Find Academic Supervisors

Hamed Nabizadeh RafsanjaniView profile

Lecturer

• IoT
• AI in Architecture, Engineering, and Construction (AEC)
• Digital Twin Technology
• +5 more

Hamed Nabizadeh Rafsanjani, Ph.D., P.E., ENV SP is a Lecturer at the University of Georgia (UGA) College of Engineering since 2018. He holds a Ph.D. and master’s degrees in Construction Engineering and Management from the University of Nebraska-Lincoln (UNL). Prior to UGA, he served as an assistant professor and visiting lecturer at various universities, earning accolades such as the Best Teacher Award and Best Researcher Award. His research focuses on IoT, AI, and Digital Twin technologies applied to Architecture, Engineering, and Construction (AEC) industries. As director of iSC-LAB, he develops IoT-based platforms to analyze building occupants’ energy-use behaviors and improve learning environments. His research interests span smart building systems, non-intrusive energy monitoring, and occupant behavior analysis. Key projects include a global occupant behavior database and an IoT-based smartphone energy assistant (iSEA). He has secured grants from agencies like the National Science Foundation (NSF), contributing to studies on energy efficiency, construction project management, and sustainability in built environments. Rafsanjani’s publications (2018–2023) emphasize AI-driven solutions for AEC, IoT applications in energy management, and adaptive learning systems. His work bridges technology and human behavior to optimize building performance and educational outcomes. Awards reflect his dual excellence in pedagogy and research, with ongoing contributions to the Sustainable Human-Building Ecosystems field.

Yew Weng KeanView profile

Assistant Professor

• Renewable Energy
• Smart Grids
• Microgrids
• +5 more

Dr. Yew Weng Kean is an Assistant Professor in the Department of Electrical Engineering at Heriot-Watt University Malaysia, part of the School of Engineering & Physical Sciences. He holds a Ph.D. in Electrical Engineering from Universiti Tenaga Nasional, where his doctoral work focused on "Design of an AC-coupled stand-alone hybrid renewable energy system with an improved control strategy". His research expertise encompasses microgrids, renewable energy systems, smart grids, and AI-driven solutions for energy management. He actively contributes to interdisciplinary projects involving material science, computer vision, and sustainable technologies. His research interests span microgrid optimization, stand-alone hybrid systems, solar PV integration, energy storage technologies, and artificial intelligence applications in energy sectors. He has authored over 24 peer-reviewed publications, with recent work focusing on defect detection in wind turbine blades, advanced control mechanisms for multi-agent systems, and sustainable bio-hydrogen production. Dr. Yew’s articles highlight trends in combining AI with renewable energy systems, such as deep learning models for fault diagnosis in photovoltaic arrays and novel approaches to wind turbine blade inspection via aerial imagery. His work also addresses challenges in material science, including heat treatment effects on aluminum alloys and acoustic properties of natural fibers. He currently supervises research in energy systems, smart grids, and sustainable materials, and collaborates on global energy education initiatives. His affiliations include roles in university-led sustainability projects and contributions to IEEE-related conferences.

