Find Academic Supervisors

Wooram ParkView profile

Associate Professor

• Robotics
• Biomedical Robotics
• Computational Structural Biology
• +3 more

Wooram Park is an Associate Professor in the Department of Mechanical Engineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He leads the Robotics and Intelligent Systems Laboratory (ROBINS Lab) and holds a PhD from Johns Hopkins University (2008), along with MS and BS degrees from Seoul National University (2003 and 1999). His research focuses on robotics, biomedical robotics, computational structural biology, and image processing. Key projects include flexible needle steering for medical applications, haptic feedback systems, and advanced algorithms for motion planning and image reconstruction. He has received notable awards such as the Creel Fellowship (2007) and Critics’ Choice Award in ArtBot Design (2004). His work spans theoretical contributions in stochastic systems and practical innovations like vibratory magnetic robots (Vimbot) and wearable haptic devices. The ROBINS Lab emphasizes interdisciplinary research at the intersection of mechanical engineering, computer science, and biomedical applications.

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.

• Construction Safety and Risk Management
• Project Risk Assessment
• Railway and Highway Safety
• +3 more

Professor Min An is a Professor of Construction and Risk Management at the University of Salford, leading the Infrastructure Research Group within the School of Science, Engineering & Environment. He holds an honorary professorship at two overseas universities (China and Portugal) and serves on the editorial boards of 12 international journals. With over 40 years of experience, his career spans academic roles at Heriot-Watt University, Coventry University, and the University of Birmingham, alongside industry roles as a civil engineer and researcher. His research focuses on safety and risk management in construction, transportation systems, and energy sectors, with over 200 publications. Key areas include railway and highway safety, offshore oil & gas risk assessment, and nuclear reliability management. He has secured funding from EPSRC, EU, DfT, and industry partners, leading 20+ projects. Notable achievements include developing methodologies for infrastructure safety and maintaining collaborations with 30+ industrial partners. Professor An has supervised 30 PhD students and over 280 postgraduate projects, contributing to industry workshops and best practices. Awards include multiple science technology prizes and conference best paper/keynote recognitions. His teaching spans risk management, construction safety, and project management across MSc programs.

Brian SurgenorView profile

Professor

• Mechanical Engineering
• Machine Vision Systems
• Autonomous Vehicles
• +3 more

Professor Brian Surgenor is a faculty member at Queen's University's Department of Mechanical and Materials Engineering, part of the Smith Engineering faculty. He holds a B.Sc. (1977), M.Eng. (AECL/Whiteshell), and Ph.D. (1983) in Mechanical Engineering from Queen's University. His research focuses on machine vision systems for automation, autonomous vehicle navigation, and mechatronic system design education. He has held key administrative roles including Department Head (1993-2002), Associate Dean (2008-2013), and Vice-Dean (2013-2016). His work emphasizes interdisciplinary innovation, such as the Mitchell Hall design project and contributions to Ingenuity Labs. Education: B.Sc. Mechanical Engineering, Queen's University (1977) M.Eng. Engineering Physics, McMaster University (AECL/Whiteshell) Ph.D. Mechanical Engineering, Queen's University (1983) Research interests include: - Pneumatic servosystems - Intelligent algorithms for machine vision - Off-road autonomous vehicle systems - Mechatronics education methodologies - Hybrid powertrain systems for vehicles His recent publications (2017–2024) explore autonomous systems, machine vision applications, and fuel cell hybrid technologies. Notable trends include advancements in UAV-based infrastructure inspection, terrain-adaptive autonomous driving, and low-cost machine vision solutions for small part sorting. His work bridges theoretical control systems with practical industrial automation challenges. He has contributed to laboratory design for CDIO curricula and pioneered mechatronics education through problem-based learning. His administrative leadership has shaped Queen's engineering graduate programs and research infrastructure. Currently involved in Ingenuity Labs, fostering cross-disciplinary innovation.

Chengzong PangView profile

Associate Professor

• Power Systems
• Electrical Engineering
• Control Systems
• +7 more

Chengzong Pang is an Associate Professor and MSECE Graduate Coordinator at the Department of Electrical and Computer Engineering, College of Engineering, Wichita State University. His work focuses on power systems, electrical engineering innovations, and renewable energy integration. He specializes in transient stability analysis, control systems, and smart grid technologies. Research Interests: Dr. Pang's expertise includes advanced control strategies for power electronics (e.g., PMSM, UPQC), machine learning applications for grid stability (LSTM/SVM), and energy storage solutions for renewable integration. His research also addresses challenges in microgrid operation, subsynchronous oscillation mitigation, and battery storage systems. Key Trends in Publications: Over 20 years of publications (2002–2022) emphasize: (1) Machine learning for power system analysis, (2) Control system design for renewable integration, (3) Grid stability enhancement via advanced algorithms, and (4) Smart grid infrastructure optimization. Recent works (2021–2022) highlight transient stability prediction and ANFIS-based power quality solutions. Labs/Teams: Active in energy systems research groups focusing on renewable integration and grid modernization, though specific lab names are not explicitly stated in the provided texts.

