Find Academic Supervisors

Sanket DeshmukhView profile

Associate Professor

• Molecular Dynamics Simulations
• Materials Science
• Tribology
• +7 more

Sanket Deshmukh is an Associate Professor in the Department of Chemical Engineering at Virginia Tech. His research emphasizes computational materials science, combining molecular dynamics simulations, machine learning, and Bayesian statistical methods to study structure-property relationships in polymers, biomaterials, and nanomaterials. He focuses on developing coarse-grained models for predictive simulations of complex systems and advancing applications in energy storage, drug delivery, and tribology. Education: Ph.D., University College Dublin, Ireland (2009) M.Sc., University of Pune, India (2004) B.Sc., University of Pune, India (2002) Research Interests: Molecular dynamics on petascale supercomputers Nano- and meso-scale tribology Smart polymers and bio-materials Corrosion mechanisms and surface chemistry Machine learning for materials design His work bridges computational theory and experimental validation, with notable contributions to metal-organic frameworks (MOFs), thermoresponsive polymers, and graphene-based systems. Recent articles highlight advancements in AI-driven drug discovery, high-entropy alloys, and self-folding nanomaterials. Deshmukh’s research group actively explores applications in sustainable energy, biomedical engineering, and advanced materials. Lab activities include the Research Group Google Scholar , emphasizing interdisciplinary collaboration in materials innovation.

Maarten van ReeuwijkView profile

Professor

• Urban Fluid Mechanics
• Atmospheric Sciences
• Turbulent Flows
• +10 more

Maarten van Reeuwijk is a Professor of Urban Fluid Mechanics at the Department of Civil and Environmental Engineering, Faculty of Engineering at Imperial College London. He serves as Director of Research and teaches at both undergraduate and postgraduate levels. His research focuses on transport processes in urban environments, atmospheric convection, and turbulent flows, with applications to air quality, urban microclimate, and sustainable infrastructure. He leads a multidisciplinary research group involving Aeronautical, Civil, and Mechanical Engineering experts. Education: MSc in Civil and Environmental Engineering (Fluid Mechanics), Delft University of Technology (1995-2002) PhD in Applied Physics (Turbulent Thermal Convection), Delft University of Technology (2002-2007) Research Interests: Urban fluid mechanics, turbulence dynamics, green infrastructure impacts, microplastics behavior, cloud physics, and large-eddy simulation (LES). His work integrates computational modeling (e.g., uDALES software) with experimental data to address environmental challenges like urban heat islands and pollution dispersion. Awards & Recognition: None explicitly stated in the text. Advising & Grants: Supervises PhD/postdoc researchers in diverse engineering and physics backgrounds. Active in securing grants for urban sustainability projects and computational fluid dynamics research. Labs/Teams: Heads the Urban Fluid Mechanics group and collaborates with Imperial's Grantham Institute and Artificial Intelligence Network. Develops the uDALES LES model for urban flow simulations.

Jan KierfeldView profile

Professor

• Soft Matter Physics
• Biological Physics
• Statistical Physics
• +12 more

Prof. Dr. Jan Kierfeld is a faculty member in the Department of Physics at Technical University of Dortmund, where he leads a research group focused on soft matter theory and biological physics. His work bridges statistical physics, mechanics, and hydrodynamics of soft and biological systems, with strong interdisciplinary connections to materials science and biophysics. His research interests include polymer physics , cytoskeletal filaments (actin and microtubules), elastic capsules and shells , active matter , and the development of novel simulation techniques such as event-chain Monte Carlo. He is particularly interested in how chemical energy (e.g., ATP/GTP hydrolysis) drives mechanical forces in biological systems, and in the mechanics of semiflexible polymer networks and microswimmers. His group also pioneers the application of machine learning to problems in soft matter, such as pendant drop tensiometry and traction force microscopy. The recent publications of Prof. Kierfeld span topics in biophysics, soft matter, and computational physics, showing a strong trend toward integrating theoretical modeling with experimental collaboration, especially in microswimmer dynamics, microtubule mechanics, and interfacial phenomena. His work frequently appears in journals such as Soft Matter , Physical Review , Biophysical Journal , and Nature Communications . He has no listed scientific awards in the provided text, but his active publication record and leadership in DFG programs (e.g., SPP1726 Microswimmers) indicate significant recognition in the field. He advises students and postdoctoral researchers in theoretical and computational soft matter physics, though specific names are not listed. His research is supported by grants from German funding agencies such as the DFG. Prof. Kierfeld’s group develops and applies advanced simulation methods and collaborates with experimentalists on problems involving elastic instabilities (buckling, wrinkling), microswimmers , and chemomechanical models of cellular structures. The group maintains strong technical development in numerical algorithms and data analysis tools, including open-source software like MLFTM for traction force microscopy.

