Find Academic Supervisors

• Solidification Modeling
• Transport Phenomena
• Computational Mechanics
• +5 more

Dr. Sergio Felicelli is a Professor and Chair in the Department of Mechanical Engineering at the University of Akron's College of Engineering and Polymer Science. He holds a Ph.D. in Mechanical Engineering from the University of Arizona (1991) and B.S./M.S. in Nuclear Engineering from Instituto Balseiro (1985). His expertise spans Solidification Modeling, Transport Phenomena, and Computational Mechanics. Dr. Felicelli has authored pioneering works on computer modeling of freckle segregation during solidification and has directed projects on casting, additive manufacturing, and parallel simulations of microstructures. His research focuses on advancing computational methods for material processing, including dendritic solidification under microgravity conditions, multiphase flow dynamics, and large-scale parallel simulations. He has secured $15M in grants through 23 funded projects and is an ASME Fellow. His work integrates experimental validation with numerical modeling, particularly using lattice Boltzmann and phase field methods. Dr. Felicelli has led international collaborations on casting defects and serves in academic leadership roles. Education: Ph.D., Mechanical Engineering, University of Arizona, 1991 B.S./M.S., Nuclear Engineering, Instituto Balseiro, 1985 Dr. Felicelli's research trends emphasize computational modeling of materials under extreme conditions, with recent articles addressing microgravity solidification effects, multiphase interactions, and high-performance computing for microstructure prediction. His work bridges fundamental science and industrial applications in additive manufacturing and energy storage systems. Awards: ASME Fellow (prestigious honor in mechanical engineering) Grants & Funding: Over $15M secured through 23 grants, focusing on solidification science and advanced manufacturing technologies. He advises on courses such as Heat Transfer and Numerical Methods, and his lab explores cutting-edge techniques in computational materials science. Collaborations include NASA and international institutions, with a focus on solving complex industrial and aerospace material challenges.

• Interventional Cardiology
• Intravascular Ultrasound
• Optical Coherence Tomography
• +3 more

Karen Witberg is an active Researcher in the Department of Cardiology at Erasmus University Medical Center, specializing in advanced intravascular imaging techniques within the Erasmus School of Medicine. Her clinical research focuses on optimizing percutaneous coronary interventions through precise imaging analysis. Her primary research interests span interventional cardiology , with deep expertise in intravascular ultrasound (IVUS) and optical coherence tomography (OCT) applications. Key investigation areas include: Coronary plaque characterization and thrombus analysis Sex-specific differences in atherosclerotic disease Stent optimization in calcified lesions Physiological gradient validation post-PCI Current work emphasizes improving outcomes in ST-elevation myocardial infarction and familial hypercholesterolemia through imaging-guided interventions. Analysis of her recent publications reveals a strong trend toward quantitative intravascular imaging for procedural guidance, with particular focus on sex-based disparities in plaque morphology and the hemodynamic impact of stent underexpansion. Her collaborative work consistently appears in high-impact cardiology journals like JACC: Cardiovascular Interventions and Catheterization and Cardiovascular Interventions .

• Electronic design automation
• Embedded systems
• Energy efficient electronic systems
• +16 more

Daniele Jahier Pagliari is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is also a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center. He is actively involved in teaching and research, focusing on embedded systems, electronic design automation, and machine learning for edge computing. He teaches courses such as Optimized Execution of Neural Networks at the Edge, Machine Learning for IoT, and Hardware/Software Codesign of Flexible Computing Systems for Edge AI across various engineering programs including Computer Science and Systems Engineering, Data Science, and Automotive Engineering. His research interests span electronic design automation, embedded systems, energy-efficient computing, low-power design, and machine learning. He is particularly engaged in applying machine learning techniques to improve the design and performance of digital and analog circuits, with a focus on edge AI applications. His work aligns with key scientific areas including computer architecture, cyber-physical systems, and scientific computing. The recent publications highlight a strong trend in optimizing deep learning models for resource-constrained environments, accelerating neural network inference on ultra-low-power devices, and integrating physics-based models with AI for battery state estimation. There is also significant focus on using machine learning to enhance electronic design automation, particularly for analog and mixed-signal circuits, reflecting a convergence of AI and hardware design. He leads and participates in several high-impact research projects, including EU-funded initiatives like HAL4SDV, ISOLDE, TRISTAN, and AMBEATion, as well as commercial projects such as MASAI and software platform development for production support. He serves as the Scientific Responsible or Director in multiple projects, demonstrating leadership in both academic and industrial research contexts. He supervises multiple PhD students in the Computer and Systems Engineering program, including Luca Benfenati, Mohamed Amine Hamdi, Beatrice Alessandra Motetti, Giovanni Pollo, and Matteo Risso, whose research topics include latency-optimized inference, compiler optimization for edge devices, and hardware-aware deep learning design. He is also involved in patent development, notably for an instrumentation method to dynamically modify circuit precision. He is a member of the College of Computer, Film and Mechatronics Engineering and contributes to various degree programs. His work supports UN Sustainable Development Goals related to good health, affordable and clean energy, industry innovation, and sustainable cities.

• Forest Ecology
• Plant Physiology
• Tree Hydraulics
• +4 more

Assoc. Prof. Sabine Rosner is a faculty member at the Institute of Botany , part of the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Her research bridges forest ecology , plant physiology , and wood hydraulics , focusing on drought stress impacts on tree health and xylem vulnerability. Education : Diploma (1994) in Agricultural Sciences, Vienna Doctoral Degree (1998) in Natural Sciences Venia Docendi (2013) in Botany Her work explores hydraulic conductivity modeling , embolism resistance gradients , and acoustic emission testing for non-invasive drought stress monitoring. Recent projects like Smart Bark and TOPWOOD highlight her focus on tree health under climate extremes. Her publications span journals such as Frontiers in Plant Science and New Phytologist , with keywords like conifer wood anatomy , water transport , and forest pest interactions . Notable scientific awards include the BOKU Teaching Award (2021), Elise Richter Stipendium (2009), and Hertha Firnberg Stipendium (2006). She has supervised 11 theses across master’s and doctoral levels, mentoring students in topics like plant stress reactions and wood variation . Her community services include editorial roles in journals like Frontiers in Plant Science and membership in societies such as the International Academy of Wood Science (IAWS).

Claudio Alanis RuizView profile

Researcher

• Computational Fluid Dynamics
• Air Curtains
• Indoor Environment
• +3 more

Claudio Alanis Ruiz is a Researcher at the Building Physics department within Eindhoven University of Technology. His academic profile includes an MSc in Sustainable Energy Technology (2016) and an EngD in progress. He actively contributes to environmental engineering and building simulation research. Education: MSc in Sustainable Energy Technology, Eindhoven University of Technology (2014–2016) Research Focus: Computational Fluid Dynamics (CFD) for indoor airflow Air curtain efficiency in building climate control UV germicidal irradiation systems Wind energy integration in high-rise structures Projects: Lead project member for CLAIRE LSHM22032 - Clean Air For Everyone (2022–2026) Scientific Output: 15+ peer-reviewed publications (2016–2025) 40+ Scopus citations

Sam OttoView profile

Assistant Professor

• Scientific Machine Learning
• Dynamical Systems
• Model Reduction
• +7 more

Sam Otto is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, joining the faculty in July 2024. His research lies at the intersection of machine learning and continuum mechanics, with a focus on developing rigorous, data-driven methods for modeling, forecasting, and controlling high-dimensional nonlinear systems such as fluid flows. He leads the Otto Lab, which advances scientific machine learning through principled algorithms and theoretical understanding. Research Interests: Dr. Otto’s work centers on scientific machine learning, particularly in developing algorithms that integrate physical knowledge—such as symmetry, scale hierarchy, and smoothness—into data-driven models. His research addresses fundamental questions about what can be learned from limited data and how to ensure reliability in engineering applications. Key areas include model reduction, operator learning, sensor placement, and symmetry enforcement in neural networks. He applies these methods to fluid dynamics and other continuum mechanics problems. Publication Trends: His recent publications (2019–2024) show a strong trajectory in developing theory and algorithms for learning in nonlinear dynamical systems. There is a clear emphasis on incorporating physical structure into machine learning models, with recurring themes of symmetry, dimensionality reduction, and operator-based modeling. The work spans theoretical advances and practical computational methods, often leveraging Koopman theory, autoencoders, and covariance-based reductions. Education: Ph.D. in Mechanical and Aerospace Engineering, Princeton University, 2022 B.S. in Aeronautics and Astronautics, Purdue University, 2016 Scientific Awards: Advising and Grants: While no current students or grants are listed, Dr. Otto is expected to advise graduate students through the Mechanical and Aerospace Engineering graduate program at Cornell. As a new faculty member, he is likely pursuing funding for his research in scientific machine learning and dynamical systems. His lab is actively publishing in top journals and preprint servers, indicating ongoing research support, possibly through postdoctoral fellowships or early-career grants. Labs and Teams: Dr. Otto leads the Otto Lab at Cornell, which focuses on machine learning for high-dimensional dynamics. The lab develops computational tools that combine data and physical principles to model complex systems. It is part of the broader research ecosystem in the Sibley School, collaborating with experts in fluid mechanics, control theory, and applied mathematics.

Etienne MeminView profile

Professor

• Fluid Dynamics
• Geophysics
• Applied Mathematics
• +3 more

Etienne Memin is a Research Director (equivalent to Full Professor) at Inria and a Visiting Professor at Imperial College London. He leads the Odyssey research group, affiliated with Inria, University of Rennes, and other institutions. His work focuses on stochastic modeling of geophysical flows, fluid dynamics, and data assimilation. Memin is the Principal Investigator (PI) of the ERC-funded STUOD project, which explores stochastic transport in upper ocean dynamics. His research bridges disciplines such as applied mathematics, computer vision, and geophysics. Education: HDR (Habilitation à Diriger des Recherches) from University Rennes I in 2003. Previous roles include Research Director at Inria since 2008 and collaborations with institutions like IFREMER, Imperial College, and Zhejiang University. Research Interests: Stochastic modeling of fluid dynamics, data assimilation frameworks, and uncertainty quantification. His work includes developing ensemble techniques for data assimilation and stochastic representations of turbulence. Recent projects involve stochastic shell models and wave solutions in shallow water dynamics. Collaborations: Active partnerships with Imperial College, IFREMER, MétéoFrance, and others on topics like stochastic parameterization and error modeling in weather prediction. Key collaborations include the STUOD project and Royal Society-funded studies on stochastic large-eddy simulations. Students and Grants: Supervised over 20 PhD students, including current advisees Francesco Tucciarone and Antoine Moneyron. His grants include the ERC STUOD and multiple ANR projects. He leads the Odyssey group, fostering interdisciplinary research in fluid dynamics and geophysics. Labs/Teams: Odyssey group at Inria, collaborating with IMT Atlantique, IRMAR, and international partners. His work emphasizes computational fluid dynamics and real-world applications like flood modeling and wind engineering.

Fei HeView profile

Research Fellow

• Fluid Dynamics
• Hydrodynamics
• Environmental Engineering
• +3 more

Fei He is a Research Fellow at the School of Engineering, The University of Western Australia, with a casual teaching role. He holds a Doctor of Philosophy (PhD). His research focuses on fluid dynamics, hydrodynamics, and environmental engineering, particularly in flow through porous media, cylinder arrays, and scour modeling. He has contributed to studies on obstacle arrangement effects, drag coefficient analysis, and vortex-induced vibrations. Research interests include fluid dynamics in coastal, civil, and mechanical engineering contexts. His work addresses challenges in flow control, sediment transport, and wave attenuation by vegetation. Key contributions include semi-empirical models for scour analysis and experimental studies on coral grain settling. Recent publications (2023–2025) explore hydrodynamic forces, three-dimensional flow past cylinders, and porous obstruction dynamics. Collaborations span geophysical, environmental, and mechanical engineering domains.

Nicolas BoulleView profile

Assistant Professor

• Numerical Analysis
• Numerical and Computational Mathematics
• Knowledge Representation
• +4 more

Nicolas Boulle is an Assistant Professor in Applied Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on the intersection of numerical analysis and deep learning, particularly in discovering mathematical models (e.g., partial differential equations) from data and developing theoretically grounded numerical techniques. Education: DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022) Research interests include numerical analysis, operator learning, machine learning, and their applications in solving complex mathematical problems. His work emphasizes data-driven methods for Green’s functions and the development of rational neural networks for enhanced accuracy in deep learning. Recent publications explore topics like dynamic mode decomposition, Koopman operators, and large language models' behavior. He advises PhD students and visiting researchers on projects involving operator learning and neural networks. Boulle contributes to open-source projects such as GreenLearning (for PDE Green’s functions) and RationalNets (rational activation functions in neural networks), hosted on GitHub.

