Aniruddha Bora is an Assistant Professor in the Department of Computer Science at Texas State University. He earned his Ph.D. in Computational Analysis and Modeling from Louisiana Tech University and was previously a Postdoctoral Research Associate in the Division of Applied Mathematics at Brown University. His research focuses on numerical methods, data-driven scientific computing, physics-informed machine learning, and scientific machine learning for multiscale physical systems. He develops neural-operator frameworks, hybrid numerical–machine learning solvers, and multi-fidelity operator methods, with applications in turbulence, climate science, nanoscale heat transfer, and metamaterials. He is also interested in interpretable machine learning, with the goal of building models that provide both accurate predictions and insight into underlying physical and statistical mechanisms. Dr. Bora’s work has appeared in venues including the International Journal of Heat and Mass Transfer, Proceedings of the Royal Society A, Advanced Materials, Applied Mathematics and Computation, Communications in Computational Physics, Neural Networks, and AAAI. His recent co-authored work includes an ICLR 2025 CCAI Workshop paper and an AI4X conference paper on explainable AI for extreme weather. He also serves as an external reviewer for leading journals and conferences in machine learning, scientific computing, and applied mathematics, and is an ATPESC alumnus.