Donald C. WunschView profile

Professor

• Neural Networks
• Reinforcement Learning
• Adaptive Resonance Theory
• +6 more

Donald C. Wunsch is the Mary K. Finley Missouri Distinguished Professor in Electrical and Computer Engineering at Missouri University of Science & Technology (Missouri S&T), where he directs the Applied Computational Intelligence Laboratory. He previously served as Interim Director of S&T’s Intelligent Systems Center and holds courtesy appointments in Computer Science, Systems Engineering, and Engineering Management. His research focuses on neural networks, reinforcement learning, adaptive resonance theory, bioinformatics, and robotics. Wunsch is an IEEE Fellow, INNS Fellow, and recipient of the 2015 INNS Gabor Award and 2019 Ada Lovelace Service Award. He has led numerous professional organizations, including the INNS Board of Governors and IJCNN conferences. Education: Executive MBA, Washington University in St. Louis (2006) Ph.D. in Electrical Engineering, University of Washington (1991) M.S. in Applied Mathematics, University of Washington (1987) B.S. in Applied Mathematics, University of New Mexico (1984) Research Interests: Donald’s work spans adaptive resonance systems, reinforcement learning architectures, neurofuzzy regression, robotic swarms, and bioinformatics. He emphasizes practical applications in energy systems (e.g., pumped storage hydro optimization), cybersecurity, and medical data analysis. Recent projects include Hamiltonian-driven adaptive dynamic programming and cluster validity indices for streaming data. Awards & Recognition: 2015 INNS Gabor Award INNS Senior Fellow (2007–2013) NSF CAREER Award 2019 INNS Ada Lovelace Service Award Labs & Leadership: As director of the Applied Computational Intelligence Lab, he oversees research in AI/AS applications for critical infrastructure and smart grids. He also chairs the Missouri S&T Information Technology and Computing Committee and actively contributes to interdisciplinary initiatives like the University of Missouri Bioinformatics Consortium.

Carlos A. Ocampo MartínezView profile

Associate Professor

• Control Engineering
• Model Predictive Control
• Constrained Control Systems
• +12 more

Carlos A. Ocampo Martínez is an Associate Professor in the Department of Automatic Control (ESAII) at the Universitat Politècnica de Catalunya (UPC), BarcelonaTech, Spain. He is affiliated with the Institut de Robòtica i Informàtica Industrial, CSIC-UPC, a joint research center between UPC and the Spanish National Research Council. He has been with UPC since 2011 and served as Deputy Director of IRI from 2014 to 2018. Education: PhD in Control Engineering, Universitat Politècnica de Catalunya, 2007 MSc in Industrial Automation, National University of Colombia, 2003 BSc in Electronics Engineering, National University of Colombia, 2001 His research is centered on model predictive control (MPC) , particularly constrained and distributed MPC, with applications in energy, water, and smart manufacturing. He investigates large-scale systems management, partitioning strategies, and non-centralized control architectures. His work integrates IoT frameworks for smart industrial systems. Key domains include renewable hydrogen production, fuel cell vehicles, microalgae bioreactors, and solar thermal plants. The recent publications reflect a strong trend in applying control theory to sustainable energy systems and environmental management. There is a clear focus on integrating game theory, population dynamics, and optimization into MPC frameworks for distributed and coalitional control. Applications span hydrogen infrastructure, solar energy, water irrigation, and transportation electrification, demonstrating interdisciplinary impact. Scientific Awards: Juan de la Cierva Research Fellow Dr. Ocampo-Martínez actively supervises PhD students and leads research projects such as MASHED , which focuses on digitalized energy systems with hybrid storage. He has advised students on topics including alkaline electrolyzers, hydrogen production control, and alcohol steam reformers. His grant involvement emphasizes renewable integration, smart grids, and sustainable transport. He collaborates with researchers across Europe and contributes to high-impact journals and IFAC conferences. He is a key member of the Automatic Control research group at ESAII and contributes to the strategic direction of the Institut de Robòtica i Informàtica Industrial. His team integrates control theory, optimization, and real-world industrial applications, particularly in energy and water systems.

Puig Oriol, XavierView profile

Assistant Professor

• Bayesian data analysis
• Model Based clustering
• Multinomial model
• +7 more

Xavier Puig is an Assistant Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the Department of Statistics and Operations Research and the School of Mathematics and Statistics (FME). He is a member of the ADBD - Analysis of Complex Data for Business Decisions and GRBIO - Biostatistics and Bioinformatics Research Group . His research focuses on Bayesian data analysis , with applications in Epidemiology Ecology Public health Political science Industrial quality control Marketing analytics Recent publications reveal a strong trend in Bayesian spatiotemporal modeling for health data, alcohol-migraine interaction studies, and industrial error rate monitoring . His work combines methodological innovation with real-world applications across diverse sectors. Collaborations include researchers from biostatistics, clinical epidemiology, and industrial engineering. He has contributed to 44 indexed journal articles and participated in 49 congress presentations , with recent projects focusing on competitive research and non-competitive industrial collaborations in statistical modeling.

