Conference Venue

  • The conference is held at Petigru College, University of South Carolina (map). 
  • All talks will be delivered at Room 108. Poster Session will be held at Room 101 and 102.

Travel Directions

  • If you travel by air, University of South Carolina is located close to the Columbia Metropolitan Airport (CAE). The easiest way to travel from CAE airport to campus is by Uber or Lyft. The ride will take approximately 15-20 minutes.


  • There are many hotels near the University of South Carolina and the Downtown Columbia area.
  • We have reserved a block of rooms at Courtyard by Marriott Columbia Downtown at USC, 630 Assembly St, Columbia, SC 29201, with a group rate of $129 per night. Reservations can be made online from THIS LINK, or by phone at 803-799-7800, mentioning USC Math Conference. The deadline for reservations at this rate is April 29, 2024.

Conference Dinner

  • The dinner banquet is held on Tuesday May 28th evening from 6:30 PM to 10:00 PM at the Carolina Room of Capstone Residential Hall (map).

Campus Dining Map


  • The schedule of the conference can be found HERE.
  • The abstract book can be found HERE.

List of Participants

  1. Jing An, Duke University.
  2. George Androulakis, University of South Carolina. 
  3. Peter Binev, University of South Carolina.
  4. Amimikh Biswas, University of Maryland, Baltimore County. [Speaker]
  5. McKenzie Black, University of South Carolina. [Poster]
  6. Victoria Chebotaeva, University of South Carolina. 
  7. Dongwei Chen, Clemson University. [Poster]
  8. Geng Chen, University of Kansas. [Speaker]
  9. Ming Chen, University of Pittsburgh. [Speaker]
  10. Peiyi Chen, University of Wisconsin, Madison. [Poster]
  11. Alina Chertock, North Carolina State University. [Speaker]
  12. Wolfgang Dahmen, University of South Carolina. [Speaker]
  13. Ronald DeVore, Texas A&M University. [Keynote Speaker]
  14. Di Fang, Duke University. [Speaker]
  15. Guosheng Fu, University of Notre Dame.
  16. Yuan Gao, Purdue University. [Speaker]
  17. Maria Girardi, University of South Carolina. 
  18. Anderson Greene, University of South Carolina. 
  19. Ziheng Guo, Illinois Institute of Technology. [Poster]
  20. Siming He, University of South Carolina. [Organizer]
  21. Jianguo Hou, University of South Carolina. 
  22. Zhongtian Hu, Duke University.
  23. Gautam Iyer, Carnegie Mellon University. [Speaker]
  24. Pierre-Emmanuel Jabin, Pennsylvania State University. [Speaker]
  25. Ruhui Jin, University of Wisconsin, Madison. [Poster]
  26. Yannis Kevrekidis, Johns Hopkins University. [Keynote Speaker]
  27. SeHwan Kim, University of South Carolina. 
  28. Trevor Leslie, Illinois Institute of Technology.
  29. Qin Li, University of Wisconsin, Madison. [Speaker]
  30. Wuchen Li, University of South Carolina. 
  31. Quyuan Lin, Clemson University. [Speaker]
  32. Hailiang Liu, Iowa State University. [Speaker]
  33. Jian-Guo Liu, Duke University. [Speaker]
  34. Jingcheng Lu, University of Minnesota, Twin Cities. [Poster]
  35. Kunhui Luan, University of South Carolina. 
  36. Mauro Maggioni, Johns Hopkins University. [Speaker]
  37. Anna Mazuccato, Pennsylvania State University. [Speaker]
  38. Lorenzo Micalizzi, North Carolina State University.
  39. Sebastien Motsch, Arizona State University. [Speaker]
  40. Ronghua Pan, Georgia Institute of Technology. [Speaker]
  41. Keith Promislow, Michigan State University. [Speaker]
  42. Ruiwen Shu, University of Georgia. [Speaker]
  43. Roman Shvydkoy, University of Illinois, Chicago. [Speaker]
  44. Henry Simmons, University of South Carolina. 
  45. Seungjae Son, Carnegie Mellon University. [Poster]
  46. Weiran Sun, Simon Fraser University. [Speaker]
  47. Yi Sun, University of South Carolina. 
  48. Eitan Tadmor, University of Maryland.
  49. Changhui Tan, University of South Carolina. [Organizer]
  50. Wei-Lun Tsai, University of South Carolina.
  51. Wendy Garcia Umbarita, Arizona State University.
  52. Li Wang, University of Minnesota. [Speaker]
  53. Zhu Wang, University of South Carolina. 
  54. Zhaoqing Xu, University of South Carolina. 
  55. Xukai Yan, Oklahoma State University. [Speaker]
  56. Cheng Yu, University of Florida.
  57. Yue Yu, Lehigh University. [Speaker]
  58. Qingtian Zhang, West Virginia University. [Speaker]
  59. Ming Zhong, Illinois Institute of Technology. [Organizer]
  60. Yuhua Zhu, University of California, San Diego. [Speaker]


