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Yang ChenView profile

Lecturer

• Composite Materials
• Computational Mechanics
• Materials Science
• +5 more

Yang Chen is a Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with multiple research centres including the Centre for Integrated Materials, Processes & Structures (IMPS) and the Centre for Regenerative Design & Engineering for a Net Positive World (RENEW). He holds a PhD from the Université Paris-Est (collaborating with French Alternative Energies and Atomic Energy Commission) and completed postdoctoral research at the University of Oxford. His research focuses on advanced techniques for data-rich experiments and simulations in heterogeneous materials, particularly fibre-reinforced composites applied in aerospace, automotive, and nuclear energy sectors. Key expertise includes FFT solvers, X-ray computed tomography, nonlinear mechanical modelling, and fluid flow in porous media. He has secured prestigious fellowships, including the EPSRC Future Composites Research Hub Innovation Fellowship and the Humboldt Fellowship. Teaching responsibilities include the ME20016 Solid Mechanics 3 course for mechanical engineering undergraduates. Current PhD supervision opportunities span topics like hydrogen storage composites, nuclear fusion materials, and nuclear waste management. He actively collaborates on projects such as HyFIVE (Hydrogen Storage) and explores advanced ceramic materials for nuclear energy. His work aligns with UN Sustainable Development Goals, emphasizing sustainable innovation and energy solutions. Notable contributions include developing physics-informed neural networks for resin flow prediction and machine learning-based emulators for constitutive modelling. His publications address fracture mechanics, computational homogenization, and material degradation under irradiation. He is open to supervising doctoral students through funded programs like ZENITH and GW4+ DLTP.

Daniel HaraborView profile

Associate Professor

• Artificial Intelligence
• Pathfinding Algorithms
• Optimization
• +3 more

Daniel Harabor is an Associate Professor in the Department of Data Science & AI at Monash University, Australia. He holds a PhD and BSc (Honours) in Computer Science from the Australian National University (ANU). His research focuses on pathfinding algorithms, multi-agent systems, and optimization, with notable contributions to multi-agent pathfinding (MAPF), transportation routing, and constraint-based reasoning. He leads or collaborates in projects like the 2023 MAPF Competition and the Future of Urban Routing initiative. Education: PhD in Computer Science, ANU (2014) BSc (Honours) in Computer Science, ANU (2007) Research Interests: His work emphasizes practical and efficient solutions for combinatorial search problems, including real-time pathfinding, public transport optimization, and large-scale multi-agent coordination. He develops algorithms for dynamic environments with applications in robotics, urban planning, and logistics. Awards: DECRA Research Fellowship (Australian Research Council) Projects & Grants: He has secured funding for projects such as 'Personalised Public Transport' (2019–2025) and 'Improved Constraint Reasoning for Robust Multi-agent Path Planning' (2021–2024). His work often involves collaboration with global experts in AI and robotics. Labs & Teams: Active in the Data Science & AI research group at Monash, contributing to MAPF competitions and open-source pathfinding tools like JPS (Jump Point Search).

Reza RazzaghiView profile

Associate Professor

• Smart Grids
• Renewable Energy
• Power Systems
• +3 more

Reza Razzaghi is an Associate Professor and ARC DECRA Fellow in the Electrical and Computer Systems Engineering department at Monash University. He leads the Distributed and Intelligent Power Systems (DIPS) research group, focusing on smart grid solutions, renewable energy integration, and electromagnetic transients. He holds a PhD from EPFL, Switzerland (2016), and has held postdoctoral roles there before joining Monash. His research spans distributed energy resources, energy data analytics, and grid reliability, contributing to UN Sustainable Development Goals. He has secured multiple grants as Primary Chief Investigator, including projects on fault location technology, voltage management, and DER hosting capacity. Notable achievements include the 2019 Richard B. Shulz Best Transactions Paper Award, Basil Papadias Best Paper Award (2013), and Dean's Award for Technological Innovation (2021). He serves on editorial boards of IEEE journals and chairs IEEE PES student activities. Key research outputs include over 110 publications, with recent work on fault location methods, EV charging coordination, and decentralized P2P energy trading frameworks. His lab develops real-time monitoring systems and advanced protection techniques for modern power grids.