Matthew MoranView profile

Professor

• International Security
• Conflict Studies
• Weapons of Mass Destruction (WMD)
• +7 more

Matthew Moran is a Professor of International Security and Head of the Department of War Studies at King’s College London. He leads one of the university's largest departments, overseeing strategic direction and daily operations. Previously held roles include Director of Research in War Studies and Co-Director of the Centre for Science and Security Studies. Education: Undergraduate and Master’s degrees from the University of Galway; PhD from University College London. His research is supported by prestigious organizations including the British Academy, ESRC, Carnegie Corporation, and MacArthur Foundation. Research focuses on: social/political drivers of security events, WMD politics, coercive diplomacy, and open-source intelligence. Key projects include the Centre for Science and Security Studies, Nuclear Security Culture Programme, and Conflict Records Unit. Publications include eight books such as forthcoming Oxford U.P. study on Syrian chemical weapons and Palgrave titles on nuclear verification and open-source intelligence. Over 30 academic articles published in journals like Security Studies and International Affairs . Engagement: Advises governments and international organizations on security policy. Collaborates with foreign ministries through executive education programs. Media contributions include BBC News, The Guardian, and France24. Labs/Initiatives: Co-director of the Centre for Science and Security Studies (CSSS). Active in King's Cybersecurity Centre (EPSRC-NCSC Academic Excellence Centre) and the Conflict Records Unit. Leads Nuclear Security Culture Programme working with global partners.

• Control Systems
• Model Predictive Control
• Data-Driven Methods
• +3 more

Shuhao Yan is a Researcher and Post Doc Researcher at the University of Stuttgart, affiliated with the Department of Mathematics/IMNG within Faculty 08. His research focuses on advanced control systems, particularly in data-driven methods for uncertain systems, stochastic model predictive control (MPC), and grid-forming control in power electronics. He leads the SimTech Project Network 4-3 (II) titled 'Data-Integrated Control Systems Design with Guarantees,' emphasizing robustness and real-world applicability. His research interests revolve around integrating data with traditional control theory to address challenges in uncertain dynamic systems. Key areas include robust MPC, stochastic optimization, and the application of machine learning in control systems. He has contributed to advancements in grid stability analysis, voltage source converter control, and adaptive control strategies under intermittent data conditions. Recent publications highlight trends in learning-based control (e.g., homothetic tube MPC), stochastic MPC with probabilistic constraints, and power grid stability analysis for renewable energy integration. His work bridges theoretical foundations with practical implementations in energy systems and automation. Shuhao Yan holds a PhD and currently conducts research within the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling. His projects emphasize interdisciplinary collaboration between control theory, applied mathematics, and electrical engineering.

• Geochemistry
• Volcanology
• Petrology
• +2 more

Sri Budhi Utami is a Researcher at the University of Liège , affiliated with the Faculty of Sciences and the Department of Geology . Her academic work focuses on volcanology, petrology, and geochemistry , particularly investigating magma dynamics, volatile element behavior, and eruptive processes. Key research themes include magma ascent rates, diffusion of redox-sensitive elements, and fluid phase impacts on eruption styles. Recent publications (2013–2025) analyze volcanic systems in Indonesia (Kawah Ijen, Kelud), Chile (Villarrica, Osorno), and Hawaii (Kaua’i dykes), with a growing emphasis on diversity and inclusion in geosciences.

