2026 Fudan University International Summer School on AI in Math
July 20 - July 25, 2026
The 2026 Fudan University International Summer School on AI in Math will be held from July 20 to July 25, 2026. Jointly hosted by the School of Mathematical Sciences, Fudan University and the Shanghai Center for Mathematical Sciences, this summer school will focus on the theme Theory and Applications of AI in Mathematics, covering cutting-edge topics including deep learning theory, scientific machine learning, approximation theory of neural networks, large language models and generative AI, and Bayesian methods and inverse problems.
The program combines tutorial-style lectures with a dedicated day of invited talks: July 20-21 and July 23-25 are reserved for systematic tutorial lectures, while July 22 is the symposium day, featuring talks by leading researchers in the field.
Open to graduate students and young scholars worldwide, the school aims to provide a platform for academic exchange at the interface of mathematics and artificial intelligence. We warmly welcome you to join us in exploring this exciting frontier!
Schedule
| Date | Topic | Speaker |
|---|---|---|
| July 20 (AM) | TBA | TBA |
| July 20 (PM) | TBA | TBA |
| July 21 (AM) | TBA | TBA |
| July 21 (PM) | TBA | TBA |
| July 22 (AM) | TBA | TBA |
| July 22 (PM) | TBA | TBA |
| July 23 (AM) | TBA | TBA |
| July 23 (PM) | TBA | TBA |
| July 24 (AM) | TBA | TBA |
| July 24 (PM) | TBA | TBA |
| July 25 (AM) | TBA | TBA |
| July 25 (PM) | TBA | TBA |
Tutorial Lecturers (in Alphabetical Order)
Asst. Prof. Juncai He
Yau Mathematical Sciences Center, Tsinghua University
Dr. Juncai He is an Assistant Professor at the Yau
Mathematical Sciences Center (YMSC), Tsinghua
University.
He received his Ph.D. in Computational Mathematics from
Peking University in 2019, and was a Visiting Ph.D. Research
Scholar at the Center for Computational Mathematics
and Applications (CCMA) at Pennsylvania State
University during his graduate studies. From 2019 to
2024, he served as a research scientist in the CEMSE
Division at King Abdullah University of Science and
Technology (KAUST), before joining Tsinghua YMSC.
His research interests include
scientific machine learning,
numerical PDEs,
connections between finite element methods and
deep neural networks,
approximation and representation theory of
neural networks, and
multiscale algorithms, spanning both
data and physical sciences.
He has received several honors, including the R. H. Bing Fellowship from the University of Texas at Austin, selection to The Elite Program of Computational and Applied Mathematics for PhD Candidates at Peking University, and the Peking University President's Scholarship for Doctoral Students.
Asst. Prof. Lei Wu
Peking University
Dr. Lei Wu is an Assistant Professor at the School of
Mathematical Sciences and the Center for Machine
Learning Research at Peking University.
He received his Ph.D. in Computational Mathematics from
Peking University in 2018, and conducted postdoctoral
research at Princeton University and the University of
Pennsylvania from 2018 to 2021, before joining Peking
University.
His research aims to uncover the mathematical
mechanisms behind the success of deep learning, with a
particular focus on
the approximation and representation power of
neural networks,
the dynamical behavior of popular optimization
algorithms such as SGD and Adam, and
emergent phenomena in the training of large
language models (LLMs).
Invited Speakers (in Alphabetical Order)
Prof. Xiuyuan Cheng
Duke University
Professor Xiuyuan Cheng is a Professor in the
Department of Mathematics and the Department of
Statistical Science at Trinity College of Arts &
Sciences, Duke University.
She received her Ph.D. from the Program in Applied
and Computational Mathematics (PACM) at Princeton
University in 2013. She then conducted postdoctoral
research at the École Normale Supérieure in Paris
(2013-2015), was a Gibbs Assistant Professor of
Mathematics at Yale University (2015-2017), and
joined Duke in 2017, where she has served as
Assistant Professor, Associate Professor, and now
Professor.
Her research focuses on developing theoretical and
computational techniques to solve problems in
high-dimensional data analysis,
signal processing, and
machine learning.
She is a recipient of the NSF CAREER Award (2023),
Sloan Research Fellow (2019), and was awarded the
Princeton University Harold W. Dodds Fellowship and
the C. V. Starr Fellowship (2012).
Assoc. Prof. Ruoyu Sun
The Chinese University of Hong Kong, Shenzhen
Dr. Ruoyu Sun is a tenured Associate Professor at the
School of Data Science, the Chinese University of
Hong Kong, Shenzhen, and is also a Senior Research
Scientist at the Shenzhen Research Institute of Big
Data (SRIBD) and Vice Chair of the Shenzhen
International Center for Industrial and Applied
Mathematics (SICIAM).
