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Hrvoje Jasak is a Professor of Continuum Physics at the Department of Physics (Cavendish Laboratory), University of Cambridge. He holds a fellowship at Christ’s College. His academic journey includes a BSc in Mechanical Engineering from the University of Zagreb (1992) and a PhD in CFD from Imperial College London (1996). Prior to academia, he held engineering roles at CD-adapco (now Siemens PLM), Nabla Ltd, and Ansys-Fluent Inc., contributing to CFD software development. His research focuses on numerical simulation methods, continuum physics, multiphase flows, naval hydrodynamics, and software development. He co-created OpenFOAM, chairs its Numerics Technical Committee, and leads the Computational Continuum Mechanics (CCM) research group within the Laboratory for Scientific Computing. His work integrates advanced numerical techniques like the partially rotating grid method, finite volume algorithms, and multiphysics coupling frameworks. Jasak is a seasoned developer with 25+ years of C++ expertise, having authored ~1 million lines of code. His group’s projects include the Naval Hydro Pack , fluid-structure interaction solvers, and the Eulerian multi-fluid model for dense sprays. He actively collaborates on international initiatives like the NUMAP-FOAM Summer School and the OpenFOAM community. His teaching spans MPhil programs, PhD supervision, and specialized CFD courses. Current research explores wave-ice interaction, lubricated contact modeling, and open-source software innovation. The CCM group’s work bridges academia and industry, addressing challenges in marine engineering, energy systems, and computational mechanics.

• Computational Fluid Dynamics (CFD)
• Continuum Mechanics
• Numerical Simulation Methods
• +5 more

Dr. Md Manjurul Ahsan serves as a Research Assistant Professor in the Department of Industrial & Systems Engineering at the University of Oklahoma, where he develops AI-driven solutions for healthcare diagnostics and advanced manufacturing optimization. His work bridges theoretical AI advancements with practical industrial and medical applications. Education: Ph.D. in Industrial and Systems Engineering, University of Oklahoma M.S. in Industrial Engineering, Lamar University B.S. in Industrial and Production Engineering, Shahjalal University of Science and Technology Research Focus: Dr. Ahsan specializes in Artificial Intelligence with technical depth in Machine Learning , Deep Learning , and Computer Vision to solve critical challenges in healthcare diagnostics and additive manufacturing . His research emphasizes Explainable AI to enhance model trustworthiness and deployment efficiency across Cyber-Physical-Social Systems, with significant contributions to Aerospace and Defense applications. Publication Trends: Recent work (2023-2025) reveals a strategic expansion from core manufacturing applications into medical AI (diffusion models for diagnostics), cultural preservation (NLP for Dravidian languages), and geopolitical AI analysis. His publications consistently address data imbalance challenges while advancing digital twin integration in quality control systems. Scientific Recognition: GCOE Dissertation Excellence Award (2023) International Student Scholarship (2022) Outstanding Academic Achievement in Engineering (2022) IEEE IEMCON Best Paper Award (2020) Netti Vincent Boggs Engineering Excellence Award (2020) Research Leadership: As director of the Sooner Additive Manufacturing Laboratory , Dr. Ahsan leads cross-disciplinary teams developing real-time monitoring systems using FARO arms and CMM metrology. His postdoctoral work at Northwestern University (2023-2024) advanced AI deployment frameworks, resulting in 60+ peer-reviewed publications with multiple papers ranking in engineering's top 1% for citations.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.