• Renewable Energy Systems
• Fluid Dynamics
• Erosion Prediction in Turbomachinery
• +3 more

Paolo Venturini is an Associate Professor at Sapienza University of Rome , specializing in fluid dynamics, renewable energy systems, and erosion prediction in turbomachinery. His research focuses on integrating computational modeling with experimental validation to address challenges in energy efficiency, material durability, and sustainable engineering solutions. Key research interests include Hydrogen production via floating photovoltaic systems Cavitation-induced damage in tidal turbines Water washing efficiency in axial compressors Biomass gasification for energy recovery Rain erosion on wind turbine blades His work bridges mechanical engineering and environmental science, with applications in renewable energy integration, waste-to-energy systems, and aerospace thermal management. Recent studies emphasize interdisciplinary approaches, including mental health assessment tools for healthcare staff. Publications span 20+ years, with notable contributions to Energy , Applied Thermal Engineering , and Renewable Energy . Ongoing projects explore hybrid energy systems, plasma-based wastewater purification, and predictive modeling for turbine blade aging.

Xiaoliang WangView profile

Research Professor

• Aerosol Instrumentation
• Air Quality Measurement
• Vehicle Emission Characterization
• +3 more

Xiaoliang Wang is a Research Professor and Director of the Atmospheric Sciences Graduate Program at the University of Nevada, Reno (UNR) and the Desert Research Institute (DRI). He holds a Ph.D. and M.S. from the University of Minnesota (2006 and 2002) and dual B.E. degrees in Thermal Engineering and Environmental Engineering from Tsinghua University (2000). His research focuses on aerosol science, including instrument development, emission characterization, and air quality measurement. He leads projects on lithium-ion battery fire emissions, vehicle non-tailpipe emissions, and coal mine dust exposure reduction. He has developed key instruments like the nanoparticle aerodynamic lens for aerosol mass spectrometers and the DRI Portable Emissions Measurement System (PEMS). Wang has received prestigious awards including the 2020 AAAR Benjamin Y. H. Liu Award for aerosol instrumentation, the 2021 DRI Science Medal, and the 2024 NSHE Mid-Career Researcher Award. He serves on committees for the American Association for Aerosol Research (AAAR) and has authored over 100 peer-reviewed articles. His work spans fields like spacecraft fire safety, fugitive dust measurement, and computational fluid dynamics modeling. Key contributions include miner safety training programs reducing silica exposure and studies on brake/tire wear emissions near highways. His research integrates laboratory methods, field measurements, and numerical simulations to address environmental and occupational health challenges.

Bin XuView profile

Assistant Professor

• Reinforcement Learning
• Propulsion Electrification
• Data-Driven Modeling and Control
• +4 more

Bin Xu is an Assistant Professor in the Department of Aerospace & Mechanical Engineering at the University of Oklahoma (College of Engineering). His research focuses on Reinforcement Learning, Propulsion Electrification, Data-Driven Modeling, and Optimal Control, with applications in waste heat recovery systems and connected/automated vehicles. He earned a Ph.D. in Automotive Engineering from Clemson University (2017) and a B.S. from Hunan University, China (2013). Dr. Xu leads the MiLa laboratory and has secured grants totaling over $11.5 million as PI/Co-PI, including DOE-funded projects on electrified propulsion, shared autonomous vehicles in tribal communities, and methane sensing networks. He teaches courses like Dynamics and Control of Autonomous Driving (AME 5970) and IC Engine Lab (AME 4442). His recent work emphasizes reinforcement learning for energy management, autonomous vehicle path planning, and battery optimization. Notable publications address transient power optimization in Organic Rankine Cycle systems, machine learning for injection molding quality monitoring, and cybersecurity in hybrid buses.

