Date/Time : 27 Jan 2026 16:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Ivan Au Yeung , Nvidia

This talk explores solving nonlinear p-Laplacian problems using NVIDIA PhysicsNeMo, a GPU-accelerated framework for building and training physics AI models at scale. The p-Laplacian is motivated through applications such as nonlinear diffusion and variational problems, with emphasis on the numerical difficulties that arise for large values of p and in complex 2D and 3D geometries. The core of the talk is a PhysicsNeMo-based workflow that recasts the p-Laplacian into Physics-Informed Neural Network and operator-learning formulations, enabling end-to-end training directly from PDE specifications and boundary conditions while leveraging multi-GPU hardware. Numerical experiments compare these neural solvers with traditional mesh-based methods in terms of accuracy, robustness for high p. The talk concludes with practical guidelines for setting up p-Laplacian problems and tuning architectures and loss terms within PhysicsNeMo.

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Date/Time : 27 Jan 2026 15:00
Venue : Conference room (S17 Level 4)
Speaker : Deborah Hendrych, Zuse Institute Berlin

Mixed Integer Nonlinear Problem (MINLP) solvers have traditionally relied on Outer Approximation schemes. Boscia, a recent framework, takes a different approach by leveraging the Frank-Wolfe algorithm—a first-order method for convex optimization—within a branch-and-bound structure. This approach eliminates the need for additional cutting planes while maintaining the oracle-based flexibility of Frank-Wolfe and capitalizing on recent advances in mixed-integer programming solvers. This talk present the intuition behind the framework. highlights the main advantages and showcase the usage and modelling through two examples: portfolio optimization and D-optimal experiment design.

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Date/Time : 26 Jan 2026 15:00
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Ernest Tan, National University of Singapore

Research in quantum information theory, particularly for quantum cryptography or quantum computing, often makes use of numerical methods and software for linear algebra. In earlier literature, most work was based around well-established methods such as semidefinite programming. However, more recent developments have highlighted the need to handle broader classes of convex optimization problems, involving a family of generalizations of the Kullback-Leibler divergence or relative entropy, for which many questions remain open. Furthermore, results from convex analysis have also proven useful in deriving powerful techniques for statistical estimation in quantum information, connected to large-deviation theory. I will present an introductory overview of some active areas of research and open problems in the field, in particular the areas that could benefit from further insights in convex optimization.

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Date/Time : 19 Jan 2026 16:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Jasper Lee, University of California, Davis

Mean estimation is perhaps the most fundamental task in statistics, yet there is a divide between the standard theory and practice. The standard theory recommends taking the sample mean, but whose brittleness forces users to apply ad-hoc outlier removal techniques in practice, leading to documented cases of wrong experimental conclusions. On the other hand, theoretical algorithms designed specifically to mitigate such brittleness (e.g. median-of-means and high-dimensional generalizations) have poor empirical performance that they cannot/should not be adopted either.

In this talk, I’ll discuss our efforts to fully resolve the mean estimation problem, achieving (minimax) estimation error that is sharp even in the leading multiplicative constant. I will present a new multivariate mean estimator that has sharp estimation error in any (small) constant dimensions (in preparation), generalizing our previous 1-dimensional mean estimator (FOCS 2021, ICML 2025). I will also discuss a very simple multivariate mean estimator that achieves sharp error guarantees in the “very high-dimensional” regime (ITCS 2022).

The constant-dimensional mean estimator is joint work with Suraj Mathashery, Paul Valiant and Kun Wang, in preparation. The very high-dimensional mean estimator is joint work with Paul Valiant, which appeared in ITCS 2022.

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Date/Time : 16 Dec 2025 14:00
Venue : S17 #05-12 (Seminar Room 4)
Speaker : Yongchun Li, The Chinese University of Hong Kong

The low-rank constrained optimization arises in various machine learning and operations research problems. It minimizes a linear objective function subject to multiple two-sided linear inequalities and a low-rank domain set. Although the low-rank constrained optimization is generally NP-hard, a viable approach is to convexify the domain set (i.e., replace the domain set with its convex hull), known as “partial convexification.” Partial convexification often leads to a tractable convex relaxation; however, its solution quality lacks theoretical guarantees. To fill this gap, (i) we establish the necessary and sufficient condition under which the partial convexification matches the original low-rank constrained optimization; and (ii) we derive an upper bound on the minimum rank among all the optimal solutions of the partial convexification and prove its tightness. To efficiently solve the partial convexification, we develop a column generation algorithm combined with a rank-reduction algorithm. This combination ensures that the output solution satisfies the theoretical guarantees. Finally, our numerical experiments validate the strength of the partial convexification and the effectiveness of the proposed algorithms.