• Genetic Programming
• Genetic Algorithms
• Grammatical Evolution
• +7 more

Dr. Michael J Pekris is a Senior Lecturer in Mechanical Engineering Sciences at the University of Surrey, serving as Director of Employability within the School of Engineering. He holds a MEng and DPhil from the University of Oxford (2004), with a thesis on liquid crystal heat transfer in turbine blade cooling. His career includes R&D roles at Rolls-Royce, focusing on advanced seal technology and engine efficiency. He is a Chartered Engineer (CEng), Fellow of the IMechE (2023), and Fellow of the Higher Education Academy (FHEA). His research spans sustainable aviation, fluid dynamics, and heat transfer, with a focus on brush seals, hydrogen-fueled aircraft, and transcritical CO2 systems. He leads the Surrey Aerothermal Test Facility (SATF) and collaborates on projects like the Surrey Hydrogen Aircraft Performance Evaluator (SHAPE). He also serves as IMechE Academic Liaison Officer and Royal Academy of Engineering Visiting Professor Scheme Champion. Research interests include environmental technologies for aero-engine seals, sustainable aviation (electric/hydrogen), fluid dynamics, thermodynamic cycles, and rotating machinery. His work addresses energy efficiency, waste heat recovery, and low-emission propulsion systems. Notable contributions include Rolls-Royce Innovation Award (2013) and patents on seal technology. He actively engages in professional development initiatives and industry-academia partnerships. Publications emphasize seal dynamics, CFD modeling, and thermal management. Recent work explores hydrogen-fueled aircraft viability and CO2-based power systems. Collaborations involve Rolls-Royce, ASME Turbo Expo, and Surrey’s interdisciplinary engineering teams. His academic roles include teaching Structural Vibrations and Engineering Management, and advising the Professional Training Year module.

• Environmental Technologies
• Sustainable Aviation
• Fluid Dynamics
• +7 more

Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.

• Machine Learning
• Deep Learning
• Predictive Maintenance
• +6 more

Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.

• Superconductor Technology
• Cryogenic Techniques
• Electric Machines and Drives
• +3 more

Dr. Michael Kleeberger is a Researcher at the Chair of Materials Handling, Material Flow, Logistics (FML) at the Technical University of Munich, based at Boltzmannstr. 15 in Garching. He collaborates closely with Prof. Johannes Fottner and maintains an active research profile in crane dynamics and mechanical systems simulation. His research specializes in Materials Handling and Logistics with emphasis on Crane Dynamics, Flexible Multibody Systems, and Control Systems. He develops advanced models for hydraulic actuated cranes, focusing on dynamic behavior during hoisting, slewing, and trajectory operations using port-Hamiltonian formulations and geometrically exact beam theory. His work bridges theoretical mechanics with industrial applications in heavy machinery. Analysis of his 15 most recent publications reveals consistent focus on numerical methods for flexible crane structures, with growing emphasis on optimal control strategies (2020-2025). Key trends include port-Hamiltonian system applications, lunar crane feasibility studies, and vibration mitigation techniques for lattice boom and knuckle boom configurations across diverse operational scenarios. As part of FML, Dr. Kleeberger contributes to TUM's leadership in logistics engineering through industry-collaborative projects and fundamental research in material flow systems, maintaining the chair's reputation for excellence in mechanical dynamics and practical engineering solutions.

• Materials Handling
• Logistics
• Crane Dynamics
• +3 more

Dr. Wade Smith is a Senior Lecturer within the School of Mechanical and Manufacturing Engineering at the University of New South Wales. He is an active member of the WAVES research group (Wear, Aeroacoustics and Vibration in Engineering Systems) and conducts his research in the Tribology and Machine Condition Monitoring laboratory. His primary research interests include vibration-based diagnostics of rotating machinery, prognostics of rotating machinery, gear wear monitoring and prediction, simulation and modeling of rotating machines for diagnostic applications, and signal processing of machine vibration signatures using cyclostationarity. His work has significant applications in industrial machinery health monitoring and predictive maintenance systems. Dr. Smith's recent publications demonstrate a consistent focus on advanced diagnostic techniques for rotating machinery, with particular emphasis on gear systems and bearings. His research integrates traditional mechanical engineering principles with modern signal processing and machine learning approaches to develop more effective condition monitoring solutions. He actively supervises PhD and Masters students on projects related to gear diagnostics, wear monitoring, and vibration analysis. His current research projects include gear diagnostics in planetary gearboxes using internal sensors, gear wear monitoring and prediction, sliding contact-induced vibration studies, and transmission-error-based gear diagnostics. Dr. Smith's laboratory is equipped with specialized facilities including gearbox test rigs (both planetary and parallel configurations), a rolling element bearing test rig, an engine test rig, friction rig, tribometer, high-quality microscope, and extensive instrumentation for vibration analysis. His research has attracted collaborations with institutions including Queensland University of Technology, SpectraQuest (USA), Weir Minerals, University of Technology Sydney, RWTH Aachen University (Germany), and Safran.

• Dynamics, vibration and vibration control
• Signal processing
• Modelling and simulation
• +7 more

Stavros G. Vougioukas is a Professor and Vice Chair in the Department of Biological and Agricultural Engineering at the University of California, Davis. His research focuses on agricultural robotics, mechanization, and automation for specialty crops, with particular emphasis on robotic harvesting systems and precision agriculture technologies. He leads initiatives in developing actuator systems, perception, and control mechanisms to optimize crop management. Key areas of expertise include robotic fruit harvesting, autonomous vehicle navigation in orchards, and site-specific pest management strategies. His work integrates mechanical engineering principles with advanced automation to address labor shortages and improve agricultural efficiency. Recent projects emphasize data-driven solutions for yield estimation, worker activity analysis, and economic viability of robotic systems. Academic contributions span over 50 peer-reviewed publications (2023-2025), with a focus on robotic orchard platforms, crop transport systems, and sensor-based automation. Notable innovations include vacuum suction end-effectors for fruit harvesting and GNSS-free navigation systems for autonomous vehicles. He also explores sustainable agricultural machinery through techno-economic analyses of electric/hybrid tractors. Current research bridges robotics and agricultural economics, addressing labor cost optimization and precision irrigation. His lab collaborates with industry partners to translate prototypes into field-ready solutions, emphasizing practical applications for specialty crop production systems.

• Agricultural Robotics
• Mechanization of Specialty Crops
• Automation in Harvesting
• +5 more

Professor Tony Shardlow is affiliated with the Department of Mathematical Sciences at the University of Bath , UK. His research spans Stochastic Differential Equations , Bayesian Inverse Problems , Statistical Shape Modelling , and Numerical Analysis , with applications in data science, medical imaging, and computational physics. Labs/Teams : IMI (Institute for Mathematical Innovation), Prob-L@b (Probability Laboratory at Bath), SAMBa (EPSRC Centre for Doctoral Training in Statistical Applied Mathematics). Recent Research Trends : Focus on geometric shape analysis using flow fields, stochastic PDEs for particle dynamics, and Bayesian inference techniques in industrial and medical contexts. Collaborative work bridges computational mathematics with applications in hip dysplasia assessment and pesticide delivery systems. Advising : Supervised Fengpei Wang's PhD thesis on dimension reduction and Sinkhorn algorithms. Collaborates with researchers like N. D. F. Campbell and C. Poon. Teaching : Offers MA30170 - Numerical Solution of Elliptic PDEs.

• Stochastic Differential Equations
• Bayesian Inverse Problems
• Statistical Shape Modelling
• +3 more

Chasalevris Athanasios is an Assistant Professor at the School of Mechanical Engineering, National Technical University of Athens (NTUA), specializing in Rotordynamics and Tribology. His research focuses on nonlinear dynamics of machines, active bearings, and turbomachinery design. He holds a Ph.D. in Rotordynamics from the University of Patras (2009) and a Diploma in Mechanical & Aeronautical Engineering (2004). Research Interests: Machine Dynamics, Turbomachinery, Active Bearings, Bifurcation Analysis, Crack Detection. Teaching: Dynamics of Rotating Machines, Kinematics and Dynamics of Mechanisms, Machine Elements I. Collaborations: KIT (Germany), RPI (USA), MTU Aero Engines (Germany), and Cleveland State University (USA). Awards include the Humboldt Foundation Fellowship (2010), GE's 'Beyond and Ahead' Award (2017), and recognition as a Top 2% Scientist (2023). He has authored over 40 publications and supervised numerous graduate students. Current roles include Academic Advisor at DOATAP and Associate Editor for journals like ASME Journal of Tribology.

