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Gyeong HwangView profile

Professor

• Multiscale modeling
• Nanomaterials synthesis
• Energy storage systems
• +4 more

Gyeong Hwang is a Matthew Van Winkle Regents Professor of Chemical Engineering at The University of Texas at Austin. He leads the Hwang Research Group focused on computational materials discovery and design for energy and electronic applications. His work emphasizes multiscale modeling of nanostructured materials, with applications in energy storage/conversion, carbon capture, and semiconductor processing. Educational Qualifications: Ph.D., Chemical Engineering, California Institute of Technology (1999) M.S., Applied Physics, California Institute of Technology (1998) M.S., Chemical Engineering, Seoul National University (1993) B.S., Chemical Engineering, Seoul National University (1991) Research Interests: Hwang's research integrates first-principles modeling with experimental validation to address challenges in: - Surface chemistry and interfacial reactions - Nanostructured materials synthesis - Electrochemical device fabrication - CO₂ capture mechanisms His group develops computational tools for predicting material behaviors at atomic and continuum scales. Recent Publications Trends: Publications (2023-2025) focus on: - Solid-state battery interfaces - Plasma-enhanced material deposition - Ionic liquid interactions - Thermal/spatial transport phenomena - Electrochemical reaction mechanisms Awards: NSF CAREER Award (2005) Electrochemical Society's F.M. Becket Memorial Award (1999) Korean Chemical Engineering Service Award (2010) Advising & Grants: Leads interdisciplinary research funded by NSF, industry partnerships, and regents' endowments. Active in graduate student training through courses like ChE 379 (Molecular Simulation) and ChE 348 (Numerical Methods). Labs/Teams: The Hwang Research Group operates state-of-the-art computational facilities for quantum mechanics simulations and multiscale modeling. Collaborates with experimental groups globally on materials prototyping.

Anil N. HiraniView profile

Professor

• Differential Equations
• Numerical Analysis
• Computational Topology
• +5 more

Anil N. Hirani is a Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Liberal Arts & Sciences. He holds a PhD from the California Institute of Technology (2003) in Computer Science with minors in Mathematics and Control and Dynamical Systems. His academic journey includes roles as Assistant Professor (Computer Science, UIUC, 2005–2013) and Associate Professor (Mathematics, UIUC, 2013–2022) before becoming a full Professor in 2022. His research focuses on the interplay between geometry/topology and algorithms, with emphasis on structure-preserving discretizations of exterior calculus and differential geometry. Key areas include Discrete Exterior Calculus (DEC), numerical methods for PDEs, computational topology, and machine learning applications. He has organized workshops, such as the 2025 Discrete Exterior Calculus workshop at IMSI, and contributed to software like PyDEC. Education: PhD, Caltech (2003); MS in Computer Science (Stanford); Undergraduate degree in Computer Science (BITS Pilani, India). Awards include the NSF CAREER Award (2007–2012). Teaching includes courses on Differential Geometry (MATH 423), Vector and Tensor Analysis (MATH 481), and Computational Mathematics (MATH 490). He has advised numerous PhD students, notable among them Kaushik Kalyanaraman and Vaibhav Karve. Articles span DEC applications in fluid dynamics, cohomology computations, and machine learning. His work bridges theoretical foundations with practical applications in engineering and computer science.

David L. DarmofalView profile

Professor

• Computational Fluid Dynamics
• Numerical Analysis
• Uncertainty Quantification
• +3 more

David L. Darmofal is the Vice Chancellor for Undergraduate and Graduate Education and the Jerome C. Hunsaker Professor of Aeronautics and Astronautics at MIT. He leads the Aerospace Computational Science & Engineering (ACSEL) Lab and contributes to the MIT Center for Computational Science & Engineering (CCSE). His research focuses on computational methods for PDEs (especially fluid dynamics) and engineering education innovation. He holds a BS from the University of Michigan and SM/PhD from MIT, with postdoctoral work at the University of Michigan. Notable awards include the MacVicar Faculty Fellow (2004), Earll M. Murman Award (2011), and NSF CAREER Award (1998). Education: B.S.E., University of Michigan, 1989 S.M., MIT, 1991 Ph.D., MIT, 1993 Affiliations: MIT Schwarzman College of Computing Aerospace Computational Design Laboratory (ACSEL) His research emphasizes higher-order adaptive finite element methods, space-time mesh adaptation, and turbulence modeling. He teaches courses in computational methods and fluid dynamics. Recent projects include the Metris open-source meshing software and studies on sonic boom propagation. His work bridges computational science and engineering education, with a focus on evidence-based pedagogy. Awards & Recognition: Michael M. Byram Visiting Professorship (2021) Common Ground Excellence in Teaching Award (2024) AIAA Student Chapter Teaching Awards (2005, 2013) Bisplinghoff Fellow & Alumni Merit Award (2012) Advising & Grants: Over 130 peer-reviewed publications, leadership in MIT’s Common Ground initiative, and mentorship of postdocs/UROPs (e.g., Emily Williams, Lucien Rochery). Active in interdisciplinary collaborations, including DOE projects on physics-informed PDEs and NASA’s CFD Vision 2030 study.

