Find Academic Supervisors

• Theoretical Chemistry
• Electrochemistry
• Catalysis
• +6 more

Christopher J. Stein is an Associate Professor of Theoretical Chemistry at the Technical University of Munich (TUM), part of the TUM School of Natural Sciences. His research focuses on theoretical (electro-)catalysis, developing electronic-structure models and solvation/embedding methods to understand and optimize catalytic processes. He leads the Stein Group, which integrates computational chemistry with high-throughput simulations to advance energy materials and battery technologies. His work emphasizes realistic modeling of catalyst behavior under operational conditions and has contributed to advancements in quantum embedding and automated reaction mechanism exploration. Education and Career: Earned his PhD in Theoretical Chemistry, with postdoctoral research at Caltech (2017-2020). Became an Associate Professor at TU Munich in 2023. He previously held roles at Karlsruhe Institute of Technology and contributed to projects like the BIG-MAP Materials Acceleration Platform. Research Interests: Theoretical chemistry, electrochemical interfaces, battery materials, high-throughput computational methods, and machine learning integration. His group explores topics like solid electrolyte interphases, charge transfer mechanisms, and automated workflows for materials discovery. Awards: While no explicit awards are listed, his contributions to materials acceleration platforms and theoretical catalysis have been widely recognized in the field. His work has been featured in journals like Journal of Chemical Physics , Chemical Science , and Angewandte Chemie . Labs/Teams: Leads the Stein Group at TUM, collaborating with institutions like the Munich Data Science Institute and MIRMI. His lab focuses on computational tools for accelerating energy material development, including quantum embedding and cloud-based simulations.

• Ergonomics
• Digital Human Modeling
• Manufacturing Systems
• +5 more

Lars Hanson is a Professor of Product Design Engineering at the University of Skövde's School of Engineering Science. His research focuses on ergonomics, digital human modeling, and optimizing manufacturing systems with a strong emphasis on human well-being and sustainable production. He leads projects like LITMUS (Industry 4.0 to 5.0 transition) and has contributed to developing tools such as IPS IMMA for ergonomic simulations. Active in virtual verification of human-robot collaboration and smart textile systems for workplace safety Published extensively in journals like International Journal of Human Factors Modelling and Simulation and IEEE Access Editor of conference proceedings and contributor to industry standards in automotive and healthcare sectors Research interests include multi-objective optimization of factory layouts, musculoskeletal risk assessment, and integrating ergonomic evaluations into product design processes. Current projects address Industry 5.0 sustainability challenges through digital twin technologies and smart manufacturing solutions.

Xi YuView profile

Lecturer

• Low Carbon Fuels
• Granulation and Tableting
• Computational Fluid Dynamics
• +3 more

Dr. Xi Yu is a Lecturer in Chemical Engineering at the University of Southampton, affiliated with the Faculty of Engineering and the Environment. He holds a Bachelor's from Tianjin University and a Ph.D. from the University of Sheffield. His research focuses on low carbon fuels, granulation techniques, and computational fluid dynamics. He has supervised PhD students such as Jerin Jacob and is currently accepting new PhD applicants in these areas. Dr. Yu's educational background includes degrees in Chemical Engineering and prior academic roles at Aston University and the Energy and Bioproducts Research Institute (EBRI). His work spans bioenergy systems, particle technology, and multi-physics modeling. Key research projects include advancements in biomass gasification, biofuel production, and sustainable energy systems. His publications emphasize computational modeling, fluid dynamics, and biomass utilization. Recent articles explore topics like absorption chiller systems, fluidization validation, and bio-oil aging strategies. He contributes to teaching modules such as CHEG3000 and CHEG3004, reflecting his commitment to both research and education.

Trym Vegard HaavardsholmView profile

Lecturer

• Computer Vision
• Machine Learning
• Robotics
• +3 more

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

Sergey FomelView profile

Professor

• Geophysics
• Seismic Data Analysis
• Computational Seismology
• +3 more

Sergey Fomel is a Professor of Geophysics at the University of Texas at Austin, holding the Wallace E. Pratt Professorship and serving as Director of the Texas Consortium for Computational Seismology (TCCS). He is affiliated with the Jackson School of Geosciences, Bureau of Economic Geology, and the Oden Institute for Computational Engineering and Sciences. His research focuses on seismic data analysis, computational seismology, and machine learning applications in geophysics. He leads the Madagascar software project for open-source geophysical data analysis. Dr. Fomel earned his Ph.D. in Geophysics from Stanford University in 2001. He has held leadership roles in the Society of Exploration Geophysicists (SEG), including Vice President, Publications (2017–2019) and Distinguished Lecturer (2020). His awards include honorary memberships in SEG and the Geophysical Society of Houston (GSH). Recent research emphasizes deep learning for seismic inversion, noise reduction, and fault segmentation. His work addresses challenges in geophysical data processing, including adaptive algorithms, wave propagation modeling, and CO2 monitoring. Fomel's contributions span both theoretical and applied domains, bridging computational methods with practical geoscience applications. Education: Ph.D. in Geophysics, Stanford University (2001) Affiliations: Jackson School of Geosciences, Bureau of Economic Geology, Oden Institute Labs/Teams: Texas Consortium for Computational Seismology (TCCS), Madagascar Project