He received his Ph.D. in Electrical Engineering from
the University of Minnesota, conducted postdoctoral
research at Stanford University, and was a full-time
visiting research scientist at Facebook AI Research
(FAIR). From 2017 to 2022, he was
a tenure-track Assistant Professor in the Department
of ISE (with a courtesy appointment in ECE) at the
University of Illinois at Urbana-Champaign (UIUC),
before joining CUHK Shenzhen.
His research focuses on
the theory and algorithms of large foundation
models, generative AI,
learning-assisted optimization,
neural network compression, and
the loss landscape of neural networks and
the Adam optimizer.
He is particularly interested in nonconvex
optimization: his contributions include one of the
first geometric analyses of nonconvex matrix
completion (FOCS), the widely cited survey
"Optimization for deep learning: an overview", and a
convergence proof for the original Adam algorithm.
Prof. Liang Yan
Southeast University
Professor Liang Yan is a full Professor and doctoral
supervisor at the School of Mathematics, Southeast
University, and is also affiliated with the Nanjing
Center for Applied Mathematics.
He received his Ph.D. in Mathematics from Lanzhou
University in 2011, and joined Southeast University
thereafter, successively serving as Assistant
Professor, Associate Professor, and Professor.
His research interests include
Bayesian modeling and computation,
inverse and ill-posed problems,
scientific machine learning,
deep Bayesian methods, and
data assimilation, with notable
contributions to surrogate-based methods for Bayesian
inverse problems, adaptive ensemble Kalman inversion,
and Stein variational gradient descent with local
approximations.
He was named Outstanding Undergraduate Advisor at
Southeast University and received the inaugural
"Outstanding Teaching Award - Emerging Teacher Prize"
of Southeast University in 2021.
Prof. Dingxuan Zhou
The University of Sydney · City University of Hong Kong
Professor Dingxuan Zhou is Professor and Head of the
School of Mathematics and Statistics at the
University of Sydney.
He received his Ph.D. from Zhejiang University in
1991. Before moving to Australia, he was a Chair
Professor in the School of Data Science and the
Department of Mathematics at the City University of
Hong Kong, and served as Director of the Liu Bie Ju
Centre for Mathematical Sciences (2019-2022),
Associate Dean of the School of Data Science
(2018-2022), and Head of the Department of
Mathematics (2006-2012).
His research interests include
deep learning theory,
statistical machine learning,
deep neural networks,
approximation theory,
wavelet analysis, and applications
of machine learning.
He received the National Science Fund for
Distinguished Young Scholars from the NSF of China
in 2005. He was recognized as a Highly Cited
Researcher by Thomson Reuters / Clarivate (2014-2017)
and was listed among Stanford University's "World's
Top 2% Scientists" in 2021 and 2022.
Prof. Tao Zhou
Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Professor Tao Zhou is a Researcher at the Institute
of Computational Mathematics and Scientific/Engineering
Computing (ICMSEC), Academy of Mathematics and Systems
Science (AMSS), Chinese Academy of Sciences, and Vice
Secretary-General of the China Society for Industrial
and Applied Mathematics (CSIAM).
He received his Ph.D. in 2011, held a postdoctoral
position at EPFL, Switzerland (2011-2012), and joined
AMSS thereafter, successively serving as Assistant
Researcher, Associate Researcher, and Researcher.
His research interests include
scientific machine learning,
numerical methods for PDEs,
spectral and high-order methods (with
applications),
parallel-in-time algorithms,
phase-field models, and
stochastic optimal control; he
proposed and systematically developed the
parallel-in-time method ParaDiag, and contributed to
adaptive surrogate model algorithms for Bayesian
inverse problems.
He is a recipient of the 22nd Chern Mathematics
Award of the Chinese Mathematical Society (2025),
the 3rd Wang Xuan Outstanding Young Scholar Award
(Peking University Foundation, 2022), and the CSIAM
Young Scientist Award (2016).
Application
Application Deadline
Please complete your application by June 18, 2026. Please note that this link is for preliminary registration only. If you are admitted, we will collect further detailed information via email. Admission results will be announced via email by June 30, 2026.
Eligibility
The Summer School is open to graduate students, postdocs and junior faculty in mathematics, computer science, artificial intelligence and related fields from universities and research institutions worldwide.
Application Materials
- Personal Statement
- CV
- Letters of Recommendation (not required for junior faculty)
Funding Program
Local accommodation and meals will be covered (double rooms provided). Outstanding applicants will get partial travel expense subsidies.
Location
Jiangwan Campus, Fudan University
Address
2005 Songhu Rd, Yangpu District, Shanghai, China
Organization
Organizers
- Shuai Lu slu@fudan.edu.cn
- Lei Shi leishi@fudan.edu.cn
- Yingzhou Li yingzhouli@fudan.edu.cn
- Jia Yin jiayin@fudan.edu.cn
- Tianyu Wang wangtianyu@fudan.edu.cn
Student Teaching Assistants
- Ming Li mingli23@m.fudan.edu.cn
