1. 일시: 2023년 3월 6일(월), 14:00-16:00

2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실

   • 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
   • 무료주차는 2시간 지원됩니다.

3. 발표자: 노영균 교수(한양대학교)

4. 주요내용: Techniques for f-divergence Estimation and their Applications to Machine Learning

   In this talk, various f-divergence estimation techniques and their applications will be explained based on the connection to loss functions. It is known that loss functions are connected to their corresponding f-divergences which measure the theoretical limit of the associated loss functions. In this regard, researchers have focused on the problems that tackle the optimization of f-divergences instead of loss functions. I will clarify the problems that we can benefit from optimizing the f-divergences and explain how novel techniques for f-divergence estimation can be used for real applications.

1. 일시: 2023년 2월 27일(월), 14:00-16:00

2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실

   • 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
   • 무료주차는 2시간 지원됩니다.

3. 발표자: 권순학 교수(성균관대학교)

4. 주요내용: Survey of vectorial Boolean functions and some unsolved problems.

Boolean functions have many applications in diverse areas of mathematics and engineering, and an efficient design of S-Box in block ciphers is one such example. It is desirable to construct invertible Boolean functions (or a class of permutation polynomials over finite fields) having good cryptographic parameters. In this talk, we give a brief survey of (vectorial) Boolean functions and their applications. We will explain standard invariants of Boolean functions such as nonlinearity, differential uniformity, Walsh spectrum, etc. Also, some results and conjectures on the functions having optimal invariants will be discussed.

** 유튜브 실시간 스트리밍 : 현장 참석이 어려운 분들을 위해 온라인으로 실시간 방송할 예정입니다. 주소는 당일 신청페이지에 업데이트 하겠습니다.

1. 일시: 2023년 2월 23일(목), 11:00-13:00

2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실

   • 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
   • 무료주차는 2시간 지원됩니다.

3. 발표자: 권순선 교수(아주대학교)

4. 주요내용: Interpretable Classification for Multivariate Gait Analysis

Motivated by gait data from both the normal and the cerebral palsy (CP) patients group with various gross motor function classification system (GMFCS) levels, we propose a multivariate functional classification method to investigate the relationship between kinematic gait measures and GMFCS levels. A sparse linear functional discrimination framework is utilized to achieve an interpretable prediction model. The method is generalized to handle multivariate functional data and multi-class classification. The method yields superior prediction accuracy and provides easily interpretable discriminant functions. And it will help clinicians to diagnose CP and assign an appropriate GMFCS level in a more consistent and mathematical evidence.

** 유튜브 실시간 스트리밍 : 현장 참석이 어려운 분들을 위해 온라인으로 실시간 방송할 예정입니다. 주소는 당일 신청페이지에 업데이트 하겠습니다.

인공지능에 관심 있는 수학자 및 관련 연구자들을 대상으로 최근 주목을 끌고 있는 GNN(Graph Neural Network), Neural ODE, 그리고 Score-based 생 성모델 및 Diffusion 확률모델을 소개하고 관련 연구 정보를 제공하고자 튜토 리얼 워크숍을 개최합니다.

수학 및 관련 연구자들이 인공지능 전문가가 아니더라도 관련 주제에 손쉽게 접근할 수 있도록 강연과 함께 컴퓨팅 시연을 포함한 튜토리얼 형식의 프로그램이니 많은 참여 바랍니다.

등록 링크: https://bit.ly/2023-winter-ai-tutorial

1. 일시: 2023년 2월 16일(목), 14:00-16:00

2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실

   • 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
   • 무료주차는 2시간 지원됩니다.

3. 발표자: 정은옥 교수(건국대학교)

4. 주요내용: Lessons from COVID-19 and Mathematical Modeling

Due to COVID-19, our society, culture, economy, public health and education have changed a lot. In the mathematics community, research has also been conducted to present a preemptive and scientific basis for the COVID-19 quarantine policy through communication with the Korea Disease Control and Prevention Agency (KDCA) rather than existing laboratory research. In this talk, we will present some mathematical modeling that supported the disease control policy decision of the KDCA during COVID-19 epidemics. Furthermore, let’s think together what mathematicians should do with COVID-19 and what the important lessons from COVID-19 are.