İlhan KocaarslanView profile

Professor

• Control Systems Engineering
• Load Frequency Control
• Adaptive Control
• +12 more

İlhan Kocaarslan is a Professor in the Department of Control and Automation Engineering at Istanbul Technical University (ITU), Faculty of Electrical and Electronic Engineering. His research focuses on advanced control systems, energy conversion, and sustainable transportation technologies. Research Interests: His work spans Load Frequency Control , Adaptive and Fuzzy Logic Control , PI Controllers , Electric Vehicle energy modeling , Battery and Fuel Cell systems , and Smart Grid integration . He actively contributes to innovations in renewable energy and electromobility. Publication Trends: Recent publications emphasize high-fidelity energy modeling for electric vehicles, optimization of battery systems, control strategies for fuel cells using fuzzy and Lyapunov-based PI controllers, and electromagnetic design for next-generation transportation like Hyperloop. His work bridges theoretical control design with practical energy applications. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He supervises 25 ongoing theses, indicating active mentorship. He is Principal Investigator (PI) on multiple research projects funded by ITU’s BAP and TTO programs, including initiatives on blockchain-based energy systems, hydrogen technologies, electric mobility, and cybersecurity-integrated sustainability platforms. Labs and Teams: While specific lab names are not mentioned, his role as PI on diverse energy and control projects suggests leadership in a multidisciplinary research group focused on smart energy systems, power electronics, and sustainable transportation at ITU.

Miguel Rico-RamirezView profile

Associate Professor

• Radar Hydrology
• Hydroinformatics
• Flood Forecasting
• +3 more

Dr. Miguel Rico-Ramirez serves as Associate Professor of Radar Hydrology and Hydroinformatics at the University of Bristol's School of Civil, Aerospace and Design Engineering. His research integrates advanced radar technology with hydrological modeling to address critical water resource challenges including flood forecasting, drought management, and precipitation measurement across diverse global contexts from South Korea to Mexico City. Education: Bachelor of Engineering (Eng.) Master of Engineering (M.Eng.) Ph.D. in Engineering, University of Bristol His research program focuses on radar-based precipitation estimation, hydroinformatics, and flood prediction systems. He pioneers deep learning applications for rainfall nowcasting and develops innovative methods for uncertainty quantification in hydrological modeling. Current work emphasizes cosmic-ray neutron sensor validation, satellite-based flood mapping, and seasonal forecast applications for reservoir operations, with strong emphasis on translating research into operational water management solutions. Recent publications (2023-2025) reveal three dominant research thrusts: (1) deep learning frameworks for spatiotemporal rainfall prediction, (2) global validation of precipitation and soil moisture datasets using novel sensor networks, and (3) operational implementation of seasonal forecasts for drought mitigation in South Korea. His work consistently bridges radar meteorology with practical hydrological applications across urban and data-scarce environments. Scientific Awards: No specific awards documented in source materials Dr. Rico-Ramirez supervises postgraduate researchers in radar hydrology and hydroinformatics, with projects spanning flood early warning systems, precipitation nowcasting, and climate adaptation strategies. His research receives funding for international collaborations focused on water security challenges, particularly in drought-prone regions and data-scarce basins like the Nile Delta. Current grants support development of integrated forecasting systems combining global datasets with machine learning for extreme event management. He leads the Radar Hydrology research group within Bristol's Water and Environmental Engineering division, collaborating closely with Professor Dawei Han on hydroinformatics and Dr. Rafael Rosolem on water-climate interactions. The team maintains active partnerships with meteorological agencies and water authorities globally, particularly in flood forecasting system implementation across South Korea and Mexico.

Pooya DavariView profile

Professor

• Harmonic and EMI analysis and mitigation in grid-tied converters
• Electromagnetic interference and compatibility
• High power density power electronic systems
• +2 more

