April Novak is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) in the Department of Nuclear, Plasma, and Radiological Engineering. She holds a joint appointment at the National Center for Supercomputing Applications (NCSA). Her research focuses on multiphysics modeling, thermal-hydraulics, and advanced reactor design, particularly using the MOOSE computational framework. She earned her PhD in Nuclear Engineering from UC Berkeley (2020) and a BS from UIUC (2015). Dr. Novak’s work emphasizes porous media modeling for pebble bed reactors (PBRs), conjugate heat transfer, and Monte Carlo transport methods. She has validated models against experiments like SANA and contributed to tools like Pronghorn and Cardinal. Her research integrates high-performance computing (HPC) for reactor simulations, with recent projects on lead-cooled fast reactors and sodium fast reactor bypass flows. Recipient of the R&D 100 Award (2023) and the Innovations in Nuclear Technology Award (2018), her teaching accolades include the Students' Award for Excellence in Undergraduate Teaching (2024). She advises on multiphysics coupling between neutronics and thermal-hydraulics and collaborates on the Virtual Test Bed (VTB) for advanced reactor models.
Richard Braatz is the Edwin R. Gilliland Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT), part of the School of Engineering. His research focuses on control systems design, multi-scale simulation, and advanced manufacturing systems, particularly in biopharmaceuticals and energy storage. He has made significant contributions to battery technology, mRNA production, and process optimization. Education includes a Ph.D. from Caltech (1993), M.S. from Caltech (1991), and B.S. from Oregon State University (1988). He has authored over 300 publications and holds numerous honors, including membership in the National Academy of Engineering and multiple industry awards for innovation and education. Research interests span control systems, data analytics, and machine learning applied to chemical and biological manufacturing. His lab develops models for viral vector production, battery degradation, and continuous pharmaceutical processes. Collaborations include work on mRNA lipid nanoparticle formulations and fast-charging protocols for lithium-ion batteries.
Raúl A. Radovitzky is the Jerome C. Hunsaker Professor in Aeronautics and Astronautics at MIT, and Associate Director of the MIT Institute for Soldier Nanotechnologies. He holds a Civil Engineer degree from the University of Buenos Aires (1991), an S.M. from Brown University (1995), and a Ph.D. from Caltech (1998). His research focuses on computational solid mechanics, fluid-structure interaction, hypersonic vehicle thermal protection systems, multiscale modeling, and high-performance computing. Key research areas include computational mechanics of materials under extreme conditions, multiscale modeling, and parallel computing. He leads the Hypersonics Research Lab and is affiliated with the Institute for Soldier Nanotechnologies and the Center for Computational Science and Engineering. Notable awards include the MIT AIAA Teaching Award (2021, 2016), Arthur C. Smith Award (2021), and Alan J. Lazarus Advising Award (2018). His work spans theoretical and applied mechanics, with applications in aerospace engineering, materials science, and biomedical safety. He has pioneered numerical methods for fracture mechanics and peridynamics, contributing to advancements in computational modeling of complex systems.
Victor Calo is the John Curtin Distinguished Professor at Curtin University's School of Elec Eng, Comp and Math Sci (EECMS) within the Faculty of Science and Engineering. He holds the CSIRO Professorial Chair in Computational Geoscience and leads the Centre for Optimisation and Decision Science. His work focuses on advancing high-performance computing (HPC) tools for geomechanics, fluid dynamics, and multiphysics modeling in resource extraction industries. Calo earned a Civil Engineering degree from the University of Buenos Aires, followed by a Master’s in Geomechanics and Ph.D. in Civil and Environmental Engineering from Stanford University. Research Interests : Geomechanics, fluid dynamics, flow in porous media, phase separation, HPC, multiphysics modeling, block copolymer self-assembly, and numerical methods for engineering systems. His research emphasizes developing open-source software to democratize advanced computational techniques. Key Achievements : Highly Cited Researcher (2013), 170+ peer-reviewed publications, 2 patents, and over 18 invited talks/keynotes in 2 years. His work bridges computational methods with geoscience applications, including reservoir simulation and material science. Grants & Collaborations : CSIRO Chair endowment, collaborations with KAUST (e.g., Center for Numerical Porous Media), and partnerships in multiphase flow modeling. Active in organizing mini-symposia at international conferences. Labs/Teams : Leads the Computational Geoscience group at Curtin, focusing on HPC-driven solutions for resource extraction challenges and materials science problems.
