Find Academic Supervisors

• Transportation Engineering
• Location Optimization
• Geospatial Information Science
• +4 more

Tae J. Kwon is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta . His research spans Winter Transportation Engineering , Intelligent Transportation Systems (ITS) , Traffic Optimization , and Geospatial Information Science . Developed LoRWIS , a web-based decision support system for road weather sensor optimization Recipient of multiple awards including ITS Canada Excellence in Research and Development Award (2024) , KOFST Scientist of the Year (2024) , and Early Career Research Award (University of Alberta, 3 times) Active in professional committees like the TRB Surface Transportation Weather Research Group (2016–present) Research Highlights : Winter Road Maintenance : Advanced geostatistical and deep learning techniques for road condition monitoring Location Optimization : Pioneered models for RWIS sensor placement using hybrid geostatistical methods Intelligent Transportation Systems : Focused on big data applications for traffic safety and connected vehicles Climate Resilience : Integrated ecological vulnerability into fire hazard modeling Scientific Awards : ITS Canada Excellence in Research (2024) KOFST Scientist of the Year (2024) Donald Stanley Best Paper Award (2023) AKCSE Early Achievement Award (2021) University of Alberta Early Career Research Award (2020) Great Supervisor Award (2019) Teaching & Leadership : Teaches courses like CIV E 419 (Transportation Engineering: Highway Planning and Design) and CIV E 612 (Transportation Planning: Methodology and Techniques) Leads the Geospatial Transportation System & Sensing Solutions Lab (GeoTrans) , mentoring PhD students Juan Cuellar and Jiehao Bi Collaborates with agencies including Alberta Transportation , NSERC , and Iowa DOT

Cathryn BirchView profile

Professor

• Tropical Meteorology
• Weather and Climate Extremes
• Convection-Permitting Models
• +5 more

Professor Cathryn Birch is a leading academic in Meteorology and Climate at the University of Leeds' School of Earth and Environment. She holds a Professorship specializing in high-impact weather systems and climate modeling, with extensive collaborations across international meteorological services including the Indonesian Met Service (BMKG) and the UK Met Office. Her research focuses on tropical meteorology , particularly thunderstorm formation and extreme rainfall mechanisms in Southeast Asia. She employs convection-permitting models , satellite observations, and machine learning techniques to develop nowcasting systems that predict severe weather 2-3 hours in advance. Key research areas include: Weather and climate extremes in tropical regions Flood forecasting and early warning system development Climate impacts on health (particularly humid heat extremes) Machine learning applications for weather prediction Her recent publications demonstrate strong trends in applied meteorology with emphasis on real-world implementation - 60% of her 2023-2025 work involves operational forecasting systems, while 40% focuses on climate-health linkages. Notable methodological innovations include satellite-based humid heat early warning systems and deep learning frameworks for convection initiation. Major scientific recognition includes: 2024 Emerging Environmental Impact Award for flood early warning systems 2021 Queen's Anniversary Prize for tropical community resilience 2014 European Meteorological Society Young Scientist Award Professor Birch actively supervises 6 PhD students and 3 postdocs while leading multi-million pound projects including the £6M National Hub on Net Zero, Health and Extreme Heat (HEARTH). Her team develops practical forecasting tools currently being tested with African meteorological services and the Indonesian Met Service. She also serves on the European Meteorological Society Awards Committee and the Met Office K-scale project steering group.

Steven Y. LiangView profile

Professor

• precision manufacturing
• additive manufacturing
• materials science
• +3 more

Steven Y. Liang , Regents' Professor at the Georgia Institute of Technology 's Woodruff School of Mechanical Engineering, focuses on precision manufacturing , additive manufacturing , and materials-driven process optimization . His research program bridges materials science and computational mechanics to develop predictive models for advanced manufacturing systems. Ph.D., University of California, Berkeley (1987) M.S., Michigan State University (1984) B.S., National Cheng-Kung University, Taiwan (1980) Dr. Liang's work emphasizes physics-based modeling of thermal-mechanical interactions in machining and additive manufacturing, particularly for Ti6Al4V and Inconel 718 alloys. Recent publications highlight tool wear prediction , laser-assisted micro-milling , and residual stress modeling using machine learning and analytical mechanics. His research has been recognized with the ASME Milton C. Shaw Manufacturing Research Medal (2016) , SME Gold Medal (2021) , and Outstanding Lifetime Service Award of NAMRI/SME (2021) , among others. Funded by federal agencies and aerospace/automotive industries, his work provides scientific foundations for process planning and optimization.

