has obtained many awards including the TMC-21 Best Paper Award and ICDM-2019 Student Travel Award.He received his master’s and Ph.D. degrees in applied mathematics and statistics from Stony Brook University.

His research lies in the fields of complex analysis in several variables, harmonic analysis, and operator theory. He is particularly interested in the Bergman kernel, the Bergman projection, and mapping properties and weighted inequalities of operators in several complex variables. His teaching interests include foundational and advanced Mathematics courses in pure math. Dr. Huo received his B.S. degree in Mathematics from Wuhan University in 2011, and his Ph.D. in Mathematics from University of Illinois at the Urbana-Champaign in 2016. From 2016 to 2018, he worked at Washington University in St. Louis as a postdoctoral fellow. Before joining Duke Kunshan, he was a visiting faculty at University of Toledo.

His research focuses on understanding quantum nature of light and matter and developing ways to harness them for next generation quantum technologies including quantum computing. His teaching interests at Duke Kunshan include integrated science and advanced physics. Hwang has a B.Sc. and a Ph.D in physics from Pohang University of Science and Technology, South Korea. Before joining Duke Kunshan, he was a postdoctoral researcher at the Institute of Theoretical Physics at Ulm University, Germany.

He obtained his BS degree from Peking University and his Ph.D. from University of Arizona. He was a postdoc at Courant Institute, New York University, an assistant and associate professors at Georgia Institute of Technology, and full professor, department chair and Vilas Distinguished Achievement Professor at University of Wisconsin-Madison, Chair of Department of Mathematics at Shanghai Jiao Tong University. He also serves as a co-director of the Shanghai Center of Applied Mathematics, director of Ministry of Education Key Lab on Scientific and Engineering Computing, and director of Center for Mathematical Foundation of Artificial Intelligence at Shanghai Jiao Tong University. He received a Feng Kang Prize of Scientific Computing in 2001, and a Morningside Silver Medal of Mathematics of International Congress of Chinese Mathematicians in 2017. He is an inaugural Fellow of the American Mathematical Society (AMS) (2012), was elected a Fellow of Society of Industrial and Applied Mathematics (SIAM) (2013), a fellow of the China Society of Industrial and Applied Mathematics (CSIAM) (2020), and an Invited Speaker at the International Congress of Mathematicians in 2018. His research interests include kinetic theory, quantum dynamics, uncertainty quantification, interacting particle systems, computational fluid dynamics, etc. He has published over 180 research articles in journals such as Acta Numerica, Communications in Pure and Applied Mathematics, Journal of Computational Physics, SIAM journals, Archive Rational Mechanics and Analysis, etc.

His research focus includes number theory, combinatorics, symbolic computation, and probability. He is mainly interested in using symbolic computation and experimental mathematics to study topics in analytic number theory and combinatorics. He has published papers in leading academic journals including the Journal of Number Theory and the Journal of Symbolic Computation. Jiu has a B.Sc. and a M.Sc. from the Beijing Institute of Technology and a Ph.D. from Tulane University, New Orleans. He served as a postdoc consecutively at the Research Institute for Symbolic Computation, Johannes Kepler University, Austria; Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Science, Austria; and Dalhousie University, Canada.

Ming Li received his Ph.D. in Electrical Engineering from University of Southern California in 2013. His research interests are in the areas of speech processing and multimodal behavior signal analysis. He has published more than 90 papers and served as scientific committee members and reviewers for multiple conferences and journals. Works co-authored with his colleagues have won awards at Body Computing Slam Contest 2009, IEEE DCOSS 2009, Interspeech 2011 Speaker State Challenge, Interspeech 2012 Speaker Trait Challenge, and ISCSLP 2014 best paper award. He received the IBM faculty award in 2016 and ISCA computer speech and language best journal paper award in 2018.

Xin Li received the Ph.D. degree in Electrical & Computer Engineering from Carnegie Mellon University in 2005. He is currently a Professor in the ECE Department at Duke University. He is leading the Institute of Applied Physical Sciences and Engineering and the Data Science Research Center at Duke Kunshan University. His research interests include integrated circuit, signal processing and data analytics. Dr. Li is the Deputy Editor-in-Chief of IEEE TCAD. He was an Associate Editor of IEEE TCAD, IEEE TBME, ACM TODAES, IEEE D&T and IET CPS. He was the General Chair of ISVLSI and FAC. He received the NSF CAREER Award in 2012 and six Best Paper Awards from IEEE TCAD, DAC, ICCAD and ISIC. He is a Fellow of IEEE.

