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Vemuri BalakotaiahView profile

Professor

• Chemical Engineering
• Catalysis
• Reaction Engineering
• +5 more

Dr. Vemuri Balakotaiah is a Professor and Hugh Roy and Lillie Cranz Cullen Distinguished University Chair in the William A. Brookshire Department of Chemical and Biomolecular Engineering at the University of Houston. He holds a Ph.D. in Chemical Engineering (University of Houston, 1982) and a B.Tech in Chemical Engineering from IIT Madras (1978). His research focuses on catalytic reactor design, cryogenic storage systems, and fluid dynamics in extreme conditions. Key research areas include: Autothermal and oxidative dehydrogenation processes using novel catalysts Electrified modular reactors for endothermic reactions Insulation strategies for large-scale liquid hydrogen storage Microgravity fluid mechanics from ISS experiments Multi-scale modeling of monolith and packed-bed reactors Recent publications highlight advancements in AI-driven process optimization, bifurcation analysis of reactor instabilities, and insulation material innovations. His work addresses energy transition challenges through sustainable chemical production and efficient hydrogen storage solutions. Recipient of the prestigious Cullen Chair distinction, his contributions have advanced reactor engineering and catalytic process design. Current projects explore synergies between AI modeling and experimental reactor validation, with emphasis on scalable clean energy technologies.

Mostafa MomenView profile

Assistant Professor

• Environmental Fluid Mechanics
• Turbulence
• Coastal Engineering
• +7 more

Mostafa Momen is an Assistant Professor at the University of Houston, affiliated with the Cullen College of Engineering's Department of Civil and Environmental Engineering. His research focuses on environmental fluid mechanics, turbulence, and computational modeling. He leads the Environmental Fluid Mechanics Lab, addressing challenges in hurricane boundary layers, coastal resiliency, and wind energy through theoretical, numerical, and data-driven approaches. Education: Ph.D., Civil and Environmental Engineering, Princeton University (2016) Ph.D. Exchange Scholar, Mechanical Engineering, MIT (2014–2015) M.A., Civil and Environmental Engineering, Princeton University (2014) B.Sc., Civil and Environmental Engineering, Sharif University of Technology (2012) Research interests include fluid dynamics, urban canopy modeling, reduced-order modeling, and machine learning integration. He actively seeks talented Ph.D. students passionate about coding, mathematical modeling, and environmental fluid dynamics. Prior to UH, he held postdoctoral positions at Stanford University, Columbia University, and Princeton University. His lab emphasizes interdisciplinary solutions to environmental engineering challenges, leveraging high-performance computing and big data analytics. Contact via email or LinkedIn for collaboration opportunities.

Panos KoniavitisView profile

Lecturer

• Sustainability in Aviation
• Aircraft Combustion Processes
• Aviation Management
• +3 more

Panos Koniavitis is a Lecturer in Aviation at the Guildhall School of Business and Law, London Metropolitan University. He teaches Airline, Airport and Aviation Management programs at both undergraduate and postgraduate levels, focusing on research methods, sustainability, and airline management. His PhD from Imperial College London (UK) centered on aircraft engine combustion processes, followed by research at the University of Cambridge collaborating with Rolls-Royce. His research emphasizes sustainable aviation, including fuels, airports, and strategic reporting. He has presented at major conferences like the European Combustion Meeting and International Conference on Numerical Combustion, contributing to combustion modeling and fuel surrogate development. His publications explore reduced chemical mechanisms and computational methods for aviation fuel analysis. Panos has no listed awards but maintains active engagement in combustion engineering research. His advising record and grants are not detailed in the provided text. He is associated with the Guildhall School's aviation management initiatives but no specific labs/teams are mentioned.

Xuemin TuView profile

Professor

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +3 more

Xuemin Tu is a Professor in the Department of Mathematics at the University of Kansas, serving as the Scheduling Officer. His research focuses on scientific computing, numerical analysis, and applied mathematics, emphasizing domain decomposition methods, BDDC algorithms, and data assimilation. He is affiliated with the College of Liberal Arts & Sciences and actively contributes to interdisciplinary computational science. Research interests include iterative methods for partial differential equations, numerical solutions of fluid dynamics problems, and the development of efficient algorithms for large-scale simulations. His work bridges theoretical analysis and practical applications in engineering and geophysics. Key trends in his recent articles (2018–2025) highlight advancements in BDDC algorithms for fluid and porous media systems, implicit sampling techniques for data assimilation, and machine learning applications in image recognition. He also explores stochastic models for parameter estimation and uncertainty quantification. No scientific awards or grants are explicitly listed in the provided text. Advising roles or student mentorship details are not included, though his research areas suggest contributions to graduate training in computational mathematics. Labs or collaborative teams are not detailed here, but his affiliations imply involvement with computational science initiatives at the University of Kansas.