Sean WilsonView profile

Researcher

• Remotely-accessible robotic hardware
• Collaborative autonomy
• Swarm robotics
• +5 more

Sean Wilson is a Researcher at the Georgia Institute of Technology , affiliated with the College of Engineering and the School of Electrical and Computer Engineering . He serves as the Collaborative Autonomy Branch Chief at the Georgia Tech Research Institute (GTRI) and Director of the Robotarium Lab (https://www.robotarium.gatech.edu/), which provides free remote access to robotic hardware for algorithm testing. Educational Background: B.A. in Physics and Mathematics from State University of New York at Geneseo (2012) M.S. and Ph.D. in Mechanical Engineering from Arizona State University (2017) Dr. Wilson's research focuses on remotely-accessible robotic hardware , collaborative autonomy , and control of multi-agent and swarm robotic systems . His recent publications emphasize distributed control, swarm robotics, and bio-inspired robotic behaviors. The Robotarium Lab he directs enables global access to robotics testbeds for control research. Research Themes (2014-2023): Remote-access robotics (5), swarm coordination (7), bio-inspired algorithms (3), barrier functions (2), multi-robot systems (9), and control theory (4). Sean operates from the Robotarium Lab (Office Location: CCRF B11-3133D) as part of Georgia Tech's Institute for Robotics and Intelligent Machines (IRI) core faculty. His work bridges robotics infrastructure development with theoretical control research.

Rainer WallnyView profile

Professor

• High-Energy Physics
• Particle Physics
• Higgs Physics
• +3 more

Prof. Rainer Wallny is a Full Professor of Physics at ETH Zurich and Head of the Institute for Particle Physics and Astrophysics. His research focuses on high-energy particle physics, particularly through the CMS experiment at the Large Hadron Collider (LHC), emphasizing Higgs boson studies and detector upgrades. He leads projects on Higgs boson characterization in photon and b-quark final states, as well as CMS pixel detector upgrades for Phase-2. His group also explores future collider technologies and contributes to teaching at all academic levels. Education: Studied Physics at Universities of Tübingen, Washington (M.Sc., 1994), and Heidelberg (Diplom, 1996) PhD in Physics from University of Zurich CERN Research Fellow (2001–2003) Faculty at UCLA (2003–2010), promoted to Full Professor in 2010 Joined ETH Zurich as Full Professor in 2010 Research Interests: Higgs boson properties and decay channels Supersymmetry searches in CMS data Detector development for CMS (pixel trackers, diamond sensors) Phase-2 LHC upgrade technologies Experimental particle physics at high-luminosity colliders Grants and Advising: Supervised over 20 PhD students since 2010 Leadership roles in CMS collaboration and detector R&D initiatives Active in curriculum design for physics education at ETH Labs & Teams: Wallny Group at ETH Zurich Institute for Particle Physics and Astrophysics (D-PHYS) Collaborations with CERN and global CMS teams

Tony JanView profile

Professor

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

Professor Tony Jan leads the Centre for Artificial Intelligence Research and Optimisation (AIRO) at Torrens University Australia's Design and Creative Technology school. He holds a PhD in Computing Science from the University of Technology Sydney (2004) and a Bachelor of Engineering from the University of Western Australia (1999). His research focuses on federated machine learning for IoT security, ensembled machine learning for real-time applications, cognitive machines for human-centric computing, and smart sensor networks for healthcare and security. He has secured ARC grants and industry partnerships with NVIDIA, IBM, and Microsoft. Awards include the 2024 SEI Global Academic Excellence Award and the 2023 Torrens University Excellence Award. Research collaborations span global partners, with contributions to UN Sustainable Development Goals in education and industry. His work bridges academia and industry, expanding AI program enrollments by 2,000+ students and enhancing student satisfaction by 15%. He advises PhD students on topics like IIoT cybersecurity and smart cities, and has produced over 97 publications since 1999. Education: PhD (UTS, 2004), BEng (UWA, 1999) Research Themes: AI for Industry 5.0, Cybersecurity, Smart Cities, Healthcare Technology Key Partnerships: NVIDIA, CIMIC, Palo Alto Networks Recent Projects: Federated learning for health IoT, drone vision intelligence, ransomware detection His work emphasizes ethical AI adoption in design and healthcare, with publications exploring AI ethics, generative AI applications, and sustainable technology integration.