Her research interests are in the areas of functional analysis, operator algebras, mathematical physics and their applications. Her original research has been published in prestigious mathematics and science journals, including two papers in the Proceedings of the National Academy of Sciences. Her teaching interests at Duke Kunshan include foundational and advanced math courses in natural/applied sciences. Over her 13-year teaching career in the United States, she taught math at all levels and has been actively engaged in STEM education in all aspects. She has received several distinguished teaching awards, including one at the University of Pennsylvania (UPenn) and two at the University of New Hampshire (UNH). Liu is has a B.Sc. in mathematics (honors) from Hebei Normal University, China, and a Ph.D. in mathematics from UNH. She was a lecturer and postdoctoral scholar at UPenn from 2010 to 2012 and a postdoctoral scholar at the University of Denver in 2012-13. Before joining Duke Kunshan, she was an assistant professor in the Department of Mathematics at the University of Central Florida, where she was also a faculty mentor in the Career Advancement Mentoring Program for Young Entrepreneurs and Scholars funded by the U.S. National Science Foundation.

His broad research interests are climate variability, weather extremes, and atmospheric dynamics. The essential motivation for his research is to better understand and predict the behavior of the climate system, which has led to his focus on the variability of the large-scale atmospheric circulation and the related weather extremes. His teaching interests at Duke Kunshan include environmental science and physics. He has had papers published in leading academic journals including Nature Communications, Journal of Climate, and Journal of Atmospheric Sciences. He is a member of the American Geophysical Union and American Meteorological Society. Ma has a B.A. in physics for Peking University and a Ph.D. in climate dynamics from Harvard University. After receiving his Ph.D., he joined Columbia University as an Earth Institute Fellow.

His primary research interests lie in the fields of probability, statistics, combinatorics and graph theory. His teaching interests at Duke Kunshan include data science and discrete mathematics. He is the co-author of two books, “Bonferroni-type Inequalities with Applications” (Springer’s Applied Probability Series, 1996) and “Products of Random Variables: Applications to Problems of Physics and to Arithmetical Functions” (Marcel Dekker, 2004). He also has had papers published in leading academic journals including Annals of Probability, SIAM Journal of Discrete Mathematics, Theory and Decisions, and Journal of Multivariate Analysis. Simonelli has a master’s degree and Ph.D. from Temple University, Philadelphia. Before joining Duke Kunshan, he was a professor of mathematics at McDaniel College, Westminster, U.S. Previously he was an associate professor of mathematics at Texas A&M University, Commerce.

His research is in mathematical physics, at the intersection of geometry and astrophysics. In particular, he is interested in general relativity, its modifications and applications, such as mathematical properties of gravitational lensing. His teaching interests at Duke Kunshan are in the applied mathematics major, especially geometrical topics, and in developing interdisciplinary courses. He has published in leading academic journals and has been a member of the American Mathematical Society, the Royal Astronomical Society (U.K.), and the German Physical Society. Werner has an M.A., an M.Nat.Sci. and a Ph.D. from the University of Cambridge. Before joining Duke Kunshan, he taught in Duke University’s Department of Mathematics before moving to Japan in 2011 to serve first as a researcher at the University of Tokyo’s Kavli Institute for the Physics and Mathematics of the Universe and then as a Hakubi assistant professor at Kyoto University.

His research interests are machine learning and data-driven model for diseases, multiscale modeling of complex fluids, homogenization theory, and numerical analysis. Xu has a B.Sc. in mathematics (honors) from Ocean University of China and a Ph.D. in mathematics from the University of Science and Technology China. From 2013 to 2017, he held postdoctoral positions at the National University of Singapore, the University of Notre Dame, the University of California, Riverside, and the Fields Institute for Research in Mathematical Sciences, Canada.

His research interests are mathematical modeling of mixing in fluids, investigating the properties of solutions to some types of fluid mechanics equations and studying the time-fractional differential equations. His teaching interests at Duke Kunshan include mathematical foundations and advanced courses in pure math. Xu has a B.Sc. in mathematics (honors) from Zhejiang University and a Ph.D. in mathematics from the University of Wisconsin, Madison. From 2016 to 2019, he held postdoctoral research positions at Carnegie Mellon University and the Institute for Computational and Experimental Research in Mathematics, Brown University.

His primary research is in the intersection among applied harmonic analysis, machine learning and signal processing. He is especially interested in robust representations and structures in geometric and graph deep learning. His teaching interests at Duke Kunshan include calculus, linear algebra, principles of machine learning, statistical machine learning. He has had papers published in leading academic journals in various areas including Applied and Computational Harmonic Analysis and IEEE Transactions on Information Theory, and at and conferences such as the International Conference on Learning Representations. Zou has a B.Sc. in mathematics from the Chinese University of Hong Kong and a Ph.D. in applied mathematics from the University of Maryland, College Park. Before joining Duke Kunshan, he served as a post-doctorate researcher at the University of Minnesota, Twin Cities.