Thursday, 07 March 2024 15:58

Nonlocal Models: Analysis and Applications

Written by

Confirmed Speakers

  • Amimikh Biswas, University of Maryland, Baltimore County.
  • Alina Chertock, North Carolina State University.
  • Wolfgang Dahmen, University of South Carolina.
  • Ronald DeVore, Texas A&M University.
  • Geng Chen, University of Kansas.
  • Ming Chen, University of Pittsburgh.
  • Di Fang, Duke University.
  • Yuan Gao, Purdue University.
  • Gautam Iyer, Carnegie Mellon University.
  • Pierre-Emmanuel Jabin, Pennsylvania State University.
  • Yannis Kevrekidis, Johns Hopkins University.
  • Qin Li, University of Wisconsin, Madison.
  • Quyuan Lin, Clemson University.
  • Hailiang Liu, Iowa State University.
  • Jian-Guo Liu, Duke University.
  • Mauro Maggioni, Johns Hopkins University.
  • Anna Mazuccato, Pennsylvania State University.
  • Sebastien Motsch, Arizona State University.
  • Ronghua Pan, Georgia Institute of Technology.
  • Keith Promislow, Michigan State University.
  • Ruiwen Shu, University of Georgia.
  • Roman Shvydkoy, University of Illinois, Chicago.
  • Weiran Sun, Simon Fraser University.
  • Li Wang, University of Minnesota.
  • Xukai Yan, Oklahoma State University.
  • Yue Yu, Lehigh University.
  • Qingtian Zhang, West Virginia University.
  • Yuhua Zhu, University of California, San Diego.


  • REGISTER HERE by May 17th.
  • A limited amount of travel and local lodging support is available for researchers in the early stages of their careers who want to attend the full program, especially for graduate students and post-doctoral fellows. Apply by April 28th.

Organizing Committee

  • Changhui Tan, University of South Carolina. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Siming He, University of South Carolina.
  • Ming Zhong, Illinois Institute of Technology.


  • Funding provided by NSF Grant DMS-2238219.
  • We acknowledge partial support from University of South Carolina: College of Arts and Sciences, Department of Mathematics, and DASIV Smart State Center.

 Speaker: Yuan-Nan Young (New Jersey Institute of Technology)

The Stoichiometric Model for the interaction of centrosomes with cortically anchored pulling motors, through their associated microtubules (MTs), has been applied to study key steps in the cell division such as spindle positioning and elongation. In this work we extend the original Stoichiometric Model to incorporate (1) overlap in the cortical motors, and (2) the dependence of velocity in the detachment rate of MTs from the cortical motors. We examine the effects of motor overlap and velocity-dependent detachment rate on the centrosome dynamics, such as the radial oscillation around the geometric center of the cell, the nonlinear nature (supercritical and subcritical Hopf bifurcation) of such oscillation, and the nonlinear orbital motions previously found for a centrosome. We explore biologically feasible parameter regimes where these effects may lead to significantly different centrosome/nucleus dynamics. Furthermore we use this extended Stoichiometric Model to study the migration of a nucleus being positioned by a centrosome. This is joint work with Justin Maramuthal, Reza Farhadifar and Michael Shelley.