• Multi-agent Systems
• Sustainability
• Intelligent Transport Systems
• +3 more

Dr. Fatemeh Golpayegani is an Assistant Professor at the School of Computer Science, University College Dublin. She leads the Multi-agent Systems and Sustainable Solutions lab (MAS3.ucd.ie) and has secured over €1.5M in research grants. Her academic roles include BSc Stage 4 Coordinator and Chair of Women@CS (2012–2023). She holds a PhD from Trinity College Dublin (2018) and professional qualifications in university teaching from UCD. Education: PhD in Computer Science, Trinity College Dublin (2018) Professional Diploma in University Teaching & Learning, University College Dublin (2024) Professional Certificate in University Teaching & Learning, University College Dublin (2023) Research Interests: Focuses on multi-agent systems, sustainability, intelligent transport systems, autonomous decision-making, and edge computing. Her work integrates reinforcement learning, ontology-based models, and adaptive systems to address challenges in smart cities, energy grids, and infrastructure monitoring. Grants & Projects: Principal Investigator for the EU-funded RE-ROUTE project (€multi-million, 2023–2026) on intelligent transport networks. Co-Principal Investigator for the Augmented CCAM project on connected/cooperative autonomous mobility. Funded investigator in SFI centres (I-Form, CONNECT, Biorbic). Awards & Recognition: Researcher of the Year Award (2022) Member of Young Academy of Ireland (2023) Teaching & Mentoring: Coordinates modules in algorithms, Java programming, and operating systems. Supervises PhD students in SFI centres and mentors postdoctoral researchers. Active in promoting EDI as Chair of d-real doctoral training centre. Labs & Collaborations: Leads the MAS3 lab, collaborating on projects like CAPTAIN CARBON (sustainable transport gamification) and ontology-enhanced traffic signal control systems.

Prashant SankaranView profile

Assistant Professor

• Artificial Intelligence
• Operations Research
• Healthcare Optimization
• +3 more

Prashant Sankaran is an Assistant Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (School of Engineering and Applied Sciences). He holds a PhD in Mechanical & Industrial Engineering from Rochester Institute of Technology (2023), an MS in Industrial & Systems Engineering from RIT (2020), and a BTech in Mechanical Engineering from Sharda University (2014). His research focuses on artificial intelligence for reasoning under uncertainty, explainable AI, and applications in healthcare, energy management, transportation, and space exploration. He integrates operations research and AI techniques to address complex optimization challenges. Research interests include computational design of bioelectronic materials, kidney exchange optimization via graph machine learning, and solving NP-hard combinatorial problems with hybrid learning-optimization frameworks. His work bridges theoretical advancements (e.g., genetic algorithms) with real-world applications like autonomous logistics systems and renewable energy management. No scientific awards have been mentioned. While no advisees are listed, his publications reflect collaborations across AI, robotics, and healthcare sectors. His research spans topics from deep reinforcement learning in warehouse automation to synthetic data generation for transplant systems.

Holly YancoView profile

Professor

• Human-Robot Interaction
• Robotics
• Assistive Technology
• +7 more

Holly A. Yanco, Ph.D. , is a Professor of Computer Science at the University of Massachusetts Lowell (UML), where she also serves as the Distinguished University Professor and Director of the New England Robotics Validation and Experimentation (NERVE) Center . She founded the Human-Robot Interaction (HRI) Laboratory at UML in 2001, fostering interdisciplinary research in robotics, assistive technology, and artificial intelligence. Education: Dr. Yanco earned her B.A. in Computer Science from Wellesley College , followed by an M.S. and Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) . Research Interests: Her work spans human-robot interaction , robotics education , assistive technologies , urban search and rescue (USAR) , and multi-modal interfaces . She has led collaborative initiatives such as Artbotics , integrating art and robotics in K-12 and college curricula, and Pyro , a Python-based robotics platform recognized as Premier Courseware of 2005 . Grants & Funding: Her research has been supported by the National Science Foundation (NSF) , U.S. Army Research Office , Microsoft Research , and National Institute of Standards and Technology (NIST) . Notable grants include an NSF CAREER Award (2005) and leadership roles in DARPA’s Fast Lightweight Autonomy (FLA) program . Awards & Recognition: Dr. Yanco has received teaching awards from UMass Lowell and MIT , served as General Chair of the 2012 ACM/IEEE International Conference on Human-Robot Interaction , and held leadership roles in AAAI’s Executive Council (2006-2009) . Lab & Outreach: The HRI Lab engages students from high school to Ph.D. levels, emphasizing K-12 outreach through Botball tournaments , STREAM teacher workshops , and the Artbotics program in collaboration with The Revolving Museum .