• Fluid Mechanics
• Environmental Fluid Dynamics
• Experimental Fluid Techniques
• +4 more

Julie Crockett is an Associate Professor in the Department of Mechanical Engineering at Brigham Young University's College of Engineering. Her research focuses on experimental and theoretical fluid mechanics, with an emphasis on environmental fluid dynamics, superhydrophobic surfaces, and heat transfer applications. She teaches graduate and undergraduate courses in mechanical engineering and maintains regular office hours for student engagement. Research Interests : Fluid Mechanics Environmental Fluid Dynamics Superhydrophobic Surface Engineering Droplet and Jet Impingement Dynamics Internal Wave Generation in Stratified Flows Thermal Transport and Atomization Scientific Awards : Nominee, Utah Engineering Educator of the Year (2018), Society of Women Engineers Publications Trends : Dr. Crockett's recent work explores fluid behavior on superhydrophobic surfaces, including droplet dynamics, condensation mechanisms, and heat transfer optimization. Her studies integrate experimental methods with theoretical analysis to address applications in thermal management and environmental fluid systems.

• Virtual Reality
• Computer Vision
• Human-Computer Interaction
• +4 more

Joe K. Kearney is a Professor in the Department of Computer Science at the University of Iowa and the University of Minnesota. His career spans over three decades, focusing on virtual reality, computer vision, and human-computer interaction research. Key affiliations: University of Iowa (Iowa City, IA, USA), University of Minnesota (Minneapolis, MN, USA) Research domains: Virtual environments, perception-action coupling, motion capture systems, pedestrian behavior simulation Research trends show sustained contributions to immersive visualization , autonomous navigation , and human factors in virtual reality . His 2014-2025 work explores AR-based pedestrian safety systems, while earlier studies (1986-2006) established foundational frameworks for virtual environment simulation and optical flow analysis. Long-term collaborations with Jodie M. Plumert (1986-2021), James F. Cremer (1986-2009), and Pooya Rahimian (2015-2017) demonstrate consistent team research in VR cognition and autonomous systems.

Gabriel PasquetView profile

Research Fellow

• Carbon Sequestration
• Enhanced Oil Recovery
• Seismicity
• +3 more

Gabriel Pasquet is a Postdoctoral Fellow at the Bureau of Economic Geology, University of Texas at Austin. His work focuses on carbon sequestration, enhanced oil recovery, and seismicity, with a strong emphasis on Permian Basin studies and the energy-water nexus. Current affiliation: Bureau of Economic Geology, University of Texas at Austin Position: Research Fellow Pasquet's research spans multiple disciplines, including: Carbon storage security and CO2 injection methodologies Seismic risk assessment from fluid injection activities Hydrological modeling integrating satellite data and machine learning Basin tectonics and stress field characterization Permafrost dynamics in Arctic environments Coastal system modifications and sediment transport Recent publications highlight his expertise in: Geological CO2 sequestration optimization Seismotectonic analysis of Midland Basin Formate-based oil recovery techniques Environmental impacts of energy development His work frequently intersects with: Energy economics Geophysical monitoring Reservoir characterization Machine learning applications in hydrology

Xianming DaiView profile

Associate Professor

• Heat transfer
• Thermal management
• Water sustainability
• +7 more

Xianming Dai is an Associate Professor in the Department of Mechanical Engineering at the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. He also holds an affiliated faculty position in Bioengineering. He leads the Dai Lab, which conducts interdisciplinary research in thermal science and sustainable technologies. Post-Doc: Mechanical Engineering, The Pennsylvania State University, 2016 PhD: Mechanical Engineering, University of South Carolina, 2013 M.S.: Thermal Engineering, Huazhong University of Science and Technology, 2009 B.S.: Thermal Energy and Power Engineering, Chongqing University, 2007 Dr. Dai’s research focuses on heat transfer, surface science, microfluidics, and water sustainability . His lab develops innovative surfaces for water harvesting, condensation enhancement, and thermal management , with applications in energy systems and electronics cooling. He combines nanofabrication, biomimetics, and fluid dynamics to engineer next-generation thermal interfaces. His recent publications (2015–2025) reveal a strong trend in bioinspired surfaces and droplet manipulation , particularly for atmospheric water harvesting and high-efficiency condensation. Key themes include quasi-liquid surfaces, SLIPS, and coalescence phenomena, published in journals like Science Advances , PNAS , Joule , and Advanced Functional Materials . Dr. Dai has received several prestigious awards, including: DARPA Young Faculty Award (2023) National Science Foundation CAREER Award (2021) Army Research Office Young Investigator Award (2019) UT Dallas Jonsson School Assistant Professor Research Award (2022) Outstanding Early Career Award (2021) JALA Top Ten Breakthrough Award (2016) Fellow, Eugene McDermott Distinguished Professor (2025) He actively advises PhD students and leads significant research initiatives, including a $3M Department of Energy award for energy sustainability and a $500K DARPA Director’s Fellowship. He was appointed Associate Editor of the journal Droplet in 2025. His lab, the Dai Lab, fosters interdisciplinary collaboration and recruits students from diverse backgrounds. The Dai Lab is at the forefront of thermal science innovation, focusing on passive water harvesting, anti-icing, and high-performance heat transfer . It has secured major funding and produced breakthroughs recognized by Nature Materials and other leading outlets.

