Find Academic Supervisors

• Plasma Spectroscopy
• Particle Transport
• Heat Transport
• +5 more

Benedikt Geiger is an Associate Professor at the University of Wisconsin–Madison College of Engineering, affiliated with the Department of Nuclear Engineering and Engineering Physics and Electrical & Computer Engineering. He leads the Helically Symmetric eXperiment (HSX), focusing on quasi-symmetry and transport in stellarators, and directs the Turbulence and Spectroscopy Group conducting diagnostics at DIII-D, W7-X, and NSTX-U. Ph.D., Ludwig-Maximilians University (2013) Diploma (MS), Ludwig-Maximilians University (2009) Diploma Thesis, Max-Planck Institute for Physics (2009) His research spans experimental plasma spectroscopy, particle/heat transport, synthetic diagnostics, and turbulence analysis in fusion devices. Recent work includes developing deep learning algorithms for ELM prediction and real-time confinement regime detection, alongside turbulence studies in negative triangularity plasmas and impurity transport in W7-X. Key awards include the 2025 Grainger Institute for Engineering Professorship, 2020 DOE Early Career Award, 2014 Landau Spitzer Award, and the 2014 Otto-Hahn Medal. He holds leadership roles in the National Stellarator Coordinating Committee, Transport Task Force, and International Stellarator and Heliotron Workshop.

• Mechanical Engineering
• Tribology
• Multimodal Sensing
• +5 more

Dr. Min Yu is an Imperial College Research Fellow (ICRF) in the Department of Mechanical Engineering at Imperial College London . He leads an independent research program focused on in-situ multimodal sensing of mechanical interfaces , integrating advanced materials, intelligent control, multiphysics modeling, and data-driven technologies. His work bridges tribology, robotics, and sensing with applications in lubrication systems and robotic haptic interfaces. Education: PhD in Mechanical Engineering, Imperial College London (2014–2018) MSc in Engineering, Zhejiang University (2011–2014) BEng in Engineering, Xi’an Jiaotong University (2007–2011) Research Interests: Dr. Yu’s core research areas include tribology , ultrasonic sensing , robotic haptics , lubrication systems , and data-driven control . He develops novel sensing technologies for real-time monitoring of mechanical interfaces, with applications in engines, bearings, transmissions, and robotic systems. His work emphasizes closed-loop intelligent lubrication and bio-inspired robotic sensing . Publications & Trends: Dr. Yu has authored over 60 peer-reviewed papers and holds 6 patents . His recent work (2024–2025) focuses on ultrasonic-based oil film measurement, triboelectric sensors for robotics, and advanced control systems for automotive suspensions. These publications reflect a strong interdisciplinary approach combining mechanical engineering , AI-driven control , and sensor innovation . Awards & Grants: Imperial College Research Fellowship (ICRF 2022–2026) Royal Society International Exchanges – Cost Share Scheme State Key Laboratory of Fluid Power and Mechatronic Systems Open Foundation Taiho Kogyo Tribology Research Foundation Grant Dame Julia Higgins Engineering Postdoc Collaborative Research Fund (2019) Peter Jost Travel Fund (2022) Collaborations & Labs: Dr. Yu collaborates with multiple groups at Imperial College London including the Tribology Group , Non-Destructive Evaluation (NDE) Group , Control and Power Group , Optical & Semiconductor Devices Group , and Geotechnics Group . He also partners with international institutions such as Georgia Tech , Xi’an Jiaotong University , Zhejiang University , HUST , and Tsinghua University , as well as industry leaders like Shell , ExxonMobil , Toyota , and Jaguar Land Rover .