Colin Denniston is a Professor in the Department of Physics & Astronomy at Western University. His research focuses on multiscale modeling of soft matter systems, including complex fluids, liquid crystals, colloidal suspensions, and polymer dynamics. He specializes in developing novel numerical methods for simulating micro/nano-fluidic systems and studying material properties during curing processes. Key research areas include: Molecular dynamics simulations of polymerization reactions Hydrodynamic interactions in confined flows Photonic band gap engineering using colloidal crystals Interfacial dynamics in coupled lattice-Boltzmann and molecular dynamics frameworks His work bridges theoretical physics, computational modeling, and materials engineering. Notable contributions include advancements in LAMMPS integration for fluid simulations and studies on defect-bonded colloidal structures in cholesteric phases. The Denniston Group actively explores applications in advanced composites and nanotechnology. Professional activities include leading the Denniston Group at Western University and collaborating on projects involving fiber-reinforced polymers for aerospace/automotive industries. He accepts graduate student applications year-round.
Dr. Shunyu Liu is an Assistant Professor in the Department of Automotive Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. She holds a Ph.D. in Mechanical Engineering from Purdue University (2020), an M.S. in Materials Science from North China Electric Power University (2015), and a B.E. in Materials Science and Engineering from the same institution (2012). Dr. Liu directs research in the Additive Manufacturing and Advanced Materials Laboratory. Her research focuses on: Laser-based additive manufacturing of metallic materials Multiscale microstructure modeling of solidification processes Development of high-performance alloys and composites Process-structure-property relationships in manufactured materials Her publications demonstrate expertise in computational modeling of manufacturing processes, experimental characterization of material properties, and development of novel metallic materials including high-entropy alloys and metallic glass composites.
Dr. Qiushi Chen is a Professor of Civil Engineering at Clemson University's College of Engineering, where he leads the Computational Geomechanics Lab. His research spans multiple interdisciplinary fields including computational mechanics, geotechnical engineering, and materials science with applications in biomass processing, extraterrestrial exploration, and earthquake engineering. Dr. Chen received his B.S. in Civil Engineering from Shanghai Jiaotong University (2006), followed by an M.S. (2009) and Ph.D. (2011) in Theoretical and Applied Mechanics with a focus on Geomechanics from Northwestern University. His academic journey has positioned him at the intersection of computational science and practical engineering applications. His primary research interests include computational geomechanics, discrete and finite element methods, extraterrestrial regolith characterization, biomass feedstock preprocessing, and liquefaction hazard assessment. Dr. Chen's work integrates advanced computational techniques with experimental validation to address complex engineering challenges across multiple scales - from particle-level interactions to regional hazard mapping. His research group develops sophisticated numerical models that bridge the gap between theoretical mechanics and practical engineering solutions. Analysis of Dr. Chen's recent publications reveals a strong focus on computational methods for granular materials, with particular emphasis on discrete element modeling (DEM) applications. His work spans terrestrial applications in biomass processing and earthquake engineering to extraterrestrial applications involving lunar and Martian regolith. The integration of machine learning techniques with traditional computational mechanics represents an emerging trend in his research, enabling more efficient and accurate modeling of complex material behaviors. Dr. Chen actively contributes to the professional community as an Associate Member of the American Society of Civil Engineers, Member of the Engineering Mechanics Institute, Member of the Geo-Institute, Member of the Soil Properties and Modeling Committee (ASCE), and Vice-Chair of the Computational Geotechnics Committee (ASCE). In addition to his research, Dr. Chen teaches courses including Introduction to Geotechnical Engineering, Inelastic Materials Modeling, and Computational Mechanics of Granular and Porous Materials. He mentors graduate students interested in computational geomechanics, which sits at the interface of geotechnical engineering, applied mechanics, computational science, and material science. The Computational Geomechanics Lab, directed by Dr. Chen, maintains active research programs in multiple areas including biomass feedstock modeling, extraterrestrial regolith characterization, multiscale liquefaction hazard mapping, and multiphysics problems in porous geomaterials. The lab's work is supported by various funding sources including the U.S. Department of Energy, NSF, NASA, and other federal agencies.