Ang Kok KengView profile

Associate Professor

• Structural Stability and Vibration
• Smart Adaptive Structures
• Computational Mechanics
• +5 more

K.K. Ang is an Associate Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS) Faculty of Engineering. He has been affiliated with NUS since 1987 and currently serves as Director of the Centre for IT and Applications (CITA Engineering) within the Faculty of Engineering. Qualifications: BEng (1st Class) from University of Singapore (1977) MEng from National University of Singapore (1980) PhD from University of New South Wales (1987) His research focuses on structural stability and vibration , smart adaptive structures , very large floating structures , computational mechanics , and meshless methods . Recent publications analyze carbon nanotube composites, high-speed rail dynamics, and offshore structural interactions. His awards include the NUS Teaching Excellence Award , Engineering Educator Award , and the IES/IStructE Best Structural Paper Award . He has taught courses in Computing, Structural Mechanics, Steel Design, and Finite Element Methods. Professor Ang is a Singapore-registered Professional Engineer (1991) and serves on the Singapore Structural Steel Society council. His work bridges structural engineering, IT applications, and advanced computational modeling.

Tilak ChandratillekeView profile

Professor

• Computational Fluid Dynamics
• Heat and Mass Transfer
• Thermal Design
• +3 more

Professor Tilak Chandratilleke is a faculty member in the School of Civil and Mechanical Engineering at Curtin University, part of the Faculty of Science and Engineering. He holds a PhD from the University of Cambridge and has extensive post-nominals including MIEAust, CPEng, and MASME. His research focuses on advanced thermal engineering, computational fluid dynamics (CFD), and heat transfer optimization. Key areas of interest include thermal energy storage systems, fluid flow in curved ducts, and thermal design for industrial applications. He also serves in the Office of the Provost, contributing to academic governance. Research Interests: - Computational Fluid Dynamics (CFD) modeling of complex thermal systems. - Heat and mass transfer in energy storage and manufacturing processes. - Design and analysis of heat exchangers and thermal recuperators. - Fluid dynamics in curved geometries and secondary vortex structures. - Applications in renewable energy systems and advanced manufacturing. Selected Publications (2022–2010): - Investigated high-temperature thermal energy storage using CaCO₃/Al₂O₃ reactors (2022). - Developed numerical models for metal hydride thermal storage systems (2021). - Analyzed boiling heat transfer in curved ducts and laser-assisted machining thermal effects (2020–2019). - Advanced CFD methodologies for convective boiling and turbulent flow modeling (2018–2016). - Pioneered studies on Dean vortices and microfluidic heat enhancement (2011–2010). Teaching: - Thermodynamics and Heat Transfer. - Fluid Mechanics and Engineering Applications. Labs/Teams: - Involved in Curtin’s thermal energy and advanced manufacturing research groups. - Collaborates with industry partners on renewable energy and thermal system optimization projects.

David Del Rey FernándezView profile

Assistant Professor

• Numerical Simulation of PDEs
• Summation-by-Parts Methods
• Continuous and Discontinuous Galerkin Schemes
• +2 more

David Del Rey Fernández is Assistant Professor and Pratt & Whitney Canada Chair in Industrial Artificial Intelligence in the Department of Applied Mathematics at University of Waterloo. His research develops efficient numerical algorithms for solving partial differential equations on high-performance systems. He holds a PhD from University of Toronto and previously worked at NASA Langley Research Center. Research focuses on robust numerical methods, mesh adaptation, and machine learning acceleration. His work includes entropy-stable schemes, summation-by-parts methods, and discretizations for compressible flows. Recent publications address Lyapunov-consistent discretizations and scalable reduced-order modeling.