• Structural Engineering
• Blast Resistance
• Ultra-High Performance Concrete
• +3 more

Professor Chengqing Wu is a distinguished academic in the School of Civil and Environmental Engineering at the University of Technology, Sydney (UTS). He serves as Professor of Structural Engineering with a research focus on blast-induced phenomena and advanced concrete technologies. His expertise spans structural response to blast loading, mitigation of blast effects, and the development of ultra-high performance concrete systems. Professor at University of Technology, Sydney Former Chair of Australian Chapter of International Association of Protective Structures (2013-2017) Associate Editor of ASCE Journal of Performance of Constructed Facilities Editorial Board Member of International Journal of Protective Structures Professor Wu's research interests center on structural engineering with emphasis on blast resistance, ultra-high performance concrete, geopolymer concrete, and structural response to extreme loading conditions. His work bridges theoretical analysis with practical applications, particularly in protective structures and extreme environment construction. His research group has made significant contributions to understanding material behavior under blast, impact, and extreme thermal conditions, with applications ranging from terrestrial infrastructure to potential lunar construction. Analysis of Professor Wu's recent publications reveals a strong focus on advanced concrete technologies for extreme environments. His research spans 3D-printed concrete, lunar and Martian construction materials, cryogenic performance of concrete, and blast-resistant structural systems. A notable trend is the increasing application of computational methods and machine learning techniques to predict structural response to explosions, alongside traditional experimental approaches. His work demonstrates a progression from fundamental material characterization to complex structural system analysis, with growing emphasis on sustainable construction and extraterrestrial applications. Author/co-author of over 200 international journal papers Editor of four conference proceedings Editor of two ASCE special issues Editor of two International Journal of Protective Structures special issues Professor Wu has successfully attracted over 4 million dollars in research funding from diverse sources including the Australian Research Council (ARC), Defence Science and Technology Organization (DSTO), and industry partners. His current projects include Eco-friendly Ultra-High Performance Rubberised Concrete, Decarbonised Infrastructure, Structural protective design on large capacity flywheel energy storage system, and Gas Explosion Resistance of Non-Cement Based High Performance Concrete. He actively supervises undergraduate honors students, coursework master's students, and research higher degree candidates, with several scholarships available for prospective postgraduates and research associates. Professor Wu leads research in protective infrastructure technology through the Joint Research Centre for Protective Infrastructure Technology and Environmental Green Bioprocess with Tianjin Chenjian University. His team operates the National Drop Weight Impact Testing Facility and contributes to the National Facility for Physical Blast Simulation. Current research directions include sustainable concrete technologies for extreme environments, blast-resistant structural systems, and innovative applications of concrete in space exploration contexts.

Tristan BuckmasterView profile

Professor

• Partial Differential Equations
• Fluid Dynamics
• Wave Turbulence
• +3 more

Tristan Buckmaster is a Professor of Mathematics at the University of Maryland, appointed in July 2022. Previously, he served as Assistant Professor at Princeton University and Courant Instructor at New York University's Courant Institute of Mathematical Sciences, following his 2014 PhD at the International Max Planck Research School (IMPRS). His doctoral research at the Max Planck Institute for Mathematics in the Natural Sciences focused on the Onsager Conjecture, defended with summa cum laude. Academic trajectory includes a sabbatical at Princeton's Institute for Advanced Study. Buckmaster specializes in partial differential equations with groundbreaking work on Navier-Stokes equations, proving non-uniqueness of weak solutions that earned the 2019 Clay Research Award. His research spans wave turbulence theory development and current investigations into singularity formation in liquids using machine learning, establishing new mathematical approaches to century-old fluid dynamics problems. He received the Clay Research Award (2019) for transformative contributions to partial differential equations, particularly resolving aspects of the Onsager Conjecture through his doctoral and postdoctoral work. Buckmaster leads a major Simons Foundation-funded international collaboration on wave turbulence, merging mathematical rigor with physics. His recent machine learning applications in fluid dynamics, featured in Quanta Magazine and associated podcasts, demonstrate interdisciplinary innovation in solving fundamental liquid flow problems.

Gjika MeriView profile

Researcher

• Biomedical Engineering
• Cardiovascular Signal Analysis
• Machine Learning
• +3 more

Meri Gjika is a PhD student in the Department of Information Engineering at Università Politecnica delle Marche, focusing on cardiovascular signal analysis, cardiac arrhythmias, and machine learning applications in cardiovascular care. Education: Bachelor's in Biomedical Engineering (July 2022), Università Politecnica delle Marche (thesis: impact of asthma on respiratory and electrocardiographic signals, QT interval variations) Master's in Biomedical Engineering cum laude (July 2024), Università Politecnica delle Marche (thesis: coronary artery resistance modeling for aortic-root fluid-dynamics and perfusion simulations) Research: Specializes in analyzing cardiovascular signals to study atrial fibrillation and other arrhythmias, leveraging machine learning and AI to develop predictive models for disease progression and personalized patient management. Her work includes computational hemodynamics and coronary blood flow dynamics through advanced numerical simulations. Experience: Completed an Erasmus+ internship at the Department of Cardiology, Erasmus Medical Center in Rotterdam (2024), focusing on vascular hemodynamics assessment for disease progression. Contact: Email m.gjika@pm.univpm.it , Office: Q170_053, Phone: +39 071 2204465.

Thomas SheldrakeView profile

Assistant Professor

• Volcanology
• Geochemistry
• Geochronology
• +3 more

Thomas Sheldrake is an SNSF Eccellenza Professorial Fellow (Assistant Professor) at the Department of Earth Sciences, University of Geneva. His research focuses on magmatic processes, volcanic systems, and geochronological methods with applications to understanding eruption dynamics and tectonic interactions. He specializes in integrating geochemical, petrological, and computational approaches to study magma evolution and volcanic hazards. Research interests include: Magmatic system evolution and eruption triggers Zircon-based geochronology and petrochronology Volcanic crystal records of magma storage and degassing Remote sensing of volcanic structures using micro-CT Statistical modeling of volcanic eruption data His work spans field-based studies in the Caribbean, Iceland, and Italy, alongside computational methods for mineral analysis. Notable contributions include linking sea-level changes to magmatic pulses during the Messinian crisis and developing zircon age spectra techniques for magma evolution quantification. His recent 2025 study on Caribbean coral porosity demonstrates interdisciplinary approaches bridging geology and environmental science. Scientific Awards: SNSF Eccellenza Professorial Fellowship Research collaborations involve institutions globally, with frequent co-authorship on topics ranging from Holuhraun eruption geochemistry to Saint Kitts volcanic stratigraphy. His work emphasizes bridging fundamental geoscience with applied volcanic hazard assessment.

• Aerodynamics
• Aeroacoustics
• Renewable Energy Systems
• +4 more

Delibra Giovanni is an Associate Professor at Sapienza University of Rome, specializing in aerodynamics, aeroacoustics, and renewable energy systems. His research focuses on optimizing turbomachinery performance, including axial fans, wind turbines, and hydrogen storage systems. He employs advanced computational fluid dynamics (CFD) and machine learning techniques to address challenges in renewable energy integration, thermal management, and noise reduction. Key research areas include: Wind energy systems and offshore wind farm design Hydrogen storage and safety in green energy applications Aeroacoustic control in industrial fans and turbines CFD-based optimization of heat exchangers and cooling systems Recent work emphasizes the integration of photovoltaic and biomass systems in renewable energy communities, as well as experimental validation of wave energy turbines. His publications highlight innovations in fan blade design, leakage modeling, and multi-objective optimization frameworks for sustainable energy infrastructure. Collaborations involve both academic institutions and industry partners, focusing on real-world applications such as tunnel ventilation systems and Mediterranean island energy solutions. Giovanni's contributions bridge theoretical modeling with practical engineering challenges in the transition to clean energy.

Torben Anker LenauView profile

Associate Professor

• Biomimetics
• Engineering Design
• Sustainable Design
• +3 more

Torben Anker Lenau is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His work focuses on biomimetic design, sustainable engineering, and innovative material applications. He leads research projects addressing multi-functional problem-solving, renewable energy systems, and eco-design principles. His expertise contributes to UN Sustainable Development Goals, particularly in sustainable cities, clean energy, and responsible consumption. Key research interests include biomimicry in product development, lifecycle assessment, and bio-inspired materials. Notable projects include kinetic energy harvesting for wildlife tracking and bio-inspired medical devices. He has published extensively on topics like prototyping strategies, material selection, and biomimetic self-healing systems. Projects: Integrated Design (1999–present), Processed Material Selection (1996–present), CAD/CAM Prototyping (1996–present) Contributions: Over 100 publications, 27 projects, and patents in medical and energy sectors His work emphasizes translating biological principles into engineering solutions for sustainability challenges. Collaborations span academia and industry, with a focus on global impact through innovative design methodologies.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +4 more