Kuldeep Singh is an Assistant Professor of Earth Sciences at Kent State University , affiliated with the Environmental Science and Design Research Institute . His research focuses on integrating geology, rock mechanics, and fluid dynamics to study physio-chemical processes in porous media, with applications to CO₂ storage, contaminant hydrology, and reservoir engineering. Education: Ph.D., Geoscience, The University of Texas at Austin (2013) M.S., Geological Science, Indiana University at Bloomington (2007) M.Sc., Geology, University of Delhi, India (2001) B.Sc., Geology (Honors), University of Delhi, India (1998) Research Interests: Computational fluid dynamics and digital rock physics Multiscale multiphysics upscaling Reactive transport in porous media Flow dynamics of multiphase systems Environmental geochemistry and contaminant transport Geomechanical characterization of subsurface systems Publications Trends: Recent work emphasizes pore-scale modeling of CO₂ storage, sedimentary structure impacts on permeability, and hyporheic zone dynamics. Key themes include non-Darcy flow regimes, boundary slip effects, and pH-dependent dissolution processes. Advising & Grants: Active in mentoring graduate students and securing funding for projects related to porous media characterization and environmental fluid dynamics. Labs & Affiliations: Core member of Kent State’s Environmental Science and Design Research Institute, collaborating on interdisciplinary projects addressing environmental challenges.
Su Yan is an Associate Professor and Director of Graduate Studies in Howard University's Department of Electrical Engineering and Computer Science. He directs the IBM-HBCU Quantum Center and leads research in computational electromagnetics, nonlinear multiphysics modeling, and machine learning-enhanced simulations. His group develops extreme-scale algorithms for electromagnetic systems with applications in RF devices, quantum technologies, and computational imaging. Current projects involve AI-accelerated metasurface design, microwave-assisted hydrogen generation, and randomized multiscale solvers. Honors include DOE/NSF Early Career Awards, ACES Early Career Award, and IEEE prize papers. He currently advises 9 PhD students and has graduated multiple doctoral candidates in computational electromagnetics. Research Grants: DOE Early Career Award for randomized multiscale algorithms NSF CAREER for neural network-enhanced RF device simulation NSF grants for AI-enhanced metasurfaces and hydrogen catalyst design Ansys collaboration on non-conforming solvers
Robert Kirby is a Professor of Mathematics and Undergraduate Advisor at Baylor University, affiliated with the College of Arts & Sciences and the Department of Mathematics. He holds a Ph.D. from the University of Texas at Austin (2000) and has held academic positions at Texas Tech University, the University of Chicago, and as a Dickson Instructor. His research focuses on automating numerical methods for partial differential equations (PDEs), including finite elements, preconditioners for multiphysics problems, and high-performance computing. Kirby's research interests bridge mathematics and computer science, emphasizing the development of efficient algorithms for PDE simulation using domain-specific languages, Bernstein polynomials, and multicore architectures. His work integrates theoretical analysis with practical software implementation in projects like FEniCS and Firedrake. His publications consistently address finite element methods, numerical stability, and computational efficiency. Recent articles emphasize automated time-stepping, domain truncation, and preconditioning techniques, reflecting a trend toward scalable and user-friendly tools for scientific computing. Grants and Advising: Kirby has secured significant funding, including NSF awards for automated algorithms and Sandia National Laboratory contracts. He advises doctoral students in mathematics and computing, with alumni at Amazon, NSA, and academia. Grants include: NSF CCF award 1117794: Metanumerical computing ($500k) NSF CCF award 0830655: Automated intrusive algorithms ($270k) DOE Early Career Award: Automatic parallel finite elements ($300k) He leads collaborations on open-source projects (FEniCS, Firedrake) and develops tools like FIAT and Irksome to streamline finite element workflows.
Dr. Jeonghun Lee is an Associate Professor of Mathematics at Baylor University's College of Arts & Sciences. His research focuses on numerical methods for partial differential equations—particularly finite element techniques—preconditioners for multiphysics problems, and computational methods for solid/fluid mechanics. He develops robust discretization schemes for complex physical systems like poroelasticity and wave propagation. He holds a Ph.D. from the University of Minnesota and completed postdoctoral research at Aalto University, University of Oslo, and UT Austin's Institute for Computational Engineering and Sciences. His publications advance computational mathematics through error analysis, hybridizable methods, and multiphysics solvers, with applications in geomechanics and materials science.
Dr. Amanda Diegel is an Assistant Professor of Mathematics at Mississippi State University's Department of Mathematics and Statistics. Her research develops numerical methods for partial differential equations with applications in physics, biology, and materials science. Specializes in finite element methods, multigrid solvers, and stability analysis for complex systems including Cahn-Hilliard models, fluid-structure interactions, and liquid crystal dynamics. Published in leading computational mathematics journals on convergence analysis and numerical schemes for multiphysics problems. Holds PhD in Mathematics from University of Tennessee (2015) and completed postdoctoral research at Louisiana State University. Current work advances computational techniques for phase field models and coupled physical systems.
Ilya Avdeev is a Professor of Mechanical Engineering at the University of Wisconsin-Milwaukee (UWM) and holds multiple leadership roles including Director of the Lubar Entrepreneurship Center (LEC), Co-Founder/Executive Director of the UWM Student Startup Challenge, and Director of the Advanced Manufacturing and Design Laboratory. He is on sabbatical during the Spring 2025 semester. His research focuses on real-time modeling (Digital Twin), energy storage systems, and design thinking in engineering education. Education: PhD in Mechanical Engineering from the University of Pittsburgh (2003), MS and BS in Mechanical Engineering from St. Petersburg State Technical University, Russia (1999 and 1997). Research interests span advanced manufacturing, battery safety, and interdisciplinary education innovation. His work integrates computational modeling with practical applications in energy systems and biomedical devices. Over 20 years of academic and industry collaboration have resulted in impactful contributions to both technical and entrepreneurial ecosystems. Notable initiatives include the Milwaukee Regional Energy Education Initiative (as PI) and the UWM Student Startup Challenge, which fosters student entrepreneurship. His publications address topics like battery impact analysis, MEMS simulation, and educational pedagogy.
Nadine Aubry is Professor and Senior Advisor to the Dean of Engineering at Tufts University School of Engineering, Department of Mechanical Engineering. An internationally recognized scholar, she previously served as Provost and Senior Vice President at Tufts (2019-2021), Dean of Engineering at Northeastern University (2012-2019), and Department Head at Carnegie Mellon University. Her leadership spans multiple institutions with global impact. Education: Ph.D. in Mechanical and Aerospace Engineering, Cornell University (1987) M.S. in Mechanical Engineering, Université Scientifique et Médicale de Grenoble B.S., Grenoble - Institut National Polytechnique Research Focus: Dr. Aubry pioneers computational approaches in fluid dynamics, specializing in turbulence modeling, microfluidics, and electrohydrodynamics. Her work integrates dynamical systems theory with nanoparticle manipulation and biofluid applications. Recent research emphasizes machine learning-enhanced methods for flow prediction, convective heat transfer optimization, and aerodynamic design using physics-informed neural networks. Publication Trends: Recent articles demonstrate strong emphasis on machine learning applications in fluid mechanics and thermal systems. Dominant themes include physics-informed neural networks for flow prediction, deep reinforcement learning for active flow control, convolutional networks for aerodynamic optimization, and hybrid AI methods for multiphysics problems across aerospace, electronics cooling, and biomedical domains. Awards & Honors: Elected Member: U.S. National Academy of Engineering Fellow: American Academy of Arts & Sciences, American Physical Society, ASME, AAAS, AIAA G.I. Taylor Medal for Fluid Mechanics Research National Academy of Inventors Fellow C.C. Mei Distinguished Lecturer Grants & Leadership: Secured USDA funding for aging research (2014-2019). Chaired International Union of Theoretical and Applied Mechanics assemblies globally. Serves on National Academy of Engineering prize committees and governance boards.
Ming Hu is a Professor in the Department of Mechanical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on computational thermal science with applications in energy systems, nanotechnology, and advanced materials engineering. Education: Dr.-Ing. in Mechanics from the Institute of Mechanics, Chinese Academy of Sciences (2006) B.S. in Mechanical Engineering from the University of Science and Technology of China (2001) Dr. Hu's research centers on micro-/nano-scale thermal transport phenomena, particularly in low-dimensional materials and nanostructures. His work spans energy nanotechnology, interfacial heat transfer for thermal management, and multi-scale multiphysics modeling of complex energy systems. He employs advanced computational techniques including molecular dynamics, first-principles simulations, and machine learning to investigate phonon transport mechanisms and thermal properties of novel materials. Analysis of recent publications reveals a strategic shift toward artificial intelligence applications in thermal science. Dr. Hu has pioneered graph neural network architectures for high-throughput prediction of phonon properties and thermal conductivity, with significant focus on materials for wide bandgap semiconductor cooling, thermoelectrics, and battery systems. His current work emphasizes accelerating materials discovery through machine learning while maintaining physical interpretability. Scientific Awards: No major awards were documented in the provided information. Dr. Hu leads the Nano Energy and Engineering Laboratory, which specializes in computational thermal science research. The laboratory develops and applies advanced simulation methodologies to investigate heat transfer in next-generation materials. No information regarding graduate students or research grants was available in the provided materials.