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Date/Time : 10 Dec 2025 15:00
Venue : S17 #05-12 (Seminar Room 4)
Speaker : Heikki Kälviäinen, Lappeenranta-Lahti University of Technology

The presentation considers computer vision and machine learning in plankton image recognition and animal re-identification, especially from a point of view of applications. Digital image processing and analysis with machine learning methods could enable efficient solutions for various areas of useful data-centric engineering applications. Challenges with imbalance of datasets, domain adaptation, active learning, open set classification, and metric learning of similarities are considered and related examples are given in plankton recognition and animal re-identification, based on the research of LUT Computer Vision and Pattern Recognition Laboratory (CVPRL).

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Date/Time : 24 Nov 2025 14:00
Venue : S17 #04-05 (Seminar Room 2)
Speaker : Wang, Chao, Southern University of Science and Technology

Variational Bayesian inference offers a powerful framework for tackling key challenges in tensor analysis and image restoration, which are critical problems in machine learning and computer vision. This talk presents innovative approaches to address these challenges by integrating Bayesian principles, variational inference, and physical modeling. First, we introduce a Bayesian framework for Tensor Robust Principal Component Analysis (TRPCA) to recover low-rank structures and characterize sparse noise in mixed-noise scenarios. By embedding a low-rank tensor nuclear norm prior and a generalized sparsity-inducing prior within a Bayesian framework, our method learns the optimal tensor nuclear norm and balances low-rank and sparse components automatically. Second, we propose a novel physics-informed optimization framework for jointly solving depth estimation and image restoration from a single defocused image. By modeling the defocused image as a function of a depth map and an all-in-focus (AiF) image based on optical physics, the framework leverages their intrinsic connections. The depth map guides AiF image recovery, while the AiF image regularizes the depth map reconstruction via reconstruction error. A variational inference algorithm, parameterized with deep neural networks, ensures flexibility and high performance.

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Date/Time : 21 Nov 2025 16:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Matthew Shunshi Zhang, University of Toronto

We introduce a new method for analyzing midpoint discretizations of stochastic differential equations (SDEs), which are frequently used in Markov chain Monte Carlo (MCMC) methods for sampling from a target measure π \propto exp(−V). Borrowing techniques from Malliavin calculus, we compute estimates for the Radon-Nikodym derivative for processes which may anticipate the Brownian motion, in the sense that they may not be adapted to the filtration at the same time. Applying these to various popular midpoint discretizations, we are able to improve the regularity and cross-regularity results in the literature on sampling methods. We also obtain a query complexity bound of O(d^{1/4}) for obtaining a ε^2-accurate sample in 𝖪𝖫 divergence, under log-concavity and strong smoothness assumptions for ∇^2 V.

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Date/Time : 19 Nov 2025 16:00
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Srecko Durasinovic, Nanyang Technological University

Mixture models, such as Gaussian mixture models (GMMs), are widely used in machine learning to represent complex data distributions. A key challenge, especially in high-dimensional settings, is to determine the mixture order and estimate the mixture parameters. We study the problem of approximating a target measure, available only through finitely many of its moments, by a mixture of distributions from a parametric family (e.g., Gaussian, exponential, Poisson), with approximation quality measured by the 2-Wasserstein ($\was$) or the total variation ($\tv$) distance. Unlike many existing approaches, the parameter set is not assumed to be finite; it is modeled as a compact basic semi-algebraic set. We introduce a hierarchy of semidefinite relaxations with asymptotic convergence to the desired optimal value. In addition, when a certain rank condition is satisfied, the convergence is even finite and recovery of an optimal mixing measure is obtained. We also present an application to clustering, where our framework serves either as a stand-alone method or as a preprocessing step that yields both the number of clusters and strong initial parameter estimates, thereby accelerating convergence of standard (local) clustering algorithms.

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