• Probability Theory
• Mathematical Finance
• Stochastic Analysis
• +4 more

Giovanni PECCATI is a Full Professor in Mathematics and Head of the Department of Mathematics (DMATH) at the University of Luxembourg’s Faculty of Science, Technology and Medicine. He leads the research group 'Revealing order in randomness' and serves as President of the Luxembourg Mathematical Society. Previously, he held positions at Sorbonne Université (1999–2008) as Assistant Professor and at Université Paris-Nanterre (2008–2010) as Full Professor before joining Luxembourg in 2010. His research focuses on probability theory, stochastic analysis, and mathematical finance, with particular expertise in Malliavin calculus, limit theorems, and random fields. Key contributions include studies on nodal sets of random waves, Poisson functionals, and Stein’s method applications. Prof. Peccati’s awards include the 2018 IMS Fellowship and the 2015 FNR Award for Outstanding Scientific Publication. His work bridges pure mathematics with applications in statistical mechanics and mathematical physics, emphasizing probabilistic structures in geometric and physical systems. He has authored over 100 publications, including the influential book *Wiener Chaos: Moments, Cumulants and Diagrams* (2011), and co-founded the *Stochastic Analysis for Poisson Point Processes* research program. His current projects explore phase transitions in stochastic systems and geometric properties of random fields.

David S. ThompsonView profile

Professor

• Computational Fluid Dynamics
• Aircraft Icing
• Unsteady Flows
• +7 more

David S. Thompson is a Professor in the Department of Aerospace Engineering at Mississippi State University, where he holds the inaugural Airbus Helicopters, Inc. Professorship. He is affiliated with the Bagley College of Engineering and has been a key figure in computational fluid dynamics (CFD) research and education. He previously served in leadership roles at the Center for Advanced Vehicular Systems (CAVS) and the Office of Research and Economic Development. Ph.D., Aerospace Engineering, Iowa State University (1987) M.S., Aerospace Engineering, Mississippi State University (1980) B.S., Aerospace Engineering, Mississippi State University (1979) Dr. Thompson's research focuses on computational fluid dynamics , particularly in aircraft icing , unsteady flows , and vortex-dominated flows . He also works on mesh generation, flow visualization, and high-performance computing applications in both aerospace and biomedical systems. His interdisciplinary work spans engineering mechanics, numerical methods, and biological flow modeling. His recent publications reflect a strong emphasis on turbulent wake analysis , flow visualization techniques , and CFD modeling of complex systems such as iced wings and lung airways. The articles demonstrate expertise in hybrid turbulence modeling, vortex detection, and adaptive mesh refinement, often applied to real-world engineering and biomedical challenges. Faculty of the Year, MSU Department of Aerospace Engineering (2015) Royal Academy of Engineering Distinguished Visiting Fellow (2014–15) Inaugural Airbus Helicopters, Inc. Professorship (2013–present) Bagley College of Engineering Hearin Faculty Excellence Award (2010) Mississippi State University StatePride Award (2010, 2011) Bagley College of Engineering Academy of Distinguished Teachers (2010) NASA Group Achievement Award for LEWICE development (2009) NASA TGIR Award for aircraft icing research (2001) Dr. Thompson has secured research funding from major agencies including the National Science Foundation , NASA , Air Force Office of Scientific Research , Army Research Office , Department of Homeland Security , and aircraft industry partners such as Airbus. He has advised numerous students and collaborators across disciplines, contributing to projects in aerospace, energy, and biomedical engineering. His work integrates simulation, visualization, and high-performance computing to solve complex fluid dynamics problems. He is associated with research facilities such as the Autonomous System Research Laboratory (ASRL) and the Center for Advanced Vehicular Systems (CAVS) , where he led the Computational Fluid Dynamics group. His collaborations extend to international institutions, including Cardiff University during his Royal Academy fellowship.