Frank L. H. WolfsView profile

Professor

• Dark Matter Detection
• Particle Physics
• Detector Technology
• +3 more

Prof. Frank L. H. Wolfs is a Professor in the Department of Physics and Astronomy at the University of Rochester. His research focuses on experimental particle physics, particularly in dark matter detection using advanced xenon-based detectors. He leads the LUX-ZEPLIN (LZ) experiment, responsible for trigger electronics development and calibration systems. His group has contributed to the Zeplin II (Boulby Mine, UK) and LUX (Sanford Underground Research Facility, South Dakota) projects, advancing detector technologies for underground physics. He also engages in educational outreach via online experiments like the muon lifetime measurement. Prof. Wolfs teaches courses ranging from introductory physics to advanced labs and participates in initiatives like the College Teaching, Learning, and Technology Roundtable. His work emphasizes precision instrumentation and interdisciplinary collaborations in astrophysics and nuclear physics. Research Interests: Direct detection of dark matter Liquid xenon time projection chambers Calibration and data acquisition systems Low-energy nuclear recoils Background reduction techniques Scientific Contributions: Design of the DDC-8DSP trigger system for LZ Development of the HydroX detector concept Advancements in electronic recoil discrimination

• Natural Hazards
• Debris Flows
• Rockfall
• +6 more

Johannes Hübl is a Full Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), leading the Institute for Alpine Natural Hazards within the Department of Landscape, Water and Infrastructure. His research focuses on alpine mass movement dynamics—particularly debris flows, rockfalls, and floods—with emphasis on risk management, mitigation engineering, and climate change adaptation. He maintains an active project portfolio including EU-funded initiatives and Austrian federal collaborations. His primary research interests include the physical processes of debris flows (surge dynamics, run-up behavior, sediment transport), rockfall hazard assessment, flood protection engineering, and climate change impacts on alpine hazards. Recent work pioneers advanced monitoring techniques like pulse-Doppler radar and LiDAR for real-time measurement of flow velocity and height, directly informing protective dam design and early warning systems. He also develops GIS-integrated simulation models for granular flows and risk assessment. Hübl's publications trend toward high-resolution field studies of debris flow dynamics, with significant focus on spatial impact distribution, statistical discharge modeling, and wildfire-hazard linkages. This reflects growing priorities in climate-resilient infrastructure and quantifiable risk assessment methodologies across European alpine regions. Scientific awards: No awards, fellowships, or medals are documented in the provided materials. Advising and grants: Professor Hübl has supervised numerous university theses as indicated by his profile. His grant leadership spans over 30 projects since 1994, including: EU projects: Nature-Based Solutions for Climate-Resilient Infrastructure (2024-2028), THARMIT (2000-2003) Austrian federal funding: 20+ projects with BMLFUW/BMF (e.g., rockfall detection, sediment management) International commissions: Bhutan flood mitigation (2000), Melamchi River reviews (2023) Industry collaborations: Check dam failure analysis, Cougar Creek optimization Labs and teams: He directs field monitoring operations including the Illgraben (Switzerland) and Gadria Creek (Italy) test sites. His institute employs physical modeling (hydraulic experiments, laser scanning) and develops real-time warning systems for alpine catchments, working closely with Austrian watershed management agencies and European consortia on event documentation and structural countermeasures.

• Phase Change Memory
• Neuromorphic Computing
• Very Large Scale Integration
• +4 more

Houpeng Chen is a Research Professor at the Chinese Academy of Sciences, specifically affiliated with the School of Microsystem and Information Technology in the Department of Microelectronics. With over two decades of research experience since the early 2000s, Chen has established himself as a leading expert in memory systems and circuit design, particularly in the areas of Phase Change Memory and neuromorphic computing. Chen's research primarily focuses on advanced memory technologies, with particular emphasis on Phase Change Memory (PCM) systems, neuromorphic computing architectures, and analog circuit design for memory applications. His work spans from fundamental circuit design for memory systems to advanced computing architectures that leverage novel memory technologies. A significant portion of his recent work explores in-memory computing paradigms and brain-inspired computing systems, demonstrating a strategic shift toward next-generation computing architectures that address the limitations of traditional von Neumann systems. Analysis of Chen's publication record shows a clear evolution from traditional circuit design toward more innovative memory-based computing architectures. His recent work demonstrates strong expertise in 3D cross-point memory systems, in-memory computing, and neuromorphic hardware implementations. The research shows consistent quality with publications in top-tier IEEE journals and conferences, indicating strong recognition within the semiconductor and memory research community. As evidenced by the authorship patterns in his publications, Chen has successfully mentored numerous graduate students and junior researchers who have gone on to become first authors on significant publications. His collaborative network includes extensive work with Zhitang Song, Qian Wang, and Xi Li, suggesting a well-established research group with strong internal collaboration.

