Aniruddha Bora, an Indian-American researcher at Texas State University, and his Noema-Laser team won the Popular Choice Award at the Nucleate Texas Demo Day Final, demonstrating the university’s strength in biotechnology innovation.
Competing as one of only eight finalists selected from accelerator chapters across Texas, the team’s accomplishments affirm Texas State University’s place in the region’s promising biotech innovation and research ecosystem, according to a university release.
Along with Bora, an assistant professor in the Texas Department of Computer Science, his team, called Noemalaser, consisted of undergraduate students Arjun Gyawali and Pawan Pradhan, as well as Hope Fearjo and Simar Singh.
Bora’s accomplishment concludes Texas State University’s inaugural participation in the Nucleate Texas Activator program. The program is a non-equity startup accelerator designed to help academic trainees and early-stage faculty bridge the gap between research in the lab and commercialization.
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“This new partnership with the Nucleate Activator program highlights Texas State’s commitment to turning laboratory discoveries into real-world solutions that improve lives,” said Shriek Mandayam, vice president of research at Texas State University.
At the final event on May 29, students connected directly with investors, scientists, and founders, building valuable relationships and gaining first-hand insight into the life sciences industry while exploring future career paths.
Bora holds a PhD in computational analysis and modeling from Louisiana Tech University and previously served as a postdoctoral fellow in the Department of Applied Mathematics at Brown University.
His research focuses on numerical methods, data-driven scientific computing, physically informed machine learning, and scientific machine learning for multiscale physical systems. In particular, we develop novel neural operator frameworks, hybrid numerical machine learning solvers, and multi-fidelity operator approaches with applications in turbulence, climate science, nanoscale heat transfer, and metamaterials.
Read: Indian-American students take lead in University of Texas at Dallas startup awards (March 23, 2026)
He is also actively interested in interpretable machine learning, aiming to build models that not only achieve high predictive accuracy, but also provide insight into the underlying physical and statistical mechanisms.
Bora’s contributions have been published in prestigious venues such as the International Journal of Heat and Mass Transfer. Proceedings of the Royal Society A; Advanced Materials; Applied Mathematics and Computation. Communication in computational physics. neural networks; and AAAI.
Recent co-authors include a paper at the ICLR 2025 CCAI Workshop and a paper at the AI4X Conference on Explainable AI Frameworks for Extreme Weather.
He also serves the scientific community as an external reviewer for major journals and conferences in machine learning, scientific computing, and applied mathematics.