Pooya Davari is a Professor and Head of the Section for Applied Power Electronic Systems at Aalborg University , Denmark. He leads the EMI/EMC in Power Electronics Research Group and serves as Vice Chair of the Energy Efficiency Mission. His research focuses on electromagnetic interference (EMI) and harmonic mitigation in power electronic systems, with over 200 publications and significant contributions to renewable energy integration. Education: B.Sc. and M.Sc. in Electronic Engineering (2004, 2008), Ph.D. in Power Electronics from Queensland University of Technology (2013) Prior Roles: Lecturer at QUT (2013–2014), Postdoc at AAU (2014) Research Interests: Harmonic and EMI analysis in grid-tied converters High power density converter design Signal processing for converter modeling Reliability of power electronic systems Article Trends: Recent work emphasizes EMI/EMC in renewable energy systems, wide bandgap semiconductors (SiC/GaN), and reliability modeling for EVs and hydrogen production via electrolysis. Sub-fields include converter topologies, grid integration challenges, and AI-driven diagnostics. Scientific Awards: Equinor 2022 Prize (Denmark’s oldest engineering award) IEEE EMC Society Young Professional Award (2020) World’s Top 2% Highly Cited Scientist (Stanford, 2021–2025) Multiple best paper awards (IEEE, Applied Sciences, etc.) Grants & Editorial Roles: Recipient of grants from Innovation Fund Denmark (Supra-EMC project), Horizon Europe (SOLARIS), and industry partnerships. Serves as Area Editor for IEEE Transactions on Transportation Electrification , Associate Editor for IEEE Transactions on Power Electronics , and Editor-in-Chief of Circuit World Journal (2020–2025). Labs & Standards: Coordinator of the EMC Laboratory at Aalborg University. Member of IEC standardization Working Groups 6 and 8 (TC77A), focusing on EMC strategies for power grids.

Maneesh AgrawalaView profile

Professor

• Computer Graphics
• Human-Computer Interaction
• Visualization
• +7 more

Maneesh Agrawala is the Forest Baskett Professor of Computer Science and Director of the Brown Institute for Media Innovation at Stanford University. He is also a consulting AI Scientist at Roblox. His research lies at the intersection of computer graphics, human-computer interaction (HCI), and visualization, with a focus on cognitive design principles for improving audio/visual media. He leads a vibrant research group and has advised numerous PhD students and postdocs. His research interests include: Computer Graphics Human-Computer Interaction Visualization and Visual Communication Cognitive Design Principles Generative AI and Diffusion Models Interactive Video and Sketch-based Interfaces Data and Information Visualization His recent publications (2023–2025) reflect a strong trend toward leveraging generative models—particularly diffusion models—for video and image synthesis, editing, and personalization. Key themes include controllable generation (e.g., SparseCtrl, ControlNet), sketch-to-image translation, relightable texturing, and tools that enhance visual storytelling and data communication. His work integrates cognitive science with computational tools to build systems that support human creativity and understanding. His scientific honors include: MacArthur Foundation Fellowship (2009) Alfred P. Sloan Foundation Fellowship (2007) NSF CAREER Award (2007) SIGGRAPH Significant New Researcher Award (2008) Allen Distinguished Investigator Award (2014) Induction into the SIGCHI Academy (2021) ACM Fellow (2022) Okawa Foundation Research Grant (2006) A dedicated mentor, Agrawala has advised a large cohort of students and postdocs, many of whom have gone on to influential positions in academia and industry. His lab develops tools for video editing (e.g., AnimateDiff, ControlNet), sketch-based design, and visualization (e.g., EmphasisChecker), often bridging theory with practical applications in media and education. He continues to be a leading figure in visual computing and interactive systems.

Qipei MeiView profile

Assistant Professor

• Civil Engineering
• Artificial Intelligence
• Robotics
• +4 more

Qipei Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. With an MSc in Computer Science and a PhD in Structural Engineering, he bridges civil engineering with artificial intelligence to enhance infrastructure productivity and sustainability. His research spans AI-driven design automation, robotics for construction safety, and IoT-based condition assessment. PhD, Structural Engineering - University of Alberta (2020) MSc, Computer Science - Georgia Institute of Technology (2018) MSc, Structural Engineering - University of Alberta (2014) B.E., Civil Engineering - Huazhong University of Science and Technology (2011) Mei's work focuses on three key areas: leveraging data-driven methods for design automation, applying sensing/robotics to construction operations, and using digital twins for infrastructure assessment. His team explores generative AI for housing design, robotic construction in remote communities, and smart monitoring systems. Recent publications highlight advancements in: lateral capacity prediction for monopile foundations, transformer-based architectural layout analysis, large language models for building code compliance, vision-language models for safety hazard detection, and sensor networks for bridge monitoring. These works demonstrate interdisciplinary integration of AI, structural engineering, and IoT. Mei actively collaborates with diverse researchers and welcomes graduate students to his Smart Infrastructure Technologies (SITE) Research Group, part of the Infrastructure and Human Tech Lab (IHT-Lab). He teaches advanced topics in structural and civil engineering while pursuing industry-funded projects through NSERC, CFI, and Alberta Innovates.