Dong ZhangView profile

Assistant Professor

• Energy storage systems
• Dynamic systems and control
• Electrified transportation
• +7 more

Dong Zhang is an Assistant Professor in the Department of Aerospace & Mechanical Engineering at the University of Oklahoma's College of Engineering. He leads the Energy Systems and Controls Lab (ESCL), focusing on energy storage systems, dynamic systems control, electrified transportation, and data-driven decision-making. His research includes battery management systems, electrochemical modeling, optimal control, and machine learning applications in energy systems. Education: Ph.D. in Systems Engineering from UC Berkeley (2020), dual M.S. in Systems Engineering (UC Berkeley, 2016), and dual B.S. degrees in Electrical and Computer Engineering (Shanghai Jiao Tong University, 2015) and Civil and Environmental Engineering (University of Michigan, 2015). Research interests emphasize battery state estimation, thermal dynamics, and safety-enhanced charging strategies for electric vehicles. He has received awards including the ASME Energy System ACC Best Paper Award (2020) and served as an invited session chair at SIAM control conferences. Key contributions include real-time battery capacity estimation, adaptive observers for electrochemical models, and PDE-based thermal control frameworks. His lab develops solutions for heterogeneous battery pack management and cyber-physical system integration in energy systems.

Xing WuView profile

Associate Professor

• Transportation Systems
• Maritime Logistics
• Traffic Engineering
• +3 more

Xing Wu, Ph.D., P.E., is an Associate Professor in the Department of Civil and Environmental Engineering at Lamar University. His expertise spans transportation systems, maritime logistics, and infrastructure resilience. He has received notable awards, including the 2017 SWEET SIXTEEN from AASHTO and ASCE ExCEED Fellowship, and served as Principal Investigator (PI) for a TxDOT-funded project addressing traffic signal timing. His research integrates advanced data analysis techniques like AIS data and machine learning to address challenges in transportation efficiency, safety, and environmental impact. **Education**: B.S. in Civil Engineering, Tsinghua University (2002) M.S. in Management Science and Engineering, Tsinghua University (2005) M.S. in Civil Engineering/Construction Management, Carnegie Mellon University (2006) Ph.D. in Transportation System Analysis and Engineering, Northwestern University (2011) **Research Interests**: Focus on optimizing transportation networks, maritime channel operations, traffic signal control systems, and environmental resilience in infrastructure. His work leverages empirical data and computational models to improve safety and efficiency in both urban and coastal settings. **Key Contributions**: Over 50 publications since 2005, including studies on vessel behavior in narrow waterways, traffic crash severity analysis, and electric vehicle feasibility. His recent work emphasizes data-driven solutions for congestion mitigation and infrastructure resilience. **Awards**: Extensive recognition from academic and professional bodies, including multiple fellowships and university merit awards. **Grants & Advising**: PI on TxDOT projects and recipient of grants like the Larry Lawson Faculty Fellowship. Advises students in civil engineering disciplines, though specific advisee names are not listed.

Mahyar AmirgholyView profile

Assistant Professor

• Cooperative Control of Connected Automated Vehicles
• Transportation Electrification and Decarbonization
• Machine Learning and Reinforcement Learning
• +3 more

Dr. Mahyar Amirgholy is an Assistant Professor at Kennesaw State University (KSU) within the Southern Polytechnic College of Engineering and Engineering Technology, and a Research Affiliate at Lawrence Berkeley National Laboratory. His research focuses on transportation systems, with expertise in cooperative control of connected automated vehicles, transportation electrification, and machine learning applications. Education: Ph.D. in Civil and Environmental Engineering (Transportation Systems) from the University of Massachusetts Amherst Postdoctoral Scholar at Cornell University’s Civil and Environmental Engineering Department M.S. from Rutgers University (Transportation Engineering) and Sharif University of Technology (Transportation Planning) B.S. in Civil Engineering from Amirkabir University of Technology Research Interests: Dr. Amirgholy’s work emphasizes optimizing large-scale transportation systems through machine learning, network modeling, and energy efficiency strategies. Key areas include decarbonization of transportation networks, cooperative control of automated vehicles, and traffic flow optimization in signal-free urban environments. Publications: His recent work addresses challenges in mixed traffic management, signal-free networks, and energy-efficient vehicle operations. Themes include leveraging AI for dynamic traffic modeling and predicting emissions from EV charging infrastructure. Awards: Recipient of the CUTC Milton Pikarsky Award for his doctoral dissertation on transportation systems. Advising & Students: Supervises a diverse team of doctoral, master’s, and undergraduate researchers focusing on smart transportation systems. Notable projects include developing the Atlanta Energy and Emission Modeling and Analysis Tool (AEEMAT). Labs & Collaborations: Leads the Futra Lab, which collaborates with entities like the Atlanta Regional Commission to advance sustainable mobility solutions. Current initiatives include predictive energy modeling for EV adoption and equitable infrastructure design.

Sukumar KamalasadanView profile

Professor

• Smart Grid Design
• Renewable Energy Integration
• Power Grid Modernization
• +7 more

Dr. Sukumar Kamalasadan is a Professor and ECE Research Coordinator at the W.S. Lee College of Engineering, University of North Carolina at Charlotte. He holds a Ph.D. from the University of Toledo (2004), M.Eng. from Asian Institute of Technology (1999), and B.Tech. from University of Calicut (1991). His primary research focuses on smart grid design, renewable energy integration, power grid modernization, and real-time control systems. He has also been honored as a W.S. Lee College of Engineering Distinguished Scholar. Key research areas include microgrid management, optimal power flow (OPF) methodologies, inverter control technologies, and grid resilience strategies. His work addresses challenges such as distributed energy resource (DER) integration, voltage/frequency control in microgrids, and dynamic stability enhancement through advanced control architectures. Publications highlight innovations in OPF modeling (e.g., SOCP-based approaches), grid-forming/following inverter designs, and fault evaluation in active distribution networks. He has contributed to curricular updates in power engineering education, emphasizing situative pedagogy and concept map assessments. Grants and collaborations involve projects on decentralized grid operation, ancillary services via uninterruptible power supplies, and resilience evaluation of distribution systems against extreme weather events.

Pedro RODRIGUEZ-AYERBEView profile

Professor

• Model Predictive Control
• Optimization-Based Control Design
• Power Electronics Control
• +4 more

Pedro RODRIGUEZ-AYERBE is a Professor at CentraleSupélec, University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S). His expertise spans Model Predictive Control, optimization-based control design, and power electronics control. He holds a Ph.D. (2003) and Habilitation (2014) in Automatic Control from Université Paris-Sud. His research addresses constrained dynamical systems, human-robot interaction, and industrial automation. Education : Ph.D. in Automatic Control (2003), University Paris XI; HDR (2014), Université Paris-Sud. Industry Experience : Fagor Automation (Spain) and Lore (France) from 1996–2000. Research Interests : Focus on robust and constrained control, predictive control algorithms, and applications in robotics, energy systems, and manufacturing. His work bridges theoretical advancements with practical implementations in power electronics and industrial automation. Recent Work : Recent articles explore fragility of control laws, human arm impedance dynamics, and zinc-air battery modeling. His 2025 paper in IEEE Transactions on Automatic Control analyzes polyhedral partition fragility in PWA control systems. Student Supervision : Over 20+ PhD students supervised since 2003, including current advisees in robotics, control systems, and energy engineering. Notable collaborations with institutions like CEA-LIST and CIAMS. Key Projects : Active in interdisciplinary research, including zinc-air battery control (2024), human-robot interaction (2023), and advanced manufacturing precision (2021). His lab contributes to EU industrial and academic initiatives.

Elza BontempiView profile

Professor

• Environmental Chemistry
• Sustainable Materials Recycling
• Battery Technology
• +5 more

Elza Bontempi is a Full Professor of Fundamental Chemistry at the University of Brescia, affiliated with the Department of Chemistry within the College of Science. Her research focuses on environmental technologies, sustainable resource recovery, and circular economy strategies, particularly in battery recycling and phosphorus recovery. She leads major projects such as CARMEL (2025), funded by the Italian Government, and Tech4Lib (2023–2026), supported by Fondazione Cariplo, employing microwave-assisted processes to recover strategic metals from spent lithium-ion batteries. Elza has authored over 200 peer-reviewed papers and 200 conference presentations, with patents in environmental technologies. She was awarded the 2024 Intellectual Property Award (Climatech Context) for her innovation in battery recycling. Her work addresses global challenges like reducing reliance on mining and achieving zero greenhouse gas emissions by 2050. Key projects include the development of eco-friendly wastewater treatment systems (e.g., car wash effluent), CO2 sequestration via steel slag carbonation, and phosphorus extraction from poultry litter ash. She coordinates interdisciplinary teams and leverages AI and machine learning for material characterization and process optimization. Her research also extends to pandemic-related studies, including SARS-CoV-2 surveillance in wastewater and the environmental impact of pandemic-related waste (e.g., surgical masks). She advocates for sustainable industrial symbiosis strategies to minimize ecological footprints.