Furat Al-ObaidyView profile

Lecturer

• Machine Learning
• Intelligent Systems
• VLSI Circuits
• +7 more

Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)

Henning Tangen SøgaardView profile

Associate Professor

• Mechanical Engineering
• Agricultural Engineering
• Control Systems
• +4 more

Henning Tangen Søgaard is an Associate Professor at the Department of Mechanical and Production Engineering, part of AU Engineering at Aarhus University. He teaches mathematics, numerical methods, and mathematical statistics to BEng students. His research focuses on robotics in agriculture , dynamic modeling , and precision agriculture . Primary Affiliation: Department of Mechanical and Production Engineering, AU Engineering, Aarhus University Research Expertise: Computer vision, control systems for agricultural robotics, environmental modeling (ammonia emissions, spray drift), and wireless sensor networks. His work includes developing autonomous systems for weed control, GPS-based geo-referencing of crops, and mathematical models for fertilizer-related emissions. Publications span peer-reviewed journals and reports in agricultural engineering , robotics , and environmental science . Scientific awards are not mentioned in the provided text. He has no listed PhD students but has collaborated on multiple projects. For direct contact, his email is hts@mpe.au.dk .

Kartic SubrView profile

Senior Lecturer

• Computer Graphics
• Monte Carlo Rendering
• Physics-based Simulation
• +13 more

Kartic Subr is a Senior Lecturer (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, where he conducts research at the intersection of computer graphics, machine learning, and robotics. He is affiliated with the Institute of Perception, Action and Behaviour and leads an active research group in physics-aware simulation and AI for scientific discovery. His research interests span Computer Graphics , Monte Carlo Rendering , Physics-based Simulation , Machine Learning , Robotics , and Computational Biology . He has made significant contributions to rendering theory, light transport analysis, BRDF modeling, and simulation of fluids and fractures. Recently, his group has applied deep learning to protein design, achieving notable success in international competitions. The recent publications reflect a strong trend toward interdisciplinary research, combining computer graphics with AI and biology. Key themes include explainable AI for physics-aware interfaces (e.g., CueTip), neural rendering (NeRF), protein design with CNNs, and reward learning in robotics. His group regularly publishes in top venues such as SIGGRAPH, NeurIPS, and IEEE RA-L. Royal Society University Research Fellowship (2014–2024) Lead, GCRF Impact Accelerator (2019) Lead, EPSRC First Grant (2017) Royal Society Challenge Grant (2016) Royal Society Newton International Fellow (2010) Marie-Curie Visitor (2006) Best Paper Honorable Mention, I3D 2012 Best Paper, I3D 2011 Kartic Subr actively supervises PhD students and postdocs, including Leonardo Castorina, Zhiyuan Zhang, and Kevin Denamganaï. Former students have gone on to positions at DeepMind, University of Edinburgh, and industry. He has secured multiple grants, including from the Royal Society and EPSRC. His research group collaborates across disciplines, particularly with biological sciences on protein design. He leads projects on approximate physics for robotics, protein design via deep learning, and interactive rendering systems. The group has developed frameworks like TIMED for de novo protein design and tools for explainable physics-aware assistants.

Lukas FuchsView profile

Researcher

• Fluid Mechanics
• Numerical Mathematics
• Turbulence Modeling
• +3 more

Lukas Fuchs is a doctoral researcher at the Institute of Fluid Mechanics and Technical Acoustics (ISTA) within the Faculty V - Transportation and Mechanical Systems at the Technical University of Berlin. He focuses on the Dynamics of Turbulent Separation Bubbles using linearized modeling approaches and deep reinforcement learning techniques. Education: M.Sc. in Physical Engineering (TU Berlin), specialization in fluid mechanics and numerical mathematics Current Role: Scientific employee (PhD candidate) since March 2023 His research combines fluid dynamics with machine learning , particularly in: Reynolds-averaged Navier–Stokes (RANS) closure model identification Deep reinforcement learning (DRL) optimization for turbulence models Large eddy simulation (LES) data alignment Jet flow dynamics at varying Reynolds numbers Notable Contributions: The 2024 publication demonstrates 48% improvement in RANS-LES alignment using DRL-augmented Spalart–Allmaras models, with cross-Reynolds number application capabilities. Challenges include computational costs, numerical stability, and hyperparameter sensitivity.

• Computational Mathematics
• Partial Differential Equations
• Finite Element Methods
• +4 more

Jichun Li is a Professor in the Department of Mathematical Sciences at the University of Nevada Las Vegas, with a prolific research career spanning computational mathematics, image processing, and computer vision. His work bridges theoretical mathematics with practical applications in medical imaging, environmental science, and biometrics. Li's research interests center on computational mathematics with particular focus on partial differential equations and finite element methods, alongside significant contributions to image processing and computer vision. His work demonstrates a unique integration of mathematical theory with practical applications, particularly in medical imaging where his techniques enable improved early cancer diagnosis through advanced lesion segmentation. In environmental science, his research on land surface albedo dynamics provides critical insights into climate change impacts in sensitive regions like the Tibetan Plateau. His recent work on face recognition with synthetic data addresses contemporary challenges in biometric security systems. Analysis of Li's publication trends reveals a strategic evolution from foundational mathematical research toward interdisciplinary applications. While maintaining strong theoretical contributions in computational mathematics, particularly in PDEs and finite element methods, he has increasingly focused on medical imaging applications since 2020, developing novel techniques for cancer diagnosis and ultrasmall object detection in CT scans. His 2022-2025 publications show growing emphasis on synthetic data applications, particularly in face recognition challenges, demonstrating adaptability to emerging AI trends. Professor Li maintains an extensive collaborative network, with frequent co-authorship patterns suggesting mentorship relationships with researchers like Bo Yan, Weimin Tan, Guannan Chen, and Encai Zhang across multiple publications. His work appears in leading journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, and Computational Mathematics and Applications, reflecting both the theoretical depth and practical relevance of his research.

Yang LinView profile

Assistant Professor

• Microfluidics
• Acoustofluidics
• Environmental Monitoring
• +7 more

Yang Lin is an Assistant Professor at the University of Rhode Island , affiliated with the Department of Mechanical, Industrial & Systems Engineering . His research focuses on Microfluidics , Acoustofluidics , and Organ-on-a-Chip technologies, with applications in Environmental Monitoring , Food Safety , and Human Health . Education : Ph.D. in Mechanical Engineering (2019) and M.S. in Mechatronic Engineering (2015) from the University of Illinois at Chicago, and B.S. in Mechanical Design Manufacturing and Automation (2012) from Beijing Information Science and Technology University. Research Interests include: Acoustofluidics : Developing non-invasive, biocompatible fluid manipulation techniques using acoustic bubbles and membranes. AI-Enhanced Diagnostics : Leveraging convolutional neural networks for sample-to-answer diagnostic systems in public health. 3D Printed Microfluidics : Expanding additive manufacturing for low-cost, complex physiological structures in healthcare. Environmental Microfluidics : Detecting microplastics and contaminants in water and food systems. Publications highlight advancements in 3D printed microneedles , machine learning for nanoplastic detection , and magnetofluidic biosensors . Lab Members include current Ph.D. students and alumni who have completed M.S. and B.S. degrees under his mentorship.

David NovogView profile

Professor

• Nuclear Reactor Physics
• Thermal Hydraulics
• Computational Fluid Dynamics (CFD)
• +4 more

David Novog is a Professor in the Department of Engineering Physics at McMaster University . His work bridges nuclear reactor physics, thermal hydraulics, and computational modeling, with a focus on safety analysis and advanced reactor designs like small modular reactors (SMRs). Research Interests: Nuclear reactor safety, computational fluid dynamics (CFD) in reactor systems, neutron tomography, uncertainty quantification in nuclear data, and energy transition strategies. Teaching: Courses include Thermal Systems Design (ENGPHYS 2NE3), Numerical Methods for Engineering (ENGPHYS 3NM4), and Computational Methods for Engineering Physics (ENGPHYS 2CE4). Grants: Co-leader of a $2.6M project on SMR research, with ongoing contributions to OECD/NEA benchmarks and nuclear safety frameworks. Labs: Active at the McMaster Nuclear Reactor , where experiments on neutron flux and heat transfer are conducted.