Time: December 8, 2023 2:30pm-3:30pm
Location: Virtually via Zoom
Host: Paula Vasquez

 Speaker: Quyuan Lin (Clemson University)

Large scale dynamics of the ocean and the atmosphere are governed by the primitive equations (PE). In this presentation, I will first review the derivation of the PE and some well-known results for this model, including well-posedness of the viscous PE and ill-posedness of the inviscid PE. The focus will then shift to discussing singularity formation and the stability of singularities for the inviscid PE, as well as the effect of fast rotation (Coriolis force) on the lifespan of the analytic solutions. Finally, I will talk about a machine learning algorithm, the physics-informed neural networks (PINNs), for solving the viscous PE, and its rigorous error estimate.

Time: November 17, 2023 2:30pm-3:30pm
Location: LeConte 440
Host: Changhui Tan

 Speaker: Adrian Tudorascu (West Virginia University)

We study Zeldovich's Sticky-Particles system when the evolution is confined to arbitrary closed subsets of the real line. Only the sticky boundary condition leads to a rigorous formulation of the initial value problem, whose well-posedness is proved under the Oleinik and initial strong continuity of energy conditions. For solutions confined to compact sets a long-time asymptotic limit is shown to exist.

Time: October 27, 2023 2:30pm-3:30pm
Location: LeConte 440
Host: Changhui Tan

 Speaker: Yuehaw Khoo (University of Chicago)

Tensor-network ansatz has long been employed to solve the high-dimensional Schrödinger equation, demonstrating linear complexity scaling with respect to dimensionality. Recently, this ansatz has found applications in various machine learning scenarios, including supervised learning and generative modeling, where the data originates from a random process. In this talk, we present a new perspective on randomized linear algebra, showcasing its usage in estimating a density as a tensor-network from i.i.d. samples of a distribution, without the curse of dimensionality, and without the use of optimization techniques. Moreover, we illustrate how this concept can combine the strengths of particle and tensor-network methods for solving high-dimensional PDEs, resulting in enhanced flexibility for both approaches.

Time: December 1, 2023 3:40pm-4:40pm
Location: LeConte 440
Host: Wuchen Li

Wednesday, 18 October 2023 16:04

Hybrid quantum classical algorithms

 Speaker: Xiantao Li (Pennsylvania State University)

Quantum computing has recently emerged as a potential tool for large-scale scientific computing. In sharp contrast to their classical counterparts, quantum computers use qubits that can exist in superposition, potentially offering exponential speedup for many computational problems. Current quantum devices are noisy and error-prone, and in near term, a hybrid approach is more appropriate. I will discuss this hybrid framework using three examples: quantum machine learning, quantum algorithms for density-functional theory and quantum optimal control. In particular, this talk will outline how quantum algorithms can be interfaced with a classical method, the convergence properties and the overall complexity.

Time: November 3, 2023 2:30pm-3:30pm
Location: LeConte 440
Host: Yi Sun

 Speaker: Jiajia Yu (Duke University)

Mean-field games study the Nash Equilibrium in a non-cooperative game with infinitely many agents. Most existing works study solving the Nash Equilibrium with given cost functions. However, it is not always straightforward to obtain these cost functions. On the contrary, it is often possible to observe the Nash Equilibrium in real-world scenarios. In this talk, I will discuss a bilevel optimization approach for solving inverse mean-field game problems, i.e., identifying the cost functions that drive the observed Nash Equilibrium. With the bilevel formulation, we retain the essential characteristics of convex objective and linear constraint in the forward problem. This formulation permits us to solve the problem using a gradient-based optimization algorithm with a nice convergence guarantee. We focus on inverse mean-field games with unknown obstacles and unknown metrics and establish the numerical stability of these two inverse problems. In addition, we prove and numerically verify the unique identifiability for the inverse problem with unknown obstacles. This is a joint work with Quan Xiao (RPI), Rongjie Lai (Purdue) and Tianyi Chen (RPI).

Time: October 6, 2023 3:40pm-4:40pm
Location: LeConte 440
Host: Wuchen Li

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