• Nanomechanics
• Materials Science
• Thin Films
• +4 more

Dr. James Paul Best is a Guest Group Leader at the Max-Planck-Institut für Eisenforschung GmbH (MPIE), leading the "Nano-/Micromechanics of Materials" group and interim leader of the "Thin Films and Nanostructured Materials" group. His research focuses on small-scale mechanics, nanomechanical testing, and the interplay between material chemistry and deformation at micro- and nanoscales. He holds a PhD from the University of Melbourne (2013) in Chemical Engineering and has held postdoctoral roles at EMPA (Switzerland) and UNSW Sydney (Australia). His work bridges fundamental materials science with applications in energy-efficient manufacturing, battery systems, and advanced alloy design. Key methodologies include in-situ scanning electron microscopy (SEM), atom probe tomography, and correlative microscopy. Research interests emphasize thin films, metallic glasses, intermetallic interfaces, and additive manufacturing. He explores how atomic-scale chemistry influences mechanical properties, particularly in materials like Laves phases and high-entropy alloys. Applications span aerospace, energy storage, and industrial forging tools. His interdisciplinary approach involves collaboration with MPIE colleagues to integrate mechanical testing with advanced structural analysis.

Riccardo FelicettiView profile

Research Fellow

• Fault Detection and Diagnosis
• Fault Tolerant Control
• Optimization
• +3 more

Riccardo Felicetti is a Research Fellow at the Department of Information Engineering, Università Politecnica delle Marche, within the Faculty of Engineering. He holds a Master's Degree (cum laude) in Computer and Automation Engineering (2016) and a Ph.D. (cum laude) in Information Engineering (2021) from the same university, with a thesis on “Active fault tolerant control for overactuated unmanned vehicles.” He also serves as Senior Tutor, coordinating tutor activities in the Faculty of Engineering. His research focuses on fault detection and diagnosis, fault-tolerant control, and optimization, with applications to unmanned vehicles (e.g., drones, ROVs) and energy management systems. He has contributed to advancements in systems like actuator fault tolerance, vibration-based fault detection, and energy optimization in smart grids. Key technical areas include disturbance observer-based control, adaptive Kalman filtering, and machine learning integration for system identification. His work bridges theoretical control frameworks with practical implementations in aerospace and marine robotics.

Thomas StatherosView profile

Assistant Professor

• Autonomous Systems
• Aerospace Engineering
• Avionics
• +4 more

Thomas Statheros is an Assistant Professor in Aerospace Engineering (Avionics, Autonomy, and Safety) at Coventry University, affiliated with the Research Centre for Future Transport and Cities (CFTC). He leads the Critical Autonomy and Safety Laboratory (CAUSLab) and contributes to British Standards Institution (BSI) standards for autonomy and safety. With over 23 years of experience in industrial and academic research, he specializes in Autonomous Systems, AI, Embedded Systems, and Avionics Design, collaborating with aerospace, automotive, and marine industries. He has designed safety-critical systems for projects like the LEMV airship and helicopter weather radar control panels, adhering to standards like DO-178B and ARP4754. His research focuses on Autonomous Systems Architecture, eVTOL aircraft, Avionics, and Safety-Critical Systems. Recent work includes developing efficient certification processes for aerospace and automotive industries, as well as designing verification/validation frameworks using simulators like Gazebo/Omniverse. He has led projects such as the EV-LIFT (Innovate UK), contributing £389K to Coventry University’s research on electric vehicle safety and performance. Key research trends in his articles include advancements in eVTOL design methodologies, battery health monitoring via optical sensors, and secure communication systems for non-terrestrial networks. His work bridges academic innovation with industrial application, emphasizing real-world testing and certification challenges. Teaching contributions include pioneering requirement-based verification in avionics modules and supervising over 35 undergraduate/postgraduate projects on autonomy, control systems, and safety-critical engineering. Collaborative efforts span industries and academic institutions, with notable partnerships in the UK, Europe, and the US. Current initiatives aim to address sustainability in aerospace and automotive sectors through hydrogen production and energy-efficient technologies.