• Rotordynamics
• Machine Dynamics and Control
• Tribology
• +4 more

Daniel C. Ludois is a Professor of Electrical and Computer Engineering at the University of Wisconsin–Madison, College of Engineering, where he also serves as the Research & Innovation Director for the Wisconsin Electric Machines and Power Electronics Consortium (WEMPEC). He holds the distinguished title of Jim and Anne Sorden Professor and is an H.I. Romnes Faculty Fellow, reflecting his leadership in research and education. Dr. Ludois earned his Ph.D. in Electrical Engineering from UW–Madison in 2012 and a B.S. in Physics from Bradley University in 2006. His research focuses on advancing power conversion technologies, particularly through electrostatic machines, capacitive wireless power transfer, wound field synchronous machines, and integrated power electronics. He teaches core courses such as ECE 411 (Introduction to Electric Drives), ECE 711 (Dynamics and Control of AC Drives), and ECE 713 (Electromagnetic Design of AC Machines). Power Electronics Wireless Power Transfer (Capacitive Coupling) Electrostatic Machines (Copper-Free, Steel-Free Motors) Sustainable Electric Machine Design High-Frequency and High-Voltage Power Conversion Brushless Excitation Systems Integration of Inductors and Capacitors His recent publications emphasize high-torque electrostatic machines using dielectric liquids, capacitive power transfer for aerial platforms and rotating machinery, and innovative inverter topologies. These works reflect a strong trend toward sustainable, high-performance electric machines that reduce reliance on rare-earth materials and traditional conductive components. Dr. Ludois has received numerous honors, including: NSF CAREER Award (2015) Moore Inventor Fellowship (2017) DOE InDEEP Competition Phase I & II Awards (2024) H.I. Romnes Faculty Fellow (2023) Vilas Faculty Early Career Investigator Award (2022) Wisconsin Alumni Research Foundation (WARF) Innovation Award (2012) He has mentored a highly entrepreneurial group of students, several of whom have founded startups such as H3X (Forbes 30 Under 30) and C-Motive Technologies, which he co-founded to commercialize electrostatic power conversion devices. His team has secured significant recognition, including multiple Grainger Power Engineering Fellowships and IEEE best paper awards. Dr. Ludois leads a vibrant research lab focused on next-generation power systems, with funding from federal agencies and industry partners, driving innovation in electric transportation, renewable energy, and industrial automation.

• Power Electronics
• Wireless Power Transfer
• Electrostatic Machines
• +13 more

Christopher Goyne is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia (UVA) and Director of the UVA Aerospace Research Laboratory. He holds a B.Eng. (1991) and Ph.D. (1999) in Mechanical Engineering from the University of Queensland, Australia. His research focuses on hypersonic propulsion, scramjet technology, instrumentation development, and advanced manufacturing. He leads the UVA Hypersonics Research Complex and is a key figure in the University Consortium for Applied Hypersonics. Goyne’s work includes contributions to NASA’s Hyper-X Program and the HyShot scramjet flight test program. He is an Associate Fellow of the AIAA and serves on editorial and advisory boards for journals and organizations such as the Shock Waves journal and Virginia’s Aerospace Advisory Council. Education: B.Eng. (Mechanical Engineering, University of Queensland, 1991); Ph.D. (Mechanical Engineering, University of Queensland, 1999). Research Interests: Hypersonics and scramjet propulsion Diagnostic techniques (e.g., laser-based methods, optical emission spectroscopy) Wind tunnel and flight testing Controls and adaptive systems for hypersonic flow paths Advanced manufacturing for aerospace components Awards: Recipient of the 2023 James C. McDaniel Fellow Award and 2022 Outstanding Researcher Award. Holds leadership roles in AIAA committees, including past Chair of the HyTASP Program Committee. Recognized with the Sigma Gamma Tau Outstanding Aerospace Professor Award (2006) and multiple research fellowships. Grants and Projects: Funded by NASA, the Air Force Office of Scientific Research, and industry partners. Leads UVA’s contributions to hypersonic ground and flight testing, including sensor development and combustion efficiency studies. Labs and Teams: Directs the UVA Aerospace Research Laboratory, collaborating on projects such as the UVA Hypersonics Research Complex and the University Consortium for Applied Hypersonics. Advises student chapters of AIAA and Sigma Gamma Tau.

• Hypersonics
• Scramjets
• Instrumentation and Laser Diagnostics
• +5 more

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +4 more

Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.

• Computational Fluid Dynamics
• Turbomachinery
• Aerodynamics
• +4 more