Dan WilsonView profile

Assistant Professor

• Control Theory
• Neuroscience
• Cardiology
• +5 more

Dan Wilson is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He holds a PhD in Mechanical Engineering from the University of California, Santa Barbara (2016), and dual undergraduate degrees from the University of Washington (BS Mechanical Engineering, 2011) and Whitman College (BA Natural and Mathematical Sciences, 2011). His research focuses on medical applications of dynamical systems, model reduction techniques for nonlinear systems, and optimal control strategies with applications to neuroscience and cardiology. He has pioneered methods for analyzing and controlling oscillatory systems, particularly in cardiac arrhythmias and neural populations. Notable contributions include phase-amplitude reduction frameworks and isostable coordinate approaches for high-dimensional systems. Wilson has received prestigious awards, including the SIAM Life Sciences Early Career Prize (2022) and an NSF CAREER Award (2022). He has secured over $1.5 million in grants, including NSF funding for nonlinear model reduction and collaborative projects in fluid dynamics and AI-driven decision-making. He teaches courses in control systems, linear systems theory, and nonlinear dynamics. He advises PhD students in areas such as cardiac chaos modeling, circadian rhythm synchronization, and fluid flow reduction. His work bridges theoretical dynamical systems with practical biomedical applications, emphasizing data-driven methodologies and interdisciplinary collaboration.

Kai SunView profile

Professor

• Power Systems
• Electrical Engineering
• Power Grid Stability
• +5 more

Roles & Affiliations: Kai Sun is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He is part of the Tickle College of Engineering and holds an IEEE Fellow distinction. His affiliations include NSF/DOE's CURENT Engineering Research Center and collaborations with institutions like EPRI and Arizona State University. Education: PhD in Control Science and Engineering (Tsinghua University, 2004) and B.S. in Automation (Tsinghua University, 1999). Postdoctoral studies at Arizona State University and University of Western Ontario, with visiting scholar appointments at Harvard University. Research Interests: Focuses on power grid stability, dynamics, and control of complex systems. Key areas include cascading failures, electromagnetic transient simulation, voltage stability, renewable energy integration, and semi-analytical methods. His work emphasizes real-time stability analysis, wide-area control, and high-performance computing for grid resilience. Recent Trends in Articles: Recent publications emphasize advanced simulation techniques (e.g., EMT simulation, semi-analytical methods), cascading failure analysis, and participation factor-based approaches. He explores nonlinear oscillations, inverter-based resource impacts, and hybrid EMT-Transient Stability models for grid reliability. Awards & Honors: 2024 IEEE Fellow, 2024 University of Tennessee Research Award, 2016 NSF CAREER Award, and multiple best paper awards from IEEE conferences. Recognized for contributions to power system stability and grid resilience. Advising & Grants: Advised 11 PhD and 2 MS students, with alumni in industry and academia (e.g., National Renewable Energy Lab, Dominion Energy). Leads research funded by NSF, DOE, and industry partnerships, focusing on grid stability, simulation tools, and control strategies. Labs & Teams: Leads a research group specializing in power systems, with collaborations on open-source simulation tools (e.g., ParaEMT, RAPID). Active in IEEE PES committees and task forces on forced oscillations and grid resilience.

Ben GrierView profile

Teaching Professor

• Computational Fluid Dynamics
• Hypersonic Aerodynamics
• Engineering Education
• +3 more

Dr. Ben Grier is an Assistant Teaching Professor in the Department of Mechanical Engineering at Colorado State University, part of the Walter Scott, Jr. College of Engineering. He holds a Ph.D. and B.S. in Mechanical Engineering from Clemson University. His career blends academic teaching with industry research, focusing on computational fluid dynamics (CFD), hypersonic aerodynamics, and innovative pedagogy. Education: Ph.D. Mechanical Engineering, Clemson University (2014) B.S. Mechanical Engineering, Clemson University (2009), Magna Cum Laude Postdoctoral Scholar, The Ohio State University (2020) Research & Teaching Interests: Dr. Grier specializes in CFD verification techniques, reduced-order modeling for hypersonic vehicles, and active learning methodologies. He has pioneered flipped-classroom approaches and developed MATLAB-based interactive curricula. Notably, he designed a virtual escape room project for engineering students, showcased at Moby Arena. Awards: 2023 Charles Ellison MacQuigg Award for Outstanding Teaching 2023 Outstanding Service to Undergraduate Students 2023 Outstanding Teaching in Engineering Education (OSU) Advising & Leadership: As MTI Coordinator for the College of Engineering, he promotes evidence-based teaching practices. He mentors graduate students and led curricular redesigns for first-year engineering programs. His service includes AIAA student advising and engineering technology coordination. Labs & Teams: Collaborates with the Department of Mechanical Engineering on active-learning lab infrastructure, including Arduino integration and accessibility initiatives for students without personal computers.