• Nuclear Fusion
• Plasma Physics
• Tritium Fuel Cycle
• +6 more

Samuele Meschini is a Fixed-term Assistant Professor at Politecnico di Torino's Department of Energy (DENERG). His research focuses on nuclear fusion reactors, specifically tritium breeding cycles, plasma physics, and reactor design. He collaborates with MIT Plasma Science and Fusion Center (MIT PSFC), Princeton Plasma Physics Laboratory (PPPL), and Eni through experimental and computational projects. Research Interests: Meschini specializes in optimizing tritium fuel cycles for fusion reactors, examining microscopic tritium transport mechanisms, neutron damage impacts, and plasma efficiency. His work integrates machine learning for data analysis and addresses global scalability of fusion energy. Recent Publications: His articles (2022-2025) predominantly explore tritium management, reactor shielding, and fuel cycle optimization. Computational modeling and experimental validation are recurring themes, with emphasis on ARC-class reactor systems and deuterium-tritium fusion efficiency. Teaching & Advising: He serves as course collaborator for nuclear engineering courses including Nuclear Fission Plants and Radiation Protection , and mentors PhD student Gabriele Iob. He also co-instructed MIT's Engineering Principles for Fusion Reactors (2023-2024). Projects: Leads the commercial research initiative Modeling and Analysis of the ARC-class Tritium Cycle (2024-2025).

Mustafa SertView profile

Associate Professor

• Audio Signal Processing
• Machine Learning
• Semantic Multimedia
• +5 more

Mustafa Sert , currently serving as Associate Professor at Başkent University's Department of Computer Engineering and as Vice Dean of the Faculty of Engineering, has made significant contributions to audio signal processing, machine learning, and semantic multimedia. He earned his PhD (2006), MSc (2001), and BSc (1997) in Computer Engineering from Gazi University, supervised by prominent academics. PhD: Gazi University (2006) MSc: Gazi University (2001) BSc: Gazi University (1997) His research focuses on audio-visual content analysis , multimodal information fusion , and machine learning applications in semantic multimedia systems. He leads projects at the intersection of deep learning , speech processing , and acoustic pattern recognition , with work appearing in venues like IEEE Access, ACM Multimedia conferences, and Signal Processing and Communications Applications Conference (SIU). Recent publications demonstrate trends in automated audio captioning , medical speech analysis , and educational data mining , with applications spanning mental health detection, chatbot development, and reviewer selection systems. His work integrates transformer architectures , convolutional networks , and fuzzy decision-making models . Scientific Recognitions: Best Paper Award (7th Eurasian Congress on Emergency Medicine 2021) Outstanding Reviewer Awards (IEEE ICME 2020, 2021) Outstanding Area Chair Award (ACM Multimedia 2024) Academic Leadership includes roles as Senior IEEE Member, technical committee board positions in IEEE CTSoc divisions, and ACM membership. He supervises over 20 graduate students in topics ranging from depression detection through speech to geothermal permeability estimation , while maintaining active involvement in conference organization and journal reviewing for top-tier publications.

Mohamed Kamel RiahiView profile

Associate Professor

• Computational Mathematics
• Artificial Intelligence
• Quantum Computing
• +8 more

Dr. Mohamed Kamel Riahi is an Associate Professor in the Department of Mathematics at Khalifa University, specializing in computational mathematics, artificial intelligence, and quantum computing. His research focuses on high-performance computational solutions for complex systems in nuclear energy, fluid dynamics, and electromagnetism, with a particular emphasis on integrating advanced mathematical models and quantum-inspired algorithms. Education: MSc in Applied Mathematics from Paris Dauphine University; PhD in Mathematical Sciences from Pierre and Marie Curie University. Research Interests: Quantum Computing and Physics-Informed Machine Learning Computational Algorithms for High-Performance Computing Optimal Control of Partial Differential Equation (PDE)-Governed Systems Domain Decomposition & Parallel Time Methods Tensor Computations and Reduced Order Modeling Nonlinear Numerical Optimization Dr. Riahi actively mentors PhD and MSc students in areas like seismic wave modeling and AI-driven optimization, while serving on editorial boards for journals such as Applied Numerical Mathematics and PLOS ONE . His recent grant projects include quantum-inspired algorithms for fluid mechanics and reactor physics, funded by collaborations with ENTC-TII-ENEC. Scientific Awards: Inverse Problems Outstanding Reviewer Award Affiliated Centers: Emirates Nuclear Technology Center