Tracy BeckerView profile

Adjunct Assistant Professor

• Earthquake Engineering
• Structural Optimization
• Seismic Isolation
• +3 more

Tracy Becker is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University, where she has been since 2014. Her expertise lies in the design, modeling, and experimental testing of high-performance structural systems with a focus on seismic isolation. Education: BS in Structural Engineering, University of California, San Diego MS and PhD in Structural Engineering, Mechanics and Materials, University of California, Berkeley Post-doctoral research at Kyoto University's Disaster Prevention Research Institute Research Interests: Becker specializes in seismic isolation systems, hybrid simulation methods, and structural performance under extreme events. Her work spans bridge engineering, nuclear infrastructure protection, and innovative materials for earthquake resilience. She integrates computational modeling with experimental validation to address challenges in: Nonlinear system behavior in isolated structures Multi-hazard optimization for seismic and wind loads Bridge management using data-driven and fuzzy logic frameworks Advanced gusset plate design for seismic retrofit Adaptive isolation systems for nuclear facilities Probabilistic lifetime demand predictions for infrastructure Teaching: She has instructed courses in Seismic Design (CIVENG 4ED4), Structural Mechanics (CIVENG 2C04), and Earthquake Engineering (CIVENG 730) at McMaster University.

FOONG Sew BunView profile

Adjunct Associate Professor

• IT Architecture Patterns
• Service-Oriented Architecture (SOA)
• Software Engineering Methodologies
• +4 more

Professor FOONG Sew Bun is an Adjunct Associate Professor at the Department of Information Systems and Analytics, School of Computing, National University of Singapore. He holds dual roles as Chief Technology Officer (CTO) for IBM Singapore and IBM ASEAN Software Group, and previously served as Managing Director at DBS Bank, Deputy Chief Executive at GovTech, and Global Head of Digital Transformation at Standard Chartered Bank. His career spans over three decades in IT leadership, technical architecture, and national-level advisory roles. Education: BSc and MSc in Computer Science from the University of Texas at Austin. Research interests focus on IT architecture patterns, service-orientation, software reuse, enterprise architecture, and fuzzy logic. He has authored/co-authored multiple IBM Redbooks and peer-reviewed publications in AI and software engineering. Awards include the 2016 IT Professional of the Year (SCS), IBM Distinguished Engineer distinction, and leadership accolades from IBM. He chairs the National Infocomm Competency Framework Steering Committee and has held key roles in Singapore's IT policy-making bodies. Teaching includes courses on digital transformation in finance (FT5002) and AI-driven business innovations (IS4261). His professional activities span technical councils, industry certifications, and academic collaborations.

Soufiene DjahelView profile

Professor

• Connected and Autonomous Vehicles (CAVs)
• Unmanned Aerial Vehicles (UAVs)
• Cyber Security
• +7 more

Soufiene Djahel is a Professor at the Centre for Future Transport and Cities (CFTC) at Coventry University, UK. His research focuses on connected and autonomous vehicles (CAVs), unmanned aerial vehicles (UAVs), cyber security, and smart cities. He holds a PhD in Secure Routing and Medium Access Protocols from Université des Sciences et Technologies de Lille (2010), and has held academic positions including Senior Lecturer at the University of Huddersfield and Manchester Metropolitan University. His research interests include CAV coordination protocols, cyber-physical security solutions, and intelligent transportation systems. Djahel leads projects such as the £1.2M AeroPharma Logistics initiative and has secured funding from the Newton Fund and JSPS. He is a recipient of the 2021 JSPS Invitational Fellowship and has published extensively in IEEE journals and conferences. Current projects explore UAVs-as-a-service, digital twins for CAVs, and B5G/6G for smart infrastructure. He advises PhD students on topics like AI-based threat mitigation and transport electrification. Djahel also serves as an external examiner and editorial board member for journals like IEEE Transactions on Intelligent Transportation Systems.

Ramana VinjamuriView profile

Associate Professor

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +4 more

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.

• Control Systems
• Signal Processing
• Neuro-Fuzzy Systems
• +3 more

Dragan S. Antic is a Professor at the Department of Automation, Faculty of Electronics, University of Niš. He earned his PhD in Automation from the same institution in 1994, following a Master's (1991) and Bachelor's (1987) degree in the same field. His research focuses on control systems, signal processing, and neuro-fuzzy systems, with a strong emphasis on sliding mode control, orthogonal function-based modeling, and adaptive neural networks. He has published 51 papers in journals with impact factors and currently participates in 2 national and 8 international research projects. Key research areas include dynamic system modeling, quasi-orthogonal filters, and finite-time stability analysis of time-delay systems. His work often integrates mathematical techniques with engineering applications, such as anti-lock braking systems and PID control optimization. Contact details include his office at Aleksandra Medvedeva 4, Niš, and the email dragan.antic@elfak.ni.ac.rs. Professional contributions include foundational research in endocrine neural networks and low-pass filter design, with notable publications in journals like Electronics Letters and Journal of the Franklin Institute . His efforts bridge theoretical advancements with practical engineering solutions in automation and control.