H. Siegfried StiehlView profile

Professor

• Computer Vision
• Computational Neuroscience of the Visual System
• Cognitive Science
• +3 more

Prof. Dr.-Ing. H. Siegfried Stiehl is a retired Senior Professor (until Sept 2021) at the Department of Informatics, University of Hamburg. He previously held roles including Dean of the Faculty of Mathematics, Computer Science, and Natural Sciences (2001–2006), Vice President for Research (2007–2013), and Head of the Image Processing Research Group. His academic journey includes a PhD from TU Berlin (1980) and a Habilitation in Computer Vision (1987). Education: 1973: Ing. Degree in Ingenieur-Informatik, Fachhochschule Furtwangen 1976: Diploma in Computer Science, TU Berlin 1980: Dr.-Ing. Dissertation on medical image processing, TU Berlin Research focuses on Computer Vision , Computational Neuroscience , and Cognitive Science , with contributions to medical image registration, 3D landmark detection, and biomechanical modeling. Key projects include the EU-funded 'COVIRA' consortium (1989–1995) and leadership in the SFB 950 'Manuscript Cultures' project (2015–2019). His 110+ publications span biomedical image registration, elastic deformation algorithms, and real-time signal processing. Notable collaborations include work with institutions like the University of Pennsylvania, University of Birmingham, and Philips Research. Leadership roles include organizing scientific events, serving on editorial boards (e.g., Biological Cybernetics), and founding the Interdisciplinary Nanoscience Center Hamburg (INCH) in 2001. His research has addressed challenges in neurosurgical interventions, VLSI implementation of neural networks, and interdisciplinary education.

Lynn KistlerView profile

Professor

• Plasma Physics
• Space Weather
• Magnetospheric Dynamics
• +3 more

Lynn Kistler is a Professor in the Department of Physics & Astronomy at the University of New Hampshire (UNH), part of the College of Engineering and Physical Sciences. Her research focuses on plasma physics, space weather, and magnetospheric dynamics, particularly investigating the interactions between the solar wind and Earth's magnetosphere-ionosphere system. She holds a Ph.D. in Physics from the University of Maryland, along with a B.S. from Harvey Mudd College. Dr. Kistler's work emphasizes understanding plasma processes such as ion outflow from the ionosphere, magnetic reconnection, and storm-time magnetospheric evolution. She has led studies using data from missions like the Van Allen Probes, Solar Orbiter, and Cluster, contributing to advancements in instrumentation (e.g., the SWA suite) and computational modeling. Her research bridges observational analysis, theoretical frameworks, and machine learning to address challenges in space weather prediction and plasma dynamics. Key areas of her research include the role of ionospheric ions (O⁺, H⁺) in plasma sheet dynamics, the effects of geomagnetic storms on ring current formation, and the behavior of heavy ions in near-Earth space. She has authored or co-authored over 260 publications, spanning journals like Nature Communications , Geophysical Research Letters , and Journal of Geophysical Research . Dr. Kistler has secured grants and collaborations through initiatives like the NASA Interstellar Mapping and Acceleration Probe (IMAP) and has served as a co-investigator on multiple missions. Her work emphasizes interdisciplinary approaches, combining spacecraft observations with ground-based data and numerical simulations to unravel the complexities of Earth's space environment.

Fernando CamelliView profile

Associate Professor

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +3 more

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

Mourad ZeghalView profile

Professor

• Geotechnical Engineering
• Soil Mechanics
• Seismic Response Analysis
• +3 more

Dr. Mourad Zeghal is a Professor in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute (RPI). His research focuses on computational geomechanics, seismic response monitoring, and geotechnical system identification. He leads projects addressing liquefaction mitigation, multiscale modeling of geosystems, and development of advanced computational tools for geotechnical analysis. Dr. Zeghal collaborates with RPI's Center for Network for Earthquake Engineering Simulation (CEES), Scientific Computation Research Center (SCOREC), and Inverse Problems Center (IPRPI). His work emphasizes reducing risks from natural hazards through improved design tools and model validation. Key projects include the Liquefaction Experiments and Analysis Projects (LEAP), which use centrifuge testing and machine learning to analyze soil behavior under seismic loads. Dr. Zeghal's research integrates experimental data with numerical simulations to enhance understanding of soil-structure interaction and lateral spreading during earthquakes. Research Interests: Soil liquefaction, multiscale modeling, inverse problem methods, computational geomechanics Key Collaborations: CEES, SCOREC, IPRPI, and global research networks Focus Areas: Centrifuge testing, model validation, seismic hazard mitigation His recent publications (2023–2025) highlight advancements in quantifying uncertainty in soil response, analyzing LEAP centrifuge experiments, and developing machine learning approaches for model calibration. Dr. Zeghal actively engages with industry and government labs to translate research into practical engineering solutions.

Chong LiuView profile

Assistant Professor

• Machine Learning
• Bayesian Optimization
• Bandit Algorithms
• +3 more

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR. PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang Postdoc: University of Chicago Data Science Institute (2023) Research Interests : Broad: Machine Learning, Optimization, AI for Science Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery Applications: Binding affinity prediction, Drug screening, Policy optimization Recent Article Trends : His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems. Scientific Awards : 2025: SUNY IITG/OER Impact Grant Professional Activities : Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech. Teaching : Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.

David MouldView profile

Professor

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +4 more

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

Bret WindomView profile

Associate Professor

• Alternative Fuels
• Combustion Dynamics
• Laser Diagnostics
• +3 more

Bret Windom is Associate Professor of Mechanical Engineering at Colorado State University's Walter Scott, Jr. College of Engineering, specializing in combustion science and alternative fuels. He directs research on physiochemical fuel characterization, laser diagnostics, and energy conversion systems. Research investigates combustion dynamics of sustainable fuels, propulsion systems, and emission reduction strategies. Recent projects include oxymethylene ethers as low-sooting biofuels, hydrogen production systems, and hybrid SOFC-engine power generation. Funded by DOE, ONR, and Caterpillar, his work advances low-carbon energy technologies. Recipient of the SAE Teetor Educator Award (2018) and multiple research fellowships. Leads the Chemical Energy Conversion Laboratory, mentoring graduate students in combustion research. Current industrial collaborations focus on marine engine decarbonization and lubricant ignition behavior. Recent Publications demonstrate strong focus on sustainable fuels (hydrogen, LPG, OMEs), advanced diagnostics (laser spectroscopy, NMR), and energy system integration (SOFC hybrids, scramjets). Article keywords frequently include combustion optimization, emissions reduction, and alternative fuel characterization.

Roman SamulyakView profile

Professor

• Computational Fluid Dynamics
• Magnetohydrodynamics
• Plasma Physics
• +2 more

Roman Samulyak is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He holds a Ph.D. from NJIT in Applied and Computational Mathematics with specializations in Hydro- and Electrodynamics. His research develops advanced numerical algorithms for modeling complex physical systems in high-energy physics and fusion energy. Research spans computational methods for magnetohydrodynamics, plasma physics, nuclear fusion/fission systems, and particle accelerator design. Current applications include disruption mitigation in tokamaks and laser-driven particle acceleration. Recent publications demonstrate strong focus on plasma-based accelerators and fusion reactor modeling, particularly pellet ablation dynamics, laser wakefield acceleration, and MHD simulations of tokamak plasmas. Research utilizes high-performance computing resources for large-scale simulations. Office location is Math Tower 1-108 at Stony Brook University.

Roland N. HorneView profile

Professor

• Geothermal reservoir engineering
• Energy production optimization
• Well test interpretation
• +8 more

Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University and Senior Fellow at the Precourt Institute for Energy. He holds positions in the Department of Energy Science & Engineering and is an Affiliate at the Stanford Woods Institute for the Environment. With degrees from the University of Auckland (BE, PhD, DSc), Horne has established himself as a leading expert in geothermal reservoir engineering and energy production optimization. His research focuses on inverse problems in reservoir modeling, including tracer analysis of fractures, computer-aided well test analysis, production schedule optimization, and automated history matching. Horne has made significant contributions to understanding geothermal reservoir engineering and multiphase flow of boiling fluids through porous materials and fractures. The analysis of his recent publications (2023-2025) reveals a strong emphasis on enhanced geothermal systems (EGS), with particular focus on flexible operations, economic modeling, and advanced characterization techniques. His work increasingly incorporates machine learning approaches for reservoir analysis and has expanded into microbial tracing methods for interwell connectivity assessment. There's also significant attention to US geothermal resource potential and integration into the broader energy transition. Honorary Member of the Society of Petroleum Engineers Member of the US National Academy of Engineering Multiple SPE Distinguished Lecturer appointments (1998, 2009, 2020) John Franklin Carl Award recipient Five Best Paper awards from Geothermal Resources Council Patricius Medal from German Geothermal Society Core Values Award from Women in Geothermal (2023) Horne has supervised 60 PhD and 135 MS students throughout his career. His current teaching includes undergraduate and graduate courses in Fundamentals of Energy Processes, Geothermal Reservoir Engineering, Mass and Energy Transport in Porous Media, and Well Test Analysis. He previously served as President of the International Geothermal Association (2010-2013) and Technical Program Chair for multiple World Geothermal Congress events. Horne maintains active research collaborations worldwide, including with the University of Tokyo (where he was a Fellow of the School of Engineering in 2016) and China University of Petroleum. His current research group focuses on advancing EGS technologies and developing more accurate reservoir characterization methods for geothermal applications.