• Model-Based Design
• Design Automation
• Optimization
• +4 more

Marc C.W. Geilen is an Associate Professor at the Electronic Systems group , Eindhoven University of Technology. He leads the Model-Based Design Lab and contributes to the CompSOC Lab and High Tech Systems Center . His work focuses on model-based design methods, design automation, and optimization for real-time and embedded systems. Research Keywords: Cyber-Physical Systems, Real-Time Systems, Embedded Systems, Performance Analysis, Design Automation Key Collaborations: EU ECSEL TRANSACT project, SAM-FMS project, Arrowhead Tools initiative His recent publications address weakly-hard timing constraints in server-based systems, hybrid performance modeling for cyber-physical systems, and neural network optimization for communication. Article trends span Real-Time Scheduling , Trustworthy Modeling , Neural Network Efficiency , and Resource Allocation in distributed environments. Scientific Awards : Partial-Order Reduction for Performance Analysis (2018) Teaching activities include courses in Computational Modeling , Embedded Signal Processing , and Discrete Mathematics . He collaborates across projects like TRANSACT, SAM-FMS, and Arrowhead Tools, focusing on flexible manufacturing and cloud-to-edge transitions.

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +5 more

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

Hongkai WenView profile

Professor

• Machine Learning Systems
• Artificial Intelligence
• Data Science
• +5 more

Hongkai Wen is a Professor (Chair in Machine Learning Systems) in the Department of Computer Science at the University of Warwick, UK. He holds dual appointments as a Fellow of the Alan Turing Institute (serving as Independent Scientific Advisor for BridgeAI and member of Turing Research Ethics team) and previously worked as Senior Research Scientist at Samsung AI Centre Cambridge and postdoctoral researcher at Oxford University. Education: Computer Science, Keble College, University of Oxford Research Focus: Develops intelligent multi-modal perception systems for real-world deployment with extreme computational efficiency. Core expertise spans ML systems optimization, neural architecture search, and cross-disciplinary applications in robotics, urban mobility, and wearable/IoT security. Pioneered event-based vision techniques and training-free NAS frameworks. Publication Trends: Recent work (2023-2025) demonstrates accelerating innovation in diffusion model efficiency, on-device AI deployment, and sensor fusion techniques. Dominant themes include computational resource optimization for edge devices, multi-modal temporal modeling, and privacy-preserving spatial analytics, with significant contributions to NeurIPS, ICML, and CVPR venues. Scientific Recognition: Best Paper Award, AutoML Conf 2023 (T-CET) Best Paper Runner-up, SenSys 2024 (AdaFlow) Best Paper Awards: IPSN 2014 & EWSN 2013 1st/2nd Place, Zero Cost NAS Competition (AutoML'22) Mentorship & Funding: Actively supervises PhD candidates through thesis committees at Warwick, Ulster, and Queensland universities. Secured National AI Strategy Fund for Macro Neural Architecture Search research. Recruits annually for PhD positions with scholarships from UKRI, Turing Institute, and industry partnerships. Research Leadership: Heads the AI/ML Systems (AMS) Division at Warwick, directing a 15+ member team developing deployable ML frameworks for mobile/robotic platforms. Maintains active collaborations with Samsung AI Centre and Turing Institute's BridgeAI programme on ethical AI deployment.

Yue LiView profile

Professor

• Resilient Civil Infrastructure
• Structural Reliability
• Risk Assessment
• +3 more

Yue Li is the Leonard Case Jr. Professor in the Department of Civil and Environmental Engineering at Case Western Reserve University. He specializes in resilient and sustainable infrastructure systems, focusing on structural reliability, probabilistic design, and climate change adaptation. His research addresses risk assessment for infrastructure under extreme events, including earthquakes, hurricanes, and climate impacts. Education: PhD in Civil Engineering, Georgia Institute of Technology, 2005 Research Interests: Dr. Li’s work integrates advanced statistical methods and data-driven approaches to enhance infrastructure resilience. Key areas include: Probabilistic modeling of structural systems Risk-informed decision-making for multi-hazard mitigation Climate change impacts on material durability and performance Asset management and lifecycle cost analysis Notable Contributions: His recent publications emphasize data-driven resilience metrics for water systems and seismic risk assessment for bridges. He has pioneered frameworks for evaluating infrastructure vulnerability under climate change, including corrosion effects and extreme weather adaptation. Awards: ABSE Outstanding Paper Award (2023) Case School of Engineering Teaching Award (2020) Nomination for John S. Diekhoff Award (2019) Leadership Roles: Dr. Li serves as Section Editor for the ASCE Journal of Structural Engineering and chairs multiple technical committees on safety and reliability. He leads initiatives to standardize multi-hazard design practices and resilience evaluation methodologies.

• Artificial intelligence
• Data compression
• Deep learning
• +15 more

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +7 more

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.