Ting-Kam Leonard WongView profile

Associate Professor

• Mathematical Finance
• Probability
• Optimal Transport
• +2 more

Ting-Kam Leonard Wong is an Associate Professor jointly appointed at the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences at the University of Toronto and University of Toronto Scarborough , respectively. He also holds a non-budgetary cross-appointment in the Department of Mathematics since May 2025. Education: PhD in Mathematics, University of Washington (2016) MPhil in Mathematics, The Chinese University of Hong Kong (2011) Research Interests: His research lies at the intersection of mathematical finance, probability theory, optimal transport, and information geometry . He explores deep theoretical questions in stochastic processes and their applications in financial modeling, market analysis, and data science. His work often involves developing geometric frameworks to understand complex probabilistic systems, including the study of Wasserstein distances, Bregman divergences, and their roles in optimization and inference. Research Trends: Across his recent publications, a clear trend emerges toward integrating geometric methods with stochastic modeling and financial applications . He has contributed to the development of new divergence measures, optimization algorithms, and portfolio frameworks. His work often bridges theoretical probability with practical tools in finance and machine learning, such as portfolio optimization, PCA under transport metrics, and scalable inference methods. Awards & Funding: NSERC Discovery Grants (2019, 2025) Connaught New Researcher Award (2020) Data Science Institute Seed Funding for Methodologists (2022) Best Paper Award, Geometric Science of Information Conference (2019) Advising & Grants: He currently supervises several PhD students including Elijah French , Madhu Gunasingam , and Amanjit Kainth . He previously co-supervised Steven Campbell , who successfully defended in 2023. His research is supported by multiple federal and institutional grants, reflecting his active role in advancing methodological research in statistics and finance. Editorial & Service Roles: He serves as an Associate Editor for the journal Information Geometry (2022–present), actively contributing to the dissemination of research in geometric methods for data science and inference.

Kristin ScharnweberView profile

Researcher

• Aquatic Ecosystem Ecology
• Food Web Ecology
• Evolutionary Ecology
• +7 more

Dr. Kristin Scharnweber is a researcher at the Institute of Biochemistry and Biology , University of Potsdam , Germany, where she currently serves as the Scientific Coordinator of the DFG-funded Research Training Group BioMove , focusing on biodiversity and movement ecology in agricultural landscapes. She has held postdoctoral and research positions at Uppsala University and the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), and earned her PhD within the TERRALAC project. Her research spans aquatic ecosystem ecology , with a focus on food web dynamics , fatty acid ecology , evolutionary divergence , and carbon cycling . She investigates how structural complexity, productivity, and environmental change affect trophic interactions and ecosystem function in lakes and streams. Her work integrates stable isotope analysis, lipid profiling, and experimental ecology. Dr. Scharnweber's recent publications highlight trends in trophic transfer efficiency , intra-specific variation in metabolism , resource partitioning , and the role of fatty acids in ecological and evolutionary processes . Her research contributes to understanding how aquatic systems respond to environmental stressors such as browning and eutrophication. Scientific Awards: None explicitly mentioned in the provided text. She has been involved in advising and grant-funded research, notably coordinating the DFG RTG BioMove, which supports interdisciplinary training in biodiversity and movement ecology. Her work often involves collaboration with large research networks and cross-institutional teams. Dr. Scharnweber is affiliated with the Plant Ecology and Nature Conservation group at the University of Potsdam, which conducts research on community ecology, movement ecology, and conservation in grasslands, drylands, and aquatic systems.