** 유튜브 실시간 스트리밍 : 현장 참석이 어려운 분들을 위해 온라인으로 실시간 방송할 예정입니다. 주소는 당일 신청 페이지에 업데이트 하겠습니다.

1. 일시: 2023년 2월 3일(금), 15:00-17:00

2. 장소: 산업수학혁신센터 세미나실

• 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
• 무료주차 2시간 등록 가능

  3. 발표자: 김연응 교수(가천대학교)

  4. 주요내용: Discrete optimal control - learning LQR

  Linear Quadratic Regulator (LQR) is one of the most popular structures in optimal control problems. This talk introduces some basic properties of the LQR problems and recent progress in learning LQR. There are two approaches to measuring the performance of the algorithm, Bayesian and Frequentist regret. The advantages and disadvantages will be discussed particularly g on Thompson sampling. Thompson sampling (TS) is known to effectively address the exploration-exploitation trade-off in online learning problems including reinforcement learning for linear-quadratic regulators (LQR). However, in TS for learning LQR, its theoretical analysis is often limited to the case of Gaussian noises. The sampling can be performed directly when we further assume that the unknown system parameters lie in a prespecified compact set which is seemingly restrictive. We propose a new TS algorithm for LQR, exploiting Langevin dynamics to handle a larger class of problems including those with non-Gaussian noises. The notion of preconditioner is introduced to generate samples from non-conjugate posterior distributions.

현장강의만 진행합니다.

1. 일시: 2023년 1월 19일(목), 14:00-16:00

2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실

- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소

- 무료주차는 2시간 지원됩니다.

3. 발표자: 최수영 교수(아주대학교)

4. 주요내용: GPU-friendly enumeration for mathematical algorithm on simplicial complexes

In this talk, we discuss the use of parallel computing for mathematical problems. In particular, we provide a GPU-friendly algorithm for obtaining all weak pseudo-manifolds whose facets are all in an input set of facets satisfying given conditions.

We use this algorithm to completely list up toric colorable seed PL-spheres with a few vertices implying the complete characterization of PL-spheres of dimension n-1 with n+4 vertices having maximal Buchstaber numbers. This result is an essential intermediate step for the classification of non-singular complete toric varieties with Picard number 4.

5. 유튜브 실시간 스트리밍 : 현장 참석이 어려운 분들을 위해 온라인으로 실시간 방송할 예정입니다. 주소는 당일 신청페이지에 업데이트 하겠습니다.

1. 일시: 2022년 12월 15일(목), 14:00-16:00
2. 장소: 판교 테크노밸리 산업수학혁신센터 세미나실 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소 /무료주차는 2시간 지원됩니다.
3. 발표자: 임용도 교수(성균관대학교)
4. 주제: Averages of manifold-valued data 
5. 초록: Positive definite matrices have become fundamental computational objects in many areas of engineering, computer science, physics, statistics, and applied mathematics. They appear in a diverse variety of settings: covariance matrices in statistics, elements of the search space in semidefinite programming, kernels in machine learning, density matrices in quantum information, data points in radar imaging, diffusion tensors in medical imaging, to cite only a few. A big problem regarding the Cartan-Hadamard-Riemannian (resp. Wasserstein) manifold of positive definite matrices is finding the best averaging of points in such a way that the resulting point is again positive definite and is invariant under the action of inversion and (resp. unitary) congruence transformations. In this talk, we introduce the Cartan and Wasserstein means of positive definite matrices and discuss several open problems.
6. 현장강의로만 진행됩니다.

1. 일시: 2022년 12월 13일(화), 13:30-15:30

2. 장소: 산업수학혁신센터 세미나실

• 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
• 무료주차 2시간 등록 가능

  3. 발표자: 최우철 교수(성균관대학교)

  4. 주요내용: An introduction to optimization and control problems for mechanical engineering

  Optimization and control problems are widely studied in connection with Mechanical engineering. In particular, one has to use highly efficient algorithms in optimization and control theory to develop intelligent robots and machines. In this talk, I will introduce the mathematical aspects of the optimization and control problems in connection with applications to intelligent robots and machines. An important concept is the model predictive control which solves the optimal control problem repeatedly in real-time applications. I will explain related constrained optimizations problems and various algorithms for solving such constrained optimization.

유튜브 스트리밍 예정(당일 신청페이지 참고)

1. 일시: 2022년 12월 8일(목), 14:00-16:00

2. 장소: 산업수학혁신센터 세미나실

• 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
• 무료주차 2시간 등록 가능

  3. 발표자: 최준호 박사(성균관대학교)

  4. 주요내용: Unsupervised Legendre-Galerkin Neural Network for Stiff Partial Differential Equations

  In this talk, I will introduce an machine learning method called unsupervised Legendre-Galerkin Neural Network(ULGNet) to solve various partial differential equations(PDEs). In recent works, many neural network approaches without using solution datasets of PDEs have been successfully developed, such as physics informed neural networks(PINN), deep Ritz method(DRM), and deep Galerkin method(DGM). While these methods are optimized to solve a single PDE, it will be presented how ULGNet is able to solve multiple PDEs. In addition, because the methods depend on auto-gradient to compute residuals of PDEs in a loss function, they have suffered with handling stiff slopes of solutions, for example, stiff boundary layers. Hence, I will introduce how ULGNet employs Legendre-Galerkin method to deal with not only the general 1D and 2D PDEs, Dirichlet and Neumann boundary condition, but also singular perturbed PEDs which have stiff boundary layers.