Location: Public Affairs Building 2270

Alan Vazquez, Assistant Professor
Department of Industrial Engineering, Tecnologico de Monterrey, Mexico

Abstract:

Experimental design is a field of statistics that deals with the planning and analysis of physical experiments, computer experiments, and clinical trials. This talk will present two recent advances in the construction of two-arm clinical trials using optimization algorithms, and in the generation of two-level fractional factorial designs using popular large language models (LLMs). Specifically, the first topic concerns the construction of two-arm trials for personalized medicine applications using novel statistical criteria and integer programming. We will demonstrate the capabilities of our methodology using simulated and real datasets. The second topic concerns a systematic assessment of GPT and Gemini models to construct two-level fractional factorial designs with 8, 16, and 32 runs. To this end, we develop a prompt template using popular prompting techniques. We compare the designs obtained by the LLMs with the optimal designs in terms of statistical criteria.

Bio:

Dr. Alan Vazquez is an Assistant Professor in the Department of Industrial Engineering at Tecnologico de Monterrey, Mexico. His main research area involves the use of optimization algorithms to construct and analyze cost-effective experimental plans. His research is featured in several high-impact statistics journals and implemented in the experimental design software called EFFEX. Dr. Vazquez is part of the editorial board of the Journal of Quality Technology and the Quality Engineering journal, and a council member of the Quality, Statistics, and Reliability (QSR) section of INFORMS. From 2020 to 2022, he was an Assistant Adjunct Professor at the Department of Statistics and Data Science at UCLA.

Location: Public Affairs Building 2270

Yizhe Zhu, Assistant Professor
Department of Mathematics, University of Southern California

Abstract:

The Bethe-Hessian matrix, introduced by Saade, Krzakala, and Zdeborová (2014), is a Hermitian matrix designed for applying spectral clustering algorithms to sparse networks. Rather than employing a non-symmetric and high-dimensional non-backtracking operator, a spectral method based on the Bethe-Hessian matrix is conjectured to also reach the Kesten-Stigum detection threshold in the sparse stochastic block model (SBM). We provide the first rigorous analysis of the Bethe-Hessian spectral method in the SBM under both the bounded expected degree and the growing degree regimes. Joint work with Ludovic Stephan.

Bio:

Yizhe Zhu is an Assistant Professor of Mathematics at the University of Southern California. His research lies at the interface of probability, combinatorics, and data science, with a focus on random matrices and random graphs. Prior to joining USC, Dr. Zhu was a Visiting Assistant Professor at the University of California, Irvine, and a postdoctoral fellow at the Simons Laufer Mathematical Sciences Institute in Berkeley. He received his Ph.D. in Mathematics from the University of California, San Diego in 2021.

Location: Public Affairs Building 2270

Xiaodong Li, Associate Professor
Department of Statistics, UC Davis

Abstract:

This talk develops robust methods for estimating signal-to-noise ratios (SNR) and variance components in high-dimensional linear models. We first show that the random-effects MLE remains consistent and asymptotically normal under substantial model misspecification, including fixed coefficients and heteroskedastic errors. We then extend the method-of-moments framework to multivariate responses, deriving asymptotic distributions using moment identities of the Wishart distribution. The resulting procedures require no sparsity assumptions and provide heteroskedasticity-robust inference through an explicit variance–inflation correction. Simulations demonstrate that the proposed confidence intervals achieve reliable coverage across a wide range of high-dimensional settings.

Bio:

Dr. Xiaodong Li is currently an associate professor in the statistics department at UC Davis. He is mainly interested in methodology and theory in high-dimensional statistics and learning, particularly the interaction between optimization and statistics. His current research focuses include high-dimensional statistical inference, non-convex optimization, and network analysis. Dr. Li has received various awards including NSF Career Award, 2019 Information Theory Paper Award, and 2022-23 UC Davis Chancellor’s Fellow. He is currently serving as an associate editor for Journal of Multivariate Analysis.

Location: Public Affairs Building 2270

Meisam Razaviyayn, Associate Professor
Departments of Industrial and Systems Engineering, Computer Science, Quantitative and Computational Biology, and Electrical Engineering at the University of Southern California

Abstract:

Differential Private Optimization provides a robust framework for safeguarding individual data during training process of machine learning models. However, the substantial noise injection required (typically added after gradient clipping) often disrupts optimizer dynamics and severely degrades performance in large-scale training. To address this challenge, we introduce a general, optimizer-agnostic framework for denoising privatized gradients. Operating as a modular wrapper, our approach uses noisy gradient observations and provides refined estimates to the optimizer, requiring no internal modifications to standard algorithms such as SGD or Adam, without losing any privacy.

We ground our method in the Kalman Filtering Mechanism and optimal despising of Taylor expansion of the objective function. We translate these theoretical insights into practical, memory-efficient filtering strategies (such as low-pass and Kalman filtering) that generate progressively refined gradient estimations. We establish rigorous privacy-utility trade-off guarantees for these mechanisms, ensuring they remain practical for large-scale applications. Extensive experiments across diverse domains, including vision tasks (CIFAR-100, ImageNet-1k) and language fine-tuning (GLUE, E2E, DART), demonstrate that this framework significantly outperforms state-of-the-art DP baselines, effectively mitigating the utility loss caused by privacy-preserving noise.

Bio:

Meisam Razaviyayn (https://sites.usc.edu/razaviyayn) is an associate professor in the departments of Industrial and Systems Engineering, Computer Science, Quantitative and Computational Biology, and Electrical Engineering at the University of Southern California. He also serves as the associate director of the USC-Meta Center for Research and Education in AI and Learning (https://realai.usc.edu) and is a Faculty Visitor at Google Research. Before joining USC, Meisam was a postdoctoral research fellow in the Department of Electrical Engineering at Stanford University. He earned his PhD in Electrical Engineering with a minor in Computer Science from the University of Minnesota, where he also received his M.Sc. in Mathematics. His research and academic efforts have been recognized with numerous awards, including the 2022 NSF CAREER Award, the 2022 Northrop Grumman Excellence in Teaching Award, the 2021 AFOSR Young Investigator Award, and the 2021 3M Nontenured Faculty Award. He received the 2020 ICCM Best Paper Award in Mathematics and the IEEE-DSW Best Paper Award in 2019, along with the Signal Processing Society Young Author Best Paper Award in 2014. Meisam was among the selected individuals by the National Academy of Engineering for the Frontiers of Engineering Symposium in 2023. Additionally, he was a finalist for the Best Paper Prize for Young Researchers in Continuous Optimization in 2013 and 2016, and a silver medalist in Iran’s National Mathematics Olympiad. His research focuses on the design and analysis of fundamental optimization algorithms relevant to the modern AI era.