Biography
Aniruddha Bora is an Assistant Professor in the Department of Computer Science at Texas State University. His research lies at the intersection of scientific machine learning, scientific reinforcement learning, agentic AI, data-driven scientific computing, and numerical methods, with a focus on developing intelligent, physics-grounded computational approaches for complex scientific and engineering systems.
His research interests include scientific machine learning, physics-informed neural networks, neural operators, reinforcement learning, agentic AI, deep learning, mathematical modeling, computational fluid dynamics, climate and Earth-system modeling, and multiscale heat transfer. A central theme of his research is integrating physical knowledge, machine learning, and autonomous decision-making to develop reliable AI systems for scientific discovery, prediction, optimization, and control.
Prior to joining Texas State University, Dr. Bora was a Postdoctoral Research Associate in the Department of Applied Mathematics at Brown University, where he worked on scientific machine learning and physics-informed computational methods. He has also served as a J. Tinsley Oden Faculty Fellow at the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin.
Dr. Bora received his Ph.D. in Computational Analysis and Modeling from Louisiana Tech University, where his doctoral research focused on numerical and neural-network-based methods for multiscale heat conduction and thermal transport. He earned an M.S. in Applied Mathematics from South Asian University in New Delhi, India, and a B.Sc. in Mathematics from Cotton College in Guwahati, India.
Ph.D., Computational Analysis and Modeling
Louisiana Tech University, Louisiana, USA, 2021
Dissertation: Gradient Preserved Method and Neural Network Method for Solving Heat Conduction Equation with Variable Thermal Conductivity in Double Layered Structures
Advisor: Prof. Weizhong Dai
M.S., Applied Mathematics
South Asian University, New Delhi, India, 2015
Thesis: Finite Difference Method of Order Two in Time and Four in Space for the Solution of Non-Linear Parabolic Equation
Advisor: Prof. Ranjan K. Mohanty
B.Sc., Mathematics
Cotton College, Guwahati, Assam, India, 2013
His research interests include scientific machine learning, physics-informed neural networks, neural operators, reinforcement learning, agentic AI, deep learning, mathematical modeling, computational fluid dynamics, climate and Earth-system modeling, and multiscale heat transfer. A central theme of his research is integrating physical knowledge, machine learning, and autonomous decision-making to develop reliable AI systems for scientific discovery, prediction, optimization, and control.
Prior to joining Texas State University, Dr. Bora was a Postdoctoral Research Associate in the Department of Applied Mathematics at Brown University, where he worked on scientific machine learning and physics-informed computational methods. He has also served as a J. Tinsley Oden Faculty Fellow at the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin.
Dr. Bora received his Ph.D. in Computational Analysis and Modeling from Louisiana Tech University, where his doctoral research focused on numerical and neural-network-based methods for multiscale heat conduction and thermal transport. He earned an M.S. in Applied Mathematics from South Asian University in New Delhi, India, and a B.Sc. in Mathematics from Cotton College in Guwahati, India.
Ph.D., Computational Analysis and Modeling
Louisiana Tech University, Louisiana, USA, 2021
Dissertation: Gradient Preserved Method and Neural Network Method for Solving Heat Conduction Equation with Variable Thermal Conductivity in Double Layered Structures
Advisor: Prof. Weizhong Dai
M.S., Applied Mathematics
South Asian University, New Delhi, India, 2015
Thesis: Finite Difference Method of Order Two in Time and Four in Space for the Solution of Non-Linear Parabolic Equation
Advisor: Prof. Ranjan K. Mohanty
B.Sc., Mathematics
Cotton College, Guwahati, Assam, India, 2013
Research Interests
My research focuses on developing scientific artificial intelligence methods that integrate data, physical knowledge, and autonomous decision-making for complex scientific and engineering systems. My scholarly interests include scientific machine learning, scientific reinforcement learning, agentic AI for autonomous scientific discovery, physics-informed and physics-grounded machine learning, neural operators, and data-driven scientific computing.
I am particularly interested in developing reliable and interpretable AI methods for modeling, prediction, optimization, control, and scientific discovery in multiscale physical systems. Application areas include computational fluid dynamics and turbulence, climate and Earth-system modeling, thermal and energy systems, and other physics-based engineering problems. A broader goal of my research is to create AI systems that can combine governing physical principles with observational and simulation data to accelerate scientific understanding and enable autonomous scientific decision-making.
I am particularly interested in developing reliable and interpretable AI methods for modeling, prediction, optimization, control, and scientific discovery in multiscale physical systems. Application areas include computational fluid dynamics and turbulence, climate and Earth-system modeling, thermal and energy systems, and other physics-based engineering problems. A broader goal of my research is to create AI systems that can combine governing physical principles with observational and simulation data to accelerate scientific understanding and enable autonomous scientific decision-making.
Teaching Interests
My teaching interests include reinforcement learning, scientific machine learning, deep learning, physics-informed machine learning, agentic AI, data analysis and visualization, numerical computing, and computational methods for scientific and engineering applications. I am particularly interested in developing research-oriented courses that connect fundamental concepts in machine learning and computational science with state-of-the-art methods and real-world scientific problems.