Congratulations to the 2026 SIAM Postdoctoral Support Program Recipients

SIAM is pleased to announce two new researchers will receive funding through the SIAM Postdoctoral Support Program. These early career professionals proposed a program of research collaboration with an established mentor in the SIAM community.

The SIAM Postdoctoral Support Program is made possible by gifts to the SIAM Postdoctoral Support Fund, which was established by Drs. Martin Golubitsky and Barbara Keyfitz. If you’d like to make a contribution to the SIAM Postdoctoral Support Fund, please click here. If you have any questions about giving to SIAM or would like to learn more about the ways that your gift can make a difference, please contact Abby Addy, Director of Development and Corporate Relations, at aaddy@siam.org or (267) 648-3529.

Lander Besabe, Clemson University

Mentor: Alessandro Veneziani, Emory University

Lander Besabe is a postdoctoral fellow in the School of Mathematical and Statistical Sciences at Clemson University, where he works on the development and analysis of finite element methods for fluid-structure interaction problems. His research focuses on the development of robust, accurate, and efficient computational models for complex physical systems, including fluid-structure interaction and geophysical flows, using methods such as filter stabilization techniques and reduced order modeling.

Q: What was the research you proposed?

A: This project aims to develop a high-fidelity, computationally efficient framework to understand how sickle-shaped red blood cells alter the rheology of blood. By combining cell-resolved fluid–structure interaction models with reduced-order modeling and data-assimilation techniques, the research aims to provide mechanistic insights into how sickle cells influence bulk flow properties such as viscosity, pressure drop, and flow profile. Our approach bridges cellular scale simulations and clinical data to study sickle cell disease.

Q: How will this award help you achieve your career goals?

A: As an applied mathematician, I want my work to contribute significantly to both mathematics and society. Nearly 8 million people live with sickle cell disease (SCD) according to the World Health Organization. I believe that this project can help build new techniques in efficiently simulating blood flows at the cellular scale and the development of patient-specific treatment for SCD.

Q: What do you most look forward to in working with your mentor?

A: Professor Alessandro Veneziani is a leading expert in computational cardiology. He makes sure that his models are both mathematically sound and clinically informed by regularly working with biomedical engineers and physicians. This makes me inspired to be trained by someone who is well-versed in collaborating with non-mathematicians and communicates with them effectively. In addition to this, I am excited to experience the strong academic environment at Emory University.

Zhaiming Shen, Georgia Institute of Technology

Mentor: Alexander Cloninger, University of California, San Diego

Dr. Zhaiming Shen is a postdoctoral scholar in the Applied and Computational Mathematics group within the School of Mathematics at Georgia Institute of Technology. He received his Ph.D. in mathematics from the University of Georgia in May 2024. His research revolves around deep learning theory, graph-based learning and clustering, and mathematical data science.

Most recently, his research has focused on the approximation and generalization theory of transformer neural networks and their applications to dynamical systems and multi-agent systems. This involves analyzing the expressive power of attention mechanisms, developing theoretical frameworks for understanding in-context learning both as forward problem and inverse problem, and exploring their connections to the classical kernel methods.

Q: What was the research you proposed?

A: In-context learning (ICL) refers to a model’s capacity to infer and perform a task by recognizing patterns within the prompt itself, without explicit parameter updates. This project studies transformer-based ICL from a kernel-theoretic perspective, with the goal of establishing theoretical and empirical connections between attention mechanisms and kernel regression. In particular, we investigate the relationship between learning anisotropic regression tasks in-context via transformer and performing kernel regression with anisotropic kernels. Leveraging the expressive power of transformer architectures, we analyze in-context regression tasks induced by various families of anisotropic kernels and establish corresponding approximation and generalization error bounds.

Q: How will this award help you achieve your career goals?

A: This award will be instrumental in supporting my long-term career goal of becoming an independent researcher in applied mathematics and data science by providing both financial support and structured mentorship. It will enable me to attend SIAM conferences and workshops to disseminate my research, receive constructive feedback, and build interdisciplinary collaborations. These opportunities, together with mentorship from Professor Alex Cloninger—a leading researcher across multiple areas of applied mathematics—will strengthen my professional network, broaden my research views, and accelerate my transition to a successful academic career.

Q: What do you most look forward to in working with your mentor?

A: I most look forward to learning from my mentor’s broad perspective and deep expertise at the intersection of applied mathematics and data science. Working with Professor Alex Cloninger, I am particularly excited to gain insight into how to formulate impactful research problems, connect theory with real-world applications, and communicate ideas effectively across disciplines. I also value the opportunity to learn from his experience in building interdisciplinary collaborations and mentoring students, which will be essential as I develop my own independent research program.