Akwum Onwunta

Akwum Onwunta is an Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University's Rossin College of Engineering. His research focuses on uncertainty quantification, optimization under uncertainty, and scientific machine learning, with applications in computational mathematics and engineering systems.

Education:

• Ph.D. in Applied Mathematics (Max Planck Institute & Otto von Guericke University, 2016)
• Ph.D. in Economics (Justus Liebig University, 2010)
• Diplome d'Universite in Mathematical Models in Economics and Finance (University of Paris 1, 2007)
• M.Sc. in Physical and Mathematical Analysis (University of Stellenbosch, 2006)
• B.Sc. in Mathematics (Michael Okpara University of Agriculture, 2003)

Research interests include developing tensor-based methods for stochastic optimization, Bayesian inverse problems, and low-rank numerical techniques for high-dimensional systems. His work bridges applied mathematics, computational science, and engineering applications.

His recent publications emphasize tensor train approximations, quantum algorithms for differential equations, and deep learning approaches to parameterized PDEs. Notable contributions include TTRISK algorithms for risk-averse optimization and low-rank solvers for stochastic Navier-Stokes equations.

No scientific awards explicitly listed in the provided text. Lehigh University received a $800K DOE grant in 2025 for advanced computational materials modeling, potentially related to his research.

Advising and grants: No formal advisees listed. Active in grant-funded projects such as the DOE initiative mentioned above. Collaborates on interdisciplinary teams focused on computational materials modeling and uncertainty-aware optimization.

Lab/Team: Engaged in computational engineering research within the Rossin College, though specific lab names are not detailed in the text.