Tyrone VincentView profile

Professor

• Control Systems
• System Identification
• Nonlinear Estimation
• +3 more

Tyrone Vincent is a Professor in the Department of Electrical Engineering at the Colorado School of Mines. His teaching and research focus on control systems, with applications in energy technologies and dynamic systems. He directs the SEED Lab (Systems Exploration, Engineering, and Design Laboratory), focusing on nonlinear estimation, system identification, and energy applications. His research spans fuel cell control, battery management systems, electric vehicle grid integration, and medical technology applications. His recent publications demonstrate strong interdisciplinary collaboration across energy systems, biomedical engineering, and control theory, with practical applications in renewable energy and healthcare technology. His work frequently employs advanced modeling techniques including model-predictive control, physics-based modeling, and system identification approaches applied to complex engineering challenges.

Cory HauckView profile

Professor

• Applied Mathematics
• Computational Kinetic Theory
• Multiscale Methods
• +3 more

Cory Hauck is a Joint Faculty Professor at the University of Tennessee, Knoxville (UTK) and Oak Ridge National Laboratory (ORNL). He holds a Ph.D. in Mathematics from the University of Maryland. His primary affiliation is with the Department of Mathematics in the College of Arts and Sciences at UTK. His research focuses on applied mathematics, particularly computational kinetic theory and multiscale methods. Key areas include radiative transfer, high-order numerical methods, and plasma simulations. Hauck’s work bridges theoretical developments with practical computational tools, emphasizing robust and accurate approximations for complex physical systems. His publications span topics like entropy-based closures, moment methods, and hybrid computational techniques for kinetic equations. Notable contributions include advancements in Boltzmann collision operator solvers, filtered spherical harmonics, and sparse dynamics for PDEs. Collaborations with institutions like ORNL and Los Alamos National Laboratory highlight his engagement in applied scientific computing. Hauck’s research demonstrates a strong emphasis on interdisciplinary applications, such as thermal radiation modeling and plasma physics. His work addresses challenges in high-energy density systems, non-equilibrium radiation, and data-driven methods for partial differential equations. While no specific awards are listed, his extensive publication record and joint faculty role reflect his expertise in computational mathematics and its applications. His lab or team affiliations likely align with UTK’s mathematics department and ORNL’s computational science initiatives.

Sigal GottliebView profile

Professor

• Numerical Analysis
• Scientific Computing
• Hyperbolic PDEs
• +2 more

Dr. Sigal Gottlieb is Chancellor Professor of Mathematics at UMass Dartmouth and founding director of the Center for Scientific Computing and Data Science Research. A Harvard Business School PLDA graduate and computational mathematics expert, her research develops high-order numerical methods for simulating hyperbolic PDEs with applications in gravitational wave physics. Research Focus: Specializes in strong stability preserving (SSP) time discretizations, WENO/spectral methods for discontinuous problems, and GPU-accelerated algorithms for computational relativity. Current NSF-funded projects include developing efficient black hole spectroscopy techniques. Leadership: Established UMass Dartmouth's computational science research hub and developed new academic programs including the Data Science BS/MS degrees. Recognized as SIAM and AWM Fellow for contributions to computational mathematics.

Alfa HeryudonoView profile

Professor

• Radial Basis Function Methods
• Numerical Analysis
• Scientific Computing
• +3 more

Alfa Heryudono is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth , where he has been since 2008. His academic journey includes a BS in Physics (2000), MS in Mathematics (2002), and PhD in Applied Mathematics (2008). BS: University of Indonesia MS: Southern Illinois University Edwardsville PhD: University of Delaware Marie Curie Visiting Fellow: Uppsala University (2010-2012) His research focuses on Radial Basis Function (RBF) methods , Scientific Computing , and Numerical Methods for PDEs , with applications in Mathematical Modeling and Engineering Problems . Recent publications highlight advancements in least squares RBF-FD , convection-diffusion equations , and meshless collocation techniques . His work is supported by grants from NSF, ONR, and Marie Curie FP7. Scientific Awards: ONR Grant: Data-driven Hydrodynamic Models ($74,409) ONR Grant: Nonlinear System Learning ($438,284) ONR Grant: Marine Technology Data Learning ($435,393) Marie Curie FP7 Fellowship (2010-2012) Heryudono leads the Mathematical and Computational Consulting (MC²) course , a multidisciplinary engagement program where students tackle real-world problems from industries and research groups. His administrative roles include Co-Director of the Data Science MS program and EAS PhD program. He actively contributes to reproducible research by sharing MATLAB codes for adaptive RBF methods and serves as Associate Editor for the Journal of Approximation Software (JAS) and SIAM member.

