Section 18. Stochastic and Differential Modelling

The mathematical development of stochastic and deterministic differential modelling, and applications to such fields as biology, chemistry, medicine, material science, finance, and social network modelling. Deterministic and stochastic systems of any (possibly high) dimension, and at several scales (multiscale modelling). Tools for model reduction, calibration, uncertainty quantification and data assimilation.
Nikhil Bansal

University of Michigan, USA

Survey lecture on discrepancy theory and related algorithms

Jointly in sections 14, 16

Nikhil Bansal is the Patrick C. Fischer professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He completed his PhD from Carnegie Mellon University in 2003 and has previously worked at IBM Research, TU Eindhoven and CWI Amsterdam. He is broadly interested in theoretical computer science with focus on the design and analysis of algorithms, discrete mathematics and combinatorial optimization. Some of his notable works include understanding the algorithmic aspects of discrepancy and algorithms for the k-server problem.
Jacob Bedrossian

University of Maryland, USA

Jacob Bedrossian is a Professor of Mathematics at the University of Maryland, College Park USA. His research interests are mainly the mathematical analysis of partial differential equations arising in incompressible fluid mechanics and plasma physics. Bedrossian and his collaborators have contributed to advances in hydrodynamic stability at high Reynolds number, random dynamics in stochastic models of fluids, and the kinetic theory of plasmas. In stochastic fluid mechanics, they proved the Batchelor spectrum of passive scalar turbulence, showed almost-sure, exponential mixing by the Navier-Stokes equations, and proved chaos for the Lorenz 96 model and for finite-dimensional approximations to the Navier-Stokes equations. In hydrodynamic stability, their contributions include the nonlinear inviscid damping of Couette flow, the determination of the subcritical transition threshold for 3D Couette flow, and the local well-posedness for vortex filaments in the 3D Navier-Stokes equations. In the kinetic theory of plasmas, a number of advancements were made regarding how weak collisions, nonlinear plasma echoes, and dispersive effects all affect Landau damping near homogeneous plasma equilibria. He was awarded a 2015 Sloan Fellowship, a 2016 NSF CAREER grant, the 2019 SIAG/APDE prize (joint with Nader Masmoudi), the 2019 IMA prize, and the 2020 Peter Lax award. He is also a 2020 Simons Fellow and a 2022 Nachdiplom lecturer at ETH Zurich.
Irene Fonseca

Carnegie Mellon University, USA

Also in section 10

Irene Fonseca is the Kavčić-Moura University Professor of Mathematics and is the Director of the Center for Nonlinear Analysis of the Mathematical Sciences Department at Carnegie Mellon University in Pittsburgh, USA.

Irene Fonseca is a Fellow of the American Mathematical Society (AMS), and a Fellow of the Society for Industrial and Applied Mathematics (SIAM). She was SIAM President in 2013 and 2014. She is a Grand Officer of the Military Order of Saint James of the Sword (Grande Oficial da Ordem Militar de Sant’Iago da Espada, Portuguese Decoration). She serves in 20 Editorial Boards, including Advances in Calculus of Variations, Archive for Rational Mechanics and Analysis, Communications of the American Mathematical Society (CAMS), ESAIM:COCV (SMAI), Journal of Nonlinear Science, M3AS, and SIAM Journal on Mathematical Analysis.

Irene Fonseca’s main contributions have been on the variational study of ferroelectric and magnetic materials, composites, thin structures, phase transitions, and on the mathematical analysis of image segmentation, denoising, detexturing, registration and recolorization in computer vision. Her research program continues to explore modern methods in the calculus of variations motivated by problems emerging from materials science and imaging science.

Tadahisa Funaki

Waseda University, Japan

Also in section 12

Tadahisa Funaki is a Professor at the Department of Mathematics of Waseda University, Japan.

He was a Professor at the University of Tokyo from 1995 until 2017. His research interests include stochastic PDEs and large scale stochastic interacting systems.

Specifically, he worked on continuous and discrete Ginzburg-Landau models, stochastic Allen-Cahn equation, random interfaces, stochastic motion by mean curvature, singular stochastic PDEs, space-time scaling limit for microscopic systems via local ergodicity, derivation of macroscopic nonlinear PDEs and stochastic PDEs including motion by mean curvature, Stefan free boundary problem and coupled KPZ equation, and other topics.

Hyeonbae Kang

Inha University, Korea

Hyeonbae Kang is a Jungseok Chair Professor at the Department of Mathematics of Inha University, S. Korea.

His interests include inverse problems, applications of partial differential equations, and spectral theory. In particular, he is known for the resolution of conjectures of Pólya-Szegö and Eshelby. He is currently working on PDE problems arising from the theory of composites and spectral analysis and geometry of the Neumann-Poincaré operator. He is a member of the Korean Academy of Science and Technology and received a few prestigious prizes, among them is the Korea Science Award (presidential award).

Yann LeCun

Facebook AI Research and Courant Institute of Mathematical Sciences, New York University, USA

Lecture on some of the mathematical questions raised by deep learning

Jointly in sections 14, 17

Yann LeCun is VP & Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute of Mathematical Sciences & the Center for Data Science.

He was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received an Engineering Diploma from ESIEE (Paris) and a PhD from Sorbonne Université.

After a postdoc in Toronto he joined AT&T Bell Labs in 1988, and AT&T Labs in 1996 as Head of Image Processing Research.

He joined NYU as a professor in 2003 and Facebook in 2013. His interests include AI machine learning, computer perception, robotics and computational neuroscience.

He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for «conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing», a member of the National Academy of Engineering and a Chevalier de la Légion d’Honneur.

Sylvie Méléard

Ecole Polytechnique Paris, France

Sylvie Méléard is a Professor of Applied Mathematics at the École Polytechnique, France, and a Senior Member of the Institut Universitaire de France, working in the field of probability theory, and more specifically interacting particle systems, measure-valued processes and quasi-stationary distributions.

For the last twenty years, her research has been focused on mathematical issues arising from biological motivations and, in particular, related to the modelling of biodiversity, through individual-based stochastic eco-evolutionary models and their numerous approximations involving different scalings. She works with theoretical biologists as well as biologists specialized in drosophila, bacteria and cells, and hematologists.

She is a member of the Academia Europaea, the European Academy of Sciences and the Academia Chilena de Ciencias «La Recherche» award 2013.

Elchanan Mossel


Survey lecture on combinatorial statistics and its role in the sciences

Jointly in sections 12, 13, 14

Elchanan Mossel is a Professor of Mathematics at the Massachusetts Institute of Technology. His research spans a number of topics across probability, statistics, economics, computer science, and mathematical biology.

He is known for his work in discrete Fourier analysis and its applications to computational complexity and social choice theory and for his research of information flow in biological, economic, and inferential networks.

Mossel held a Sloan Fellowship. He is a fellow of the American Mathematical Society, a Simons Fellow and a Vannevar Bush Fellow.

Eric Vanden-Eijnden

Courant Institute of Mathematical Sciences/NYU, USA

Lecture on the computational aspects of statistical mechanics

Jointly in sections 11, 12, 15

Eric Vanden-Eijnden is a Professor of Mathematics at the Courant Institute of Mathematical Sciences,New York University. His research focuses on the mathematical and computational aspects of statistical mechanics, with applications to complex dynamical systems arising in molecular dynamics, materials science, atmosphere-ocean science, fluid dynamics, and neural networks. He is also interested in the mathematical foundations of machine learning (ML) and the applications of ML in scientific computing. He is known for the development and analysis of multiscale numerical methods for systems whose dynamics span a wide range of spatio-temporal scales. He is the winner of the Germund Dahlquist Prize and the J.D. Crawford Prize,and a recipient of the Vannevar Bush Faculty Fellowship.
Vlad Vicol

Courant Institute of Mathematical Sciences, New York University, USA

Also in section 10

Vlad Vicol is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He received his Ph.D. in 2010 from the University of Southern California, under the supervision of Igor Kukavica. His research focuses on the analysis of partial differential equations arising in fluid dynamics, with an emphasis on problems motivated by hydrodynamic turbulence. He was awarded an Alfred P Sloan Research Fellowship (2015), the MCA Prize by the Mathematical Congress of the Americas (2017), and shared a Clay Research Award (2019).
Thu Nov 18 2021 13:50:58 GMT+0300 (Moscow Standard Time)