The 3-day summer school is intended for graduate students and postdocs who are interested in or working on computational and data science. It includes lecture series from introduction to forefront research on various topics listed below. The followed workshop will provide a platform for experts and participants to share their research and discuss new developments in this field. Participants of the summer school will also have opportunities to give a short presentation and a poster about their work during the workshop. There will be ample opportunities for all participants to communicate, interact, and connect during the summer school and workshop. Partial funding from NSF RTG is available to support a limited number of graduate student participants (US citizens and permanent residents). Participants are also encouraged to find other funding sources. 
The summer school  (Aug 14-16) will be held at Duke University Marine Lab right on the coast in Beaufort, NC. The workshop (Aug 17-18) will be held on Duke University campus in Durham, NC. Shuttle service  between these two sites will be provided.
Organizers: Jian-Guo Liu and Hongkai Zhao
Lecturers for the summer school:
Di Fang (UC Berkeley) “Introduction to quantum algorithms”
Yuan Gao (Purdue) “Stochastic optimal control, large deviation and transition paths on continuous/discrete states.”
Rongjie Lai (RPI) “Computational Mean-field Games: from conventional numerical methods to deep generative models”
Jonathan Siegel (Texas A&M) “Theory of ReLU Neural Networks: Representation, Interpolation, and Approximation”
Leonardo Andres Zepeda Nunez(Google Research and University of Wisconsin) “Reduced-order modeling with machine learning: from linear projections to hypernetworks”
Speakers  for the Workshop:
Di Fang (UC Berkeley) “Quantum algorithms for Hamiltonian simulation with unbounded operators”
Mohammad Farazmand (NCSU) “Shape-morphing neural networks for solving PDEs with conserved quantities”
Yuan Gao (Purdue) “Thermodynamic limit, and global energy landscape for non-equilibrium chemical reactions”
Caroline Moosmueller (UNC) “Approximations and learning in the Wasserstein space”
Rongjie Lai (RPI) “Learning Dynamics guided by Mean-field Games”
Jonathan Siegel (Texas A&M) “Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces”
Abiy Tasissa (Tufts) “Local Sparse coding via a Delaunay triangulation”
Leonardo Andres Zepeda Nunez(Google Research and University of Wisconsin) “Statistical Downscaling via Optimal Transport and Conditional Diffusion Models”
Anru Zhang (Duke) “Tensor Learning in 2020s: Methodology, Theory, and Applications”
Yimin Zhong (Auburn) “Implicit boundary integral method for linearized Poisson Boltzmann equation: computation and analysis”
Registration deadline: May 20, 2023.