Yanlai ChenView profile

Professor

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +3 more

Yanlai Chen is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth and serves as Chief Research Officer. He holds a PhD (2007) and MS (2007) from the University of Minnesota Twin Cities, and a BS (2002) from the University of Science and Technology of China. His research integrates numerical analysis, scientific computing, and machine learning. Research interests focus on numerical PDEs, model reduction, machine learning applications in scientific computing, uncertainty quantification, and high-performance computing algorithms. His work bridges theoretical mathematics with practical computational challenges. His publications demonstrate consistent focus on physics-informed neural networks (2021-2024), model reduction techniques (2019-2024), and computational methods for differential equations. Recent work shows increased emphasis on machine learning integration with traditional numerical methods. He has supervised 6 doctoral dissertations and leads the NSF-funded ACCOMPLISH program supporting STEM education through contextualized computing curricula.

Yangwen ZhangView profile

Assistant Professor

• Applied Mathematics
• Scientific Computing
• Numerical Analysis
• +7 more

Yangwen Zhang is an Assistant Professor in the Department of Mathematics at the University of Louisiana at Lafayette. He holds a Ph.D. in Mathematics from the Missouri University of Science and Technology (2018). Prior to his current position, he completed postdoctoral research at the University of Delaware (2018–2021) and Carnegie Mellon University (2021–2023). His research focuses on applied mathematics, scientific computing, numerical analysis, and data science. Core areas include computational methodologies for partial differential equations (PDEs), hybridizable discontinuous Galerkin (HDG) methods, model order reduction for PDEs, optimal control, inverse problems, and engineering applications. Key research trends in his publications emphasize HDG methods for PDEs, with applications in fluid dynamics, semiconductor devices, and electromagnetic wave propagation. Recent work highlights advancements in divergence-free approximations for Stokes equations, superconvergence analysis of HDG schemes, and spectral methods for thin surface modeling. His academic career includes postdoctoral fellowships at top institutions, reflecting expertise in numerical analysis and computational mathematics. No scientific awards are explicitly mentioned, but his extensive publication record underscores contributions to the field. Advising and grants are not detailed in the provided text, but his research collaborations span institutions like Missouri S&T, Delaware, and CMU. He currently contributes to the Mathematics Department at UL Lafayette, bridging theoretical and applied computational mathematics.

Daniel FloryanView profile

Assistant Professor

• Fluid Mechanics
• Nonlinear Dynamics
• Bio-inspired Flows
• +3 more

Dr. Daniel Floryan is the Kalsi Assistant Professor of Mechanical & Aerospace Engineering at the University of Houston, affiliated with the College of Engineering. His research focuses on fluid mechanics, nonlinear dynamics, bio-inspired flows, applied mathematics, and scientific machine learning. He holds a PhD from Princeton University (2019) and undergraduate degrees from Cornell University (BS and BA). Key research interests include turbulence dynamics, flow control, and optimization of swimming and flying systems. His work bridges experimental, computational, and theoretical approaches. Recent studies explore particle dynamics in Rayleigh-Bénard convection, energy-efficient propulsion, and data-driven modeling of fluid systems. Notable awards include the Teaching Excellence Award (2024), Thomas J. R. Hughes Fellowship (2021), and the Sibley Prize (2014). He teaches courses on fluid mechanics and machine learning applications in fluids. His lab (FLO Lab) investigates fluid-thermal interactions and bio-inspired locomotion, emphasizing practical innovations in propulsion and energy systems. Publications span over a decade, addressing topics like flexible foil propulsion, turbulence structure decomposition, and optimal swimming gaits. Collaborations with industry and academic partners drive applied fluid dynamics research with real-world impact.

Yingjie TangView profile

Research Professor

• Multi-physics modeling
• Fluid mechanics
• Heat transfer
• +7 more

Dr. Yingjie Tang is a Research Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston. His research focuses on multi-physics modeling, fluid mechanics, heat transfer, and numerical simulations using CFD and FEA. He holds a Ph.D. in Mechanical Engineering from Texas A&M University. His work addresses challenges in pipeline integrity, multiphase flow dynamics, and advanced actuator design. Dr. Tang's research interests span computational fluid dynamics, thermodynamic systems, and corrosion analysis. He has contributed to studies on cavitating flows, two-phase flow modeling, and subsea engineering systems. His recent work includes electromagnetic inspection of pipelines, high-pressure flow dynamics, and analytical solutions for transient flows. His publications highlight expertise in both experimental and computational methods, with applications in offshore engineering, nanomaterials, and energy systems. Collaborations involve industries and academic partners in mechanical, aerospace, and petroleum engineering domains.