Ke Yan is an Associate Professor in the Department of Computer Science at the National University of Singapore's College of Design and Engineering. With extensive research output from 2021-2026, their work spans computer vision, artificial intelligence, and multimodal learning systems. National University of Singapore (Primary Affiliation) Collaborations with University of Electronic Science and Technology of China Research ties with Tencent Youtu Lab in Shanghai Research focuses on advancing computer vision techniques, particularly in medical image analysis, vision-language integration, and fault diagnosis systems. Their work bridges theoretical AI development with practical applications in healthcare, industrial systems, and environmental monitoring. Notable contributions include novel approaches to multimodal learning, medical image segmentation with sparse annotations, and improving reliability of large vision-language models. Recent publication trends (2024-2026) demonstrate increasing focus on multimodal systems, with significant contributions to vision-language model reliability, medical AI applications, and efficient transfer learning techniques. Their work frequently addresses critical challenges like hallucination mitigation in large models and sparse data scenarios in medical imaging. As an advisor, they mentor multiple researchers including Junlong Du, Shouhong Ding, and Zhiwen Lin, with whom they frequently collaborate on cutting-edge computer vision projects. Their research group maintains strong industry connections, particularly with Tencent's AI research division. The research is conducted within NUS's computer vision and AI research ecosystem, collaborating with multiple laboratories focused on multimodal intelligence and practical AI deployment in real-world systems.

• Electrical Engineering
• Automotive Engineering
• Machine Learning
• +3 more

Shengdun Zhao is an active researcher in the fields of Electrical Engineering, Automotive Engineering, and Machine Learning, contributing extensively to optimization techniques and control systems for electric vehicles and motors. His work spans journals like IEEE Transactions on Vehicular Technology and Journal of Intelligent & Fuzzy Systems , focusing on practical applications of deep reinforcement learning, meta-learning, and multi-objective optimization. Key research areas: Electric motor control, energy management systems, and clustering algorithms. Collaborates with researchers such as Yiming Zhang, Wei Du, Chee-Kong Chui, and Chin-Boon Chng. His publications from 2007–2025 address technical challenges in mechatronics, sustainable transportation, and data-driven engineering solutions. Research Trends Recent articles highlight Zhao's emphasis on deep reinforcement learning for motor control, meta-learning in energy systems, and evolutionary algorithms for multi-objective optimization. He integrates machine learning with automotive engineering to improve electric vehicle efficiency and motor performance.

• High-Performance Computing (HPC)
• Computational Fluid Dynamics
• Quantum Computing
• +3 more

Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.

• Machine Learning
• Climate Modeling
• Environmental Science
• +3 more

Dr. Shuolin (Shawn) Li is a Postdoctoral Research Scientist at Columbia University's Data Science Institute, collaborating with Professors Pierre Gentine, Upmanu Lall, and Tian Zheng. He holds a Ph.D. in Fluid Dynamics and Hydrology and an M.S. in Computer Science from Duke University. His research focuses on developing machine learning algorithms for Earth observations, particularly in climate model parameterization using Bayesian inference, neural networks, and physical parameterizations. He collaborates with the Learning the Earth with Artificial Intelligence and Physics (LEAP) initiative and scientists at the National Center for Atmospheric Research (NCAR). Research interests include data assimilation, environmental fluid mechanics, and interdisciplinary applications of machine learning in hydrology, turbulence, and climate science. His work bridges computational methods with physical processes, addressing challenges in sediment transport, vegetation dynamics, and turbulent flow modeling. Recent publications highlight advancements in generative data assimilation, sediment flux parameterization, and turbulence modeling. His contributions span environmental engineering, climate science, and mathematical modeling, emphasizing scalable solutions for complex Earth systems. Collaboration with LEAP and NCAR underscores his commitment to advancing AI-driven Earth science. No formal awards are listed, but his work reflects significant contributions to interdisciplinary environmental research.