DJEMAI MohamedView profile

Professor

• Control Theory
• Hybrid Systems
• Multi-Agent Systems
• +9 more

Dr. Mohamed Djemai is a Full Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Cergy, and INSA Hauts-de-France. He is affiliated with the Quartz Laboratory (EA 7393) and LAMIH UMR CNRS 8201 at University Polytechnic Hauts-de-France. His research focuses on nonlinear control systems theory, with emphasis on hybrid and variable structure systems, sliding mode approaches, fault detection, and applications to power systems, robotics, and vehicle dynamics. IEEE Senior Member Associate Editor for Nonlinear Analysis: Hybrid Systems Co-Facilitator of National Working Group GT-SDH (2014–present) Member of IFAC-TC-1.3 (Discrete Event and Hybrid Systems) since 2001 Member of IFAC-TC-2.1 (Control Design) since 2005 His recent publications address fractional-order control of multiagent systems, stability analysis on time scales, fault-tolerant satellite attitude control, and robust consensus algorithms for nonlinear systems. Key methodologies include sliding mode control, event-triggered control, and observer-based fault detection. Current teaching activities encompass diagnostics, linear systems, signal processing, and sensor conditioning. The trend in Dr. Djemai's research since 2022 involves advanced control strategies for cyber-physical systems, distributed fault detection mechanisms, and time scale theory applications to intermittent communication problems. Notable collaborations include work with Michael Defoort, Stefano Di Gennaro, and international institutions like Kyungpook National University and University of Reims. Scientific contributions include: IEEE Senior Member recognition Development of robust exact filtering differentiators Innovations in fixed-time consensus protocols Leadership in IFAC technical committees Editorial role in hybrid systems analysis His laboratory work at Quartz and LAMIH supports applications in aerospace systems, renewable energy conversion, and industrial risk management architectures.

Laura M. WallaceView profile

Research Professor

• tectonics
• crustal deformation
• earthquakes
• +3 more

Laura M. Wallace serves as a Research Professor at the University of Texas Institute for Geophysics (UTIG) with a joint appointment at GNS Science in New Zealand. Her pioneering work focuses on geodetic analysis of crustal deformation at plate boundaries, particularly slow slip events in subduction zones like New Zealand's Hikurangi Margin. In 2018, she co-led IODP Expedition 375 aboard the JOIDES Resolution, drilling into active slow slip zones to install long-term monitoring observatories. Education: Ph.D. in Earth Sciences, University of California, Santa Cruz B.S. in Geology, University of North Carolina at Chapel Hill Research Focus: Wallace's work centers on tectonics and crustal deformation , utilizing land-based GPS and seafloor geodetic instruments to study subduction zone dynamics . Her 2002 discovery of slow slip events at Hikurangi revolutionized understanding of fault behavior, revealing how fluid pressure and lithological heterogeneity control slip patterns. She integrates geodetic data with seismic and drilling results to model earthquake cycles. Publication Trends: Recent work (2023-2025) analyzes spatiotemporal evolution of slow slip using multi-instrument approaches (GNSS, InSAR, seafloor sensors), with emphasis on New Zealand's seismic hazard models. Key themes include fluid-mediated fault weakening, seamount subduction effects, and probabilistic forecasting of megathrust events. Leadership & Grants: Wallace secured major funding through IODP for Expedition 375, enabling core sampling and observatory deployment at Hikurangi. She contributes to national hazard assessments for New Zealand, developing geodetic strain rate models and deformation frameworks for seismic hazard maps. Collaborative Infrastructure: She leverages UTIG's geodetic networks and GNS Science partnerships, utilizing JOIDES Resolution drilling data and SMART subsea cable initiatives for real-time offshore monitoring. Her lab integrates field observations with numerical modeling to predict subduction zone behavior.

• Cardiovascular magnetic resonance
• myocardