Find Academic Supervisors

• Environmental Hydraulics
• Sediment Transport
• Turbulence Modelling
• +6 more

Dr. Jaan Pu is an Associate Professor at the University of Bradford, UK, affiliated with the School of BEACI within the Faculty of Engineering & Digital Technologies. He holds visiting scientist roles at Tsinghua University and Nanyang Technological University. His research focuses on numerical and experimental approaches to water engineering challenges, including sediment transport, turbulence modeling, and flood dynamics. Dr. Pu serves as Associate Editor for Frontiers in Environmental Science and Frontiers in Built Environment , and editorial roles in other journals. His work integrates computational tools (e.g., Delft3D, MATLAB) with experimental techniques (ADV, PIV) to study flows in rivers, coastal areas, and urban environments. Research interests include turbulence structures around bridge piers, scour reduction mechanisms, and environmental hydraulics. Teaching areas cover Water Engineering, Fluid Dynamics, and Mechanics. His publications span 73 works addressing topics from coral reef protection to sedimentation in rainwater systems. Professional activities include editorial board memberships, conference organizing, and peer review for high-impact journals.

Raj ManglikView profile

Professor

• Heat Transfer
• Thermal Engineering
• Fluid Dynamics
• +7 more

Dr. Raj M. Manglik is a Professor of Mechanical & Materials Engineering and Director of the Thermal-Fluids & Thermal Processing Laboratory at the University of Cincinnati. His research focuses on interfacial phenomena, molecular dynamics, heat transfer enhancement, and thermal systems design. He holds a Ph.D. from Rensselaer Polytechnic Institute, an M.S. from Iowa State University, and a B.Tech. from Indian Institute of Technology Madras. Dr. Manglik has secured over $9.5M in grants, including recent projects on advanced dry-cooling systems, 3D printed ceramic heat exchangers, and thermal energy storage. His work spans experimental and computational studies of heat exchangers, non-Newtonian fluids, and phase-change materials. He has authored numerous peer-reviewed publications and co-edited books such as Principles of Heat Transfer . Awards include the ASME Heat Transfer Memorial Award (2016), ASME James Harry Potter Gold Medal (2018), and multiple university teaching and research accolades. His lab explores topics like boiling heat transfer, drop impact dynamics, and energy-water nexus solutions in power plants.

Gaofeng JiaView profile

Associate Professor

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +4 more

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

Andrew IresonView profile

Professor

• Subsurface Hydrology
• Climate Change and Water Security
• Land-Water Management
• +3 more

Andrew Ireson is a Professor at the School of Environment and Sustainability, University of Saskatchewan, and a Member of the Global Institute for Water Security. His research focuses on subsurface hydrology, climate change impacts on water resources, land-water management, and sustainable natural resource development. He has contributed significantly to understanding soil moisture dynamics, infiltration processes in frozen soils, and groundwater-surface water interactions in boreal and prairie ecosystems. His recent work emphasizes advanced numerical modeling techniques for hydrological processes, including Richards’ Equation solutions and soil freezing dynamics. He has also developed frameworks for integrating water quality simulations with existing hydrological models (e.g., OpenWQ). His publications highlight themes like climate change adaptation, seasonal hydrology, and the role of ephemeral ponds in groundwater recharge. No scientific awards or grants are listed here, though his extensive publication record underscores his scholarly contributions. His advising roles are not detailed in the provided texts.

Di ZhuView profile

Assistant Professor

• Geographic Information Science
• Geospatial Artificial Intelligence (GeoAI)
• Spatial Networks
• +6 more

Di Zhu is an Assistant Professor of Geographic Information Science at the University of Minnesota's Department of Geography, Environment and Society. He directs the Geospatial Data Intelligence (GeoDI) Lab, focusing on GeoAI and social sensing to analyze human-environment interactions in urban systems, public health, and socioeconomic dynamics. His educational background includes a PhD in Cartology and GIScience from Peking University, complemented by a BSc in GIS and a BA in Economics from the same institution. Key research interests include spatial regression models, human mobility patterns, and GeoAI applications. He has collaborated on projects funded by NIH, NSF, and other agencies, exploring topics like spatiotemporal data imputation, urban flow analysis, and pandemic spatial dynamics. His work bridges traditional GIScience with modern machine learning techniques, emphasizing actionable insights from big geospatial data. Teaching focuses on advanced GIS, numerical spatial analysis, and urban sensing. He actively mentors students through the University of Minnesota's Master of GIS program and serves on academic boards including CPGIS. Current projects include analyzing Twin Cities mobility networks and developing intelligent spatial prediction frameworks.

Liangping LiView profile

Associate Professor

• Hydrogeology
• Groundwater Modeling
• Data Assimilation
• +5 more

Dr. Liangping Li is an Associate Professor in the Department of Geology and Geological Engineering at South Dakota School of Mines & Technology. He holds a Ph.D. from Technical University of Valencia and an M.S. from China University of Geoscience, with expertise in hydrogeology, groundwater modeling, and geothermal energy systems. Education: M.S., China University of Geoscience; Ph.D., Technical University of Valencia His research focuses on integrating machine learning with groundwater modeling, data assimilation, geostatistics, and optimization of geothermal energy systems. He has pioneered methods combining generative adversarial networks (GANs) and ensemble smoother techniques for inverse modeling in complex aquifers. Recent publications highlight his work on extremal optimization for well placement, progressive growing GANs for facies modeling, and stochastic inversion of fracture networks. His research trends emphasize computational innovation in subsurface flow simulation and sustainable groundwater management. Scientific Awards: NSF RII Track-4 Grant, NSF REU Site Grant, BLM Environmental Monitoring Grant, and appointments as Associate Editor for Advances in Water Resources and Mathematical Geosciences . Dr. Li teaches courses in groundwater engineering, statistical methods, and environmental field camp, while mentoring graduate and undergraduate researchers in subsurface energy and water resource projects.

Christopher Thomas RyanView profile

Associate Professor

• Optimization
• Game Theory
• Theoretical Economics
• +7 more

Christopher Thomas Ryan is an Associate Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds a PhD and BA from UBC in Management Science and Mathematics, respectively. His research spans optimization theory, game theory, theoretical economics, video game design, and pedagogy for business education. He has held positions at the University of Chicago Booth School of Business and is affiliated with the Colibri Learning Foundation. Education: BA (Mathematics) and PhD (Management Science) from UBC; BA (Sociology and Economics) from University of Guelph. He has authored numerous textbooks and case studies, including works on research careers, operations management, and teaching methodologies. His work integrates human-centric approaches to operations and education systems. Research Interests include small business operations, education operations, theoretical economics, and the design of video games. He has published in top journals like Management Science, Econometrica, and Mathematical Programming. His grants include SSHRC and NSERC awards. Ryan advises students across multiple institutions and has mentored prominent faculty in marketing, operations, and MIS fields. Teaching focuses on core operations management for MBA programs and pedagogical innovation. He has developed cases on AI in education, early childhood development, and small business sustainability. Ryan’s work bridges academic research with practical applications in business and education sectors.

Fikile R. BrushettView profile

Professor

• Electrochemical Energy Storage
• Redox Flow Batteries
• Electrochemistry
• +3 more

Fikile R. Brushett is a Professor and holds the Chevron Chair of Chemical Engineering at the Massachusetts Institute of Technology (MIT), within the Department of Chemical Engineering under the School of Engineering. His research focuses on electrochemical energy storage systems, particularly redox flow batteries, catalyst synthesis, and environmental applications like CO₂ capture. Brushett has received numerous accolades, including the Allan P. Colburn Award and Charles W. Tobias Young Investigator Award for his impactful publications. His work integrates experimental and computational methods to advance energy technologies, emphasizing sustainable materials and system optimization. He leads projects addressing global energy challenges through innovations in battery design, electrolyte development, and process modeling. Education: Ph.D. in Chemical Engineering, University of Illinois at Urbana-Champaign (2010) M.S.E. in Chemical Engineering, University of Illinois at Urbana-Champaign (2009) B.S.E. in Chemical Engineering, University of Pennsylvania (2006) Research: Brushett’s lab explores electrochemical energy conversion/storage, microfluidics, and interfacial phenomena. Key areas include redox flow battery optimization, scalable carbon materials for energy storage, and CO₂ capture via electrochemical methods. His team develops novel diagnostic tools (e.g., microelectrode sensors) and models for system efficiency. Awards: Over two dozen honors, including named chairs and fellowships from organizations like ACS and GEM. Notable recognitions include the 2024 Chevron Chair and 2022 AIChE Colburn Award. Grants & Labs: Active in MIT’s Electrochemical Energy Lab, focusing on grid-scale storage solutions. Collaborates on projects funded by DOE, industry partners (e.g., Chevron), and foundations. Leads initiatives in material sustainability and battery lifecycle analysis. Publications: Over 140 peer-reviewed articles (2022–2025) emphasize interdisciplinary innovations in energy storage, electrolyte design, and environmental electrochemistry. Recent work highlights advancements in non-aqueous systems, CO₂ capture, and Bayesian-optimized material synthesis.

Peter L. OlsonView profile

Research Professor

• Geophysics
• Core Dynamics
• Geomagnetism
• +2 more

Dr. Peter L. Olson is a Research Professor at Johns Hopkins University's Department of Earth and Planetary Sciences. His research examines Earth's deep interior dynamics including core-mantle interactions, geomagnetic field generation, and planetary evolution processes. Research Focus: Combines theoretical models, numerical simulations, and laboratory experiments to study core dynamics, geodynamo processes, and mantle convection. Current investigations include the slow carbon cycle and polar ice shelf dynamics through interdisciplinary collaborations. Publications: Recent work explores geomagnetic reversal mechanisms, core-mantle boundary interactions, and planetary dynamo diversity using advanced computational models and fluid dynamics experiments. Education: Ph.D. from University of California, Berkeley

Alan KaptanogluView profile

Professor

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +3 more

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Hao YuanView profile

Lecturer

• Chemical Kinetics
• Lithium-ion Battery
• Combustion
• +3 more

Hao Yuan is a Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Institute for Advanced Automotive Propulsion Systems (IAAPS). His research focuses on Chemical Kinetics, Lithium-ion Batteries, Combustion, and Numerical Modelling, with a particular emphasis on improving engine efficiency and low-carbon technologies. He leads the JLR Prosperity Partnership project, a £5M EPSRC-funded initiative exploring electrified propulsion systems. Education: PhD in Mechanical Engineering (2013–2018), University of Melbourne MSc in Electrical Engineering (2009–2012), University of the Chinese Academy of Sciences BSc in Mechanical Engineering (2005–2009), Beijing Forestry University Research Interests: Dr. Yuan's work bridges computational modelling and experimental validation, addressing challenges in combustion optimization, thermal management, and sustainable energy systems. His studies on hydrogen internal combustion engines and fuel cell hybrid vehicles aim to advance low-emission propulsion technologies. Grants & Projects: JLR Prosperity Partnership (2021–2026) : Developing low-emission powertrains for future automotive systems. Labs/Teams: Active within IAAPS, collaborating on interdisciplinary projects to enhance automotive propulsion sustainability.

Chiara BodeiView profile

Associate Professor

• Computer Science
• Cybersecurity
• Formal Methods
• +4 more

Chiara Bodei is an Associate Professor at the Department of Computer Science, University of Pisa. She specializes in cybersecurity, formal methods, and programming languages. Her teaching responsibilities include courses such as 'Programming Fundamentals with Laboratory' and 'Security Methods and Verification' at both undergraduate and graduate levels. She has contributed to research in automotive cybersecurity, IoT security, and formal analysis of software systems. Her work emphasizes secure communication protocols, privacy policies in automotive data, and context-aware security mechanisms. She collaborates with industry and academia on projects related to software-defined vehicles and IoT security. Her research has been published in top-tier conferences and journals, focusing on practical and theoretical advancements in computer security and formal methods. Research Interests: Cybersecurity, Formal Methods, Automotive Systems Security, IoT Security, Programming Languages, Context-Aware Systems. She has co-authored numerous papers on topics such as vehicular communication security, secure automotive protocols, and privacy in IoT systems. Teaching: Bodei has taught courses like 'Fondamenti di Programmazione' (undergraduate), 'Security Methods and Verification' (graduate), and 'Language-Based Technology for Security' (master’s level) across multiple academic years. She emphasizes practical programming skills and theoretical foundations in her courses. Labs/Teams: Her work is supported by the Department of Computer Science labs at the University of Pisa, focusing on applied research in cybersecurity and formal systems.

Tobias GrafkeView profile

Associate Professor

• Rare Events
• Fluid Dynamics
• Large Deviation Theory
• +3 more

Dr. Tobias Grafke is Associate Professor of Mathematics at the University of Warwick, specializing in applied and computational mathematics. His research develops tools to analyze stochastic systems in fluid dynamics, climate science, and active matter. Current projects focus on predicting rare events like AMOC collapse and turbulence proliferation using large deviation theory and numerical methods. Recent articles (2023-2025) investigate noise-induced climate tipping points, rogue wave mechanics, and scalable algorithms for stochastic PDEs. Grants include an EPSRC New Investigator Award (2020) and NSF/EPSRC joint funding. Teaches MA3J4 (Mathematical Modelling with PDE) and MA2K4 (Numerical Methods).

• Computational Fluid Dynamics
• Vehicle Aerodynamics
• Bluff-Body Flows
• +3 more

Sinisa Krajnovic is a Professor of Computational Fluid Dynamics and Head of the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. His research focuses on vehicle aerodynamics, bluff-body flows, and time-dependent numerical simulations, particularly in ground vehicle flows (trains, cars, buses) and high-speed train dynamics. He leads studies on flow control mechanisms, bi-stable wake phenomena, and turbulence modeling using advanced CFD techniques like LES and PANS. Recent work emphasizes active flow control optimization, snow-resistance performance of bogies, and aerodynamic interactions in platoons. His 247+ publications span topics including high-speed train aerodynamics, ship airflow control, and bluff-body wake dynamics. Collaborations involve experimental validation and industrial applications in rail and marine transportation.

Ameya JagtapView profile

Assistant Professor

• Scientific Machine Learning
• Deep Learning
• Computational Physics
• +8 more

Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.