• Intelligent Transportation Systems
• Automated Vehicles
• Control Systems
• +6 more

Lingxi Li is a Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University's Indianapolis campus. His research focuses on modeling complex systems, connected and automated vehicles, intelligent transportation systems, and parallel intelligence. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008), and master's and bachelor's degrees from the Chinese Academy of Sciences (2003) and Tsinghua University (2000). Research Interests: Dr. Li's work bridges control systems, transportation engineering, and AI, with emphasis on human-machine interaction, autonomous vehicle systems, and scenario-based traffic modeling. His projects include developing frameworks for Industry 5.0 collaboration, enhancing traffic flow prediction through parallel learning, and advancing safety in micro-mobility systems like e-scooters. Recent Publications: Over 15+ articles (2023-2025) explore topics such as game-theoretic vehicle interaction modeling, vision-language systems for autonomous driving, and acoustic SLAM technologies. These studies reflect a focus on real-world validation and system integration in smart transportation. Labs & Initiatives: Leads research in autonomous mining systems and scenario engineering for intelligent vehicles, leveraging parallel intelligence concepts. Collaborates on projects like ParallelWorkforce (Industry 5.0 frameworks) and SceNDD++ (naturalistic driving datasets).

Andrew ZhangView profile

Professor

• Wireless Communications
• Sensing Technologies
• Signal Processing
• +3 more

Prof. Andrew Zhang is a Professor at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). He leads the UTS Radio Sensing and Pattern Analysis (RaSPA) Lab and serves as Technical Director of the UTS-TPG Network Sensing Lab. His research focuses on integrated sensing and communications (ISAC), wireless signal processing, and autonomous vehicular networks. He holds a PhD from the Australian National University and has over 15 years of industry experience, including roles at CSIRO and ZTE Corp. Education: B.S. (Xi’an Jiaotong University), M.Sc. (Nanjing University of Posts and Telecommunications), Ph.D. (Australian National University). Research Interests: ISAC, radio sensing, machine learning for communications, and 6G waveform design. Key projects include developing perceptive mobile networks and flood/storm sensing via ISAC. Publications: Over 290 papers, 5 patents, and notable works on ISAC frameworks, joint communication-sensing systems, and mmWave technologies. Recent trends emphasize ISAC, 6G waveforms, and IoT integration with federated learning. Awards: CSIRO Chairman’s Medal, Australian Engineering Innovation Award, and multiple best paper awards. Active in IEEE leadership roles, including Editor-in-Chief of ISAC-Focus. Grants: ~$8M in research funding. Advises on ISAC-ETI initiatives and collaborates with industry partners like TPG Telecom. Labs: RaSPA Lab (radio sensing analytics) and UTS-TPG Lab (ISAC industrial solutions).

• Information Systems Methodologies
• Artificial Intelligence
• Agent Systems
• +8 more

Prof Ghassan Beydoun is a Professor and Head of Discipline (Information Systems) at the School of Computer Science, University of Technology Sydney (UTS). He leads the Information Systems discipline and is affiliated with the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). His research focuses on AI-driven systems, agent-based modelling, ontologies, and disaster management, with notable contributions to knowledge graphs, enterprise architecture, and IoT applications. Beydoun actively supervises Masters and PhD students in these domains. His research interests span metamodelling, agent systems, and AI applications in disaster management (e.g., flood, landslide, and earthquake risk assessment), health systems, and smart infrastructure. He has pioneered frameworks for reproducible machine learning solutions, digital identity systems, and cloud migration strategies. Beydoun’s work integrates interdisciplinary methods, such as bibliometric analysis for journal evolution and XAI for spatial hazard prediction. Recent publications highlight his expertise in AI for climate-induced hazard modelling, agent-based knowledge transfer mechanisms, and metaverse applications in education. His funded projects include AI-powered circular economy initiatives, smart beach safety systems, and health data querying frameworks. Beydoun collaborates with industry partners like CSIRO, Capsicum Business Architects, and Data Zoo, translating research into practical solutions for enterprise architecture, cybersecurity, and public health.