Neil SimsView profile

Professor

• Smart Fluids
• Modeling and Design of Smart Fluid Dampers
• Control and Stability of Smart Fluid Dampers
• +6 more

Professor Neil Sims holds a faculty position in the Department of Mechanical Engineering at the University of Sheffield . His research spans smart fluid dampers , machining vibration control , and uncertainty propagation in structural systems. He has pioneered methods for chatter suppression in high-speed machining and energy harvesting system design. Educational Background: First Degree in Mechanical Engineering, University of Sheffield PhD in Mechanical Engineering, University of Sheffield Research Focus includes smart materials (magnetorheological/electrorheological fluids), nonlinear dynamics , and adaptive control systems . His work integrates computational modeling , experimental validation , and machine learning for industrial applications. Recent Publications highlight advancements in inerter-based vibration absorbers , machine learning for machining optimization , and actuator saturation challenges . These studies reflect his interdisciplinary approach to structural stability and manufacturing efficiency. Scientific Recognition: EPSRC Advanced Research Fellowship (2003-2008) on machining chatter Collaborative Impact extends to seismic isolation , robotic machining , and energy harvesting from human motion and machinery. His editorial work in the Proceedings of the Institution of Mechanical Engineers, Part B (2021) underscores leadership in machining science.

Alessandro CorbettaView profile

Assistant Professor

• Pedestrian Dynamics
• Machine Learning
• Fluid Turbulence
• +3 more

Alessandro Corbetta is an Assistant Professor in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He leads the 'AI for Traffic and Complex Flows' group, focusing on pedestrian dynamics, machine learning in fluid mechanics, and active flowing matter. His work integrates empirical data, statistical physics, and computational methods to model crowd behavior and optimize pedestrian environments. Education & Academic Background: MSc (cum laude) in Mathematical Engineering, Polytechnic University of Turin (2011) PhD in Applied Mathematics, TU/e (2016) PhD in Structural Engineering, Polytechnic University of Turin (2016) Research Interests: His research spans pedestrian dynamics, machine learning for fluid mechanics, turbulence modeling, and high-performance computing. Key areas include real-world crowd tracking, AI-driven crowd management, and statistical mechanics applied to big data analytics. Awards & Grants: 2021 Ig Nobel Prize in Physics for studying pedestrian collision avoidance 2018 VENI Grant (NWO) for 'Understanding and Controlling Human Crowd Flows' Teaching & Academic Contributions: Responsible for courses like 'Machine Learning in Science' and 'Machine Learning for Fluid Mechanics' Editor-in-Chief of Collective Dynamics Labs & Collaborations: Collaborates with municipalities, museums, and festivals to deploy real-time crowd management systems. Active in TU/e's Intelligent Lighting Institute and Fluids and Flows research groups.

• Nanotechnology
• Condensed Matter Physics
• Materials Science
• +3 more

Chang Liu is an Assistant Professor at the Department of Economics, Stony Brook University. He specializes in advanced microscopy techniques and nanoscale material characterization, with a focus on plasmonics, 2D materials, and quantum phenomena. His research employs cutting-edge tools like scattering-type scanning near-field optical microscopy (s-SNOM) and terahertz spectroscopy to explore electronic and optical properties at the nanoscale. His work spans interdisciplinary areas including graphene heterostructures, Dirac materials, and phase transitions in correlated oxides. Liu’s contributions include developing novel imaging modalities (e.g., BOSON) and advancing understanding of polaritonic systems. He collaborates with synchrotron facilities like NSLS-II for ultra-high resolution studies. Notable research trends include exploring nanoscale structural phase separations, tunable phonon polaritons in van der Waals heterostructures, and nano-photocurrent dynamics in twisted bilayer systems. His technical innovations, such as machine learning for nano-optical data analysis and cryogenic s-SNOM setups, enhance experimental precision. Liu’s lab is part of the Stony Brook Center for Game Theory, though his direct research themes focus on materials physics. Current projects include roadmap development for 2D material photonics and exploring moiré ferroelectricity in twisted WSe₂ systems. No awards or grants are explicitly listed in the provided materials.