Cathy WuView profile

Associate Professor

• Reinforcement Learning
• Machine Learning
• Large-scale Optimization
• +6 more

Cathy Wu is the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering at MIT, affiliated with the Institute for Data, Systems, and Society (IDSS). Her research bridges machine learning, optimization, and urban systems, with a focus on mixed autonomy systems in mobility. She holds degrees from MIT (B.S., M.Eng in EECS) and a Ph.D. from UC Berkeley (EECS). Education: B.S. and M.Eng in Electrical Engineering and Computer Science, MIT (2012-2013) Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2018) Research Interests: Reinforcement Learning and Machine Learning Large-scale Optimization and Control Theory Mobility Systems and Urban Infrastructure Implications of AI and Automation Her work emphasizes interdisciplinary collaboration, involving transportation, computer science, and public policy. She founded the Interdisciplinary Research Initiative within the ACM Future of Computing Academy to advance cross-disciplinary computing research. Key Projects: Includes Flow (open-source RL framework for traffic control), eco-driving incentive mechanisms, and mixed autonomy traffic optimization. Her articles address congestion mitigation, autonomous vehicle integration, and scalable supervision strategies. Awards: Recipient of fellowships, best paper awards, and teaching honors (specific names unlisted). Engagement: Collaborations with institutions like Microsoft Research, OpenAI, and Caltrans. Active in policy-oriented initiatives and education through IDSS programs.

• Agricultural Economics
• Food Policy
• Policy Analysis
• +3 more

Prof. Zoltán Károly Lakner is a Professor at the Hungarian University of Agriculture and Life Sciences, affiliated with the Department of Agricultural and Food Economics. His research focuses on applying dynamic systems theory, econometrics, and artificial intelligence to analyze food supply networks, agricultural policy, and socio-economic systems in Africa and Central Asia. Key areas include big-data modeling of complex systems, game-theory applications, and open-source intelligence tools in innovation policy. He leads the SafeConsume H2020 project, developing strategies to mitigate foodborne illness risks through consumer education and prototype tools. His work spans food policy analysis, climate change impacts on agriculture, and ethical food consumption behaviors. Education details not explicitly provided in text. Research interests emphasize quantitative methods in agricultural economics, econometrics-driven policy analysis, and interdisciplinary approaches to global food challenges. Recent publications explore bioenergy expansion effects, water tax policy implications, and post-Paris Agreement green finance trends. No scientific awards explicitly stated in the text. Grants include the Horizon 2020 SafeConsume project. Active in advising and mentoring, though specific student names are not listed. Affiliated with the Szént István Campus in Gödöllő, contributing to institutional research and policy initiatives.

Israel OrimoloyeView profile

Lecturer

• Remote Sensing
• GIS Applications
• Drought Disaster Management
• +3 more

Dr. Israel Orimoloye is a Lecturer in the School of Environment, Geography, and Sustainability at Western Michigan University. He holds dual Ph.D. degrees in Disaster Management (University of the Free State, 2022) and Geography (University of Fort Hare, 2018). His research focuses on remote sensing, GIS applications, climate science, and disaster risk management. He has authored over 65 publications and contributed to global policy frameworks through collaborations with institutions in South Africa, Canada, India, and beyond. Education: Ph.D., Disaster Management, University of the Free State (2022) Ph.D., Geography, University of Fort Hare (2018) MTech., Meteorology, Federal University of Technology (2015) BTech., Meteorology, Federal University of Technology (2010) Research Interests: His work integrates Earth Observation (EO) data and climate models to address drought, land degradation, and environmental hazards. Key areas include: Remote sensing for disaster monitoring GIS-driven climate impact assessment Sustainable resource management Community resilience strategies Publications: His 15 most recent articles (2020–2025) emphasize drought assessment, climate adaptation, and participatory risk management. Topics span geospatial modeling, flood dynamics, and policy integration. Grants & Advising: He leads projects funded by the Central Research Fund and collaborates internationally. No listed advisees are currently available. Labs & Teams: Affiliated with WMU's environmental research initiatives, focusing on spatial data science and disaster mitigation.

Ray BaiView profile

Assistant Professor

• Bayesian Statistics
• High-Dimensional Modeling
• Scalable Machine Learning
• +7 more

Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina (USC), part of the McCausland College of Arts and Sciences. Effective August 2025, he will join the George Mason University (GMU) Department of Statistics as a faculty member. His research focuses on Bayesian statistics, deep learning, and causal inference, with applications to biomedical and public health challenges such as genomic studies, drug repositioning, and electronic health records analysis. Bai holds a PhD in Statistics from the University of Florida (2018), an MS in Applied Mathematics from the University of Massachusetts Amherst, and a BA from Cornell University. His work has been supported by the National Science Foundation (NSF). Education: PhD in Statistics, University of Florida (2018) MS in Applied Mathematics, University of Massachusetts Amherst BA, Cornell University Research interests include scalable algorithms for high-dimensional data, nonconvex optimization, and distributed inference methodologies. His work bridges statistical theory with practical applications in healthcare, emphasizing robustness and computational efficiency. Recent contributions address challenges in single-index models for skewed data, generative quantile regression, and Bayesian varying-coefficient models. Advising includes supervising PhD students Zile Zhao and Shijie Wang, who have contributed to survival analysis and deep learning frameworks. Future openings for students at GMU focus on Bayesian methodology and machine learning. Labs/Teams: Collaborates on projects involving interdisciplinary teams in biostatistics and computational biology.

Tao ShuView profile

Associate Professor

• Cybersecurity
• Wireless Communication
• Federated Learning
• +3 more

Dr. Tao Shu is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University. His research focuses on cybersecurity, wireless communication systems, federated learning, and IoT applications. He holds a Ph.D. in Electrical and Computer Engineering from the University of Arizona, and M.S. and B.S. degrees in Electronic Engineering from South China University of Technology. Dr. Shu's work emphasizes secure communication and distributed learning systems, including projects funded by the NSF such as a novel method to prevent cyberattacks on Low Earth Orbit (LEO) satellites. He has been recognized for academic excellence, including being named to Auburn University’s 2020 promotion and tenure list. His research interests span cybersecurity mechanisms for autonomous vehicles, privacy-preserving federated learning, and resource allocation in metaverse environments. He explores innovative solutions for sensor spoofing detection, adversarial machine learning, and energy-efficient IoT systems. Dr. Shu is affiliated with Auburn’s Center for Artificial Intelligence and Cybersecurity Engineering and actively contributes to interdisciplinary projects. His publications reflect a strong focus on practical applications of theoretical advancements in wireless systems and secure data transmission.

Tingkai WangView profile

Senior Lecturer

• Mobile Robots
• Intelligent Systems
• Artificial Intelligence (AI)
• +5 more

Dr. Tingkai Wang is a Senior Lecturer in the School of Computing and Digital Media at London Metropolitan University. His research focuses on mobile robots, intelligent systems, artificial intelligence, control systems, image/signal processing, and virtual reality. He teaches the Programming for Computer Science module and has led projects like the Virtual Environment and Simulation System (2000-2002) and Navigation and Control of Mobile Robots (1995-1998). His work emphasizes interdisciplinary approaches, combining expert systems, neural networks, and fuzzy logic to address challenges in autonomous systems. Notable contributions include AGV navigation algorithms, hybrid control systems, and predictive modeling. Over 30 publications span robotics, control engineering, and AI applications. He collaborates internationally and has presented at venues like the International Conference on Intelligent Systems Engineering and the IEEE Conference on Engineering in Medicine and Biology. Dr. Wang’s expertise bridges theoretical modeling and practical implementation, with applications in manufacturing automation, environmental monitoring, and industrial management systems. His current research continues exploring adaptive control mechanisms and AI-driven robotics solutions.

Anastasios P. VassilopoulosView profile

Adjunct Professor

• Composite Materials
• Offshore Renewables
• Wind Turbine Technology
• +5 more

Anastasios P. Vassilopoulos is an Adjunct Professor and Head of the Composite Mechanics Group (GR-MEC) at the École Polytechnique Fédérale de Lausanne (EPFL) within the School of Architecture, Civil and Environmental Engineering (ENAC). He holds additional roles as Director of the Doctoral Program in Civil and Environmental Engineering (EDCE) and is affiliated with multiple research groups and committees at EPFL. His expertise spans composite materials, structural adhesives, and offshore renewable energy systems. Education: PhD in Mechanical Engineering, University of Patras, Greece (2001) Dipl. Mechanical Engineer, University of Patras, Greece (1995) Erasmus Exchange, University of Bristol, UK (1994–1995) Research Interests: Offshore renewables, particularly wind turbine blade materials and joints Fatigue analysis of composite materials and adhesive joints Experimental and analytical methods for composite structures Design of composite constructions and structural integrity His work emphasizes practical applications in engineering, including wind energy systems and fiber-reinforced polymer (FRP) composites. Teaching & Labs: Teaches courses on structural mechanics, advanced composites, and floating offshore renewables Leads the GR-MEC lab, focusing on composite mechanics and joint behavior Supervised over 15 PhD students at EPFL Grants & Projects: Swiss National Science Foundation-funded projects on bonded composite structures and wind turbine blade materials Research on fire-resistant composite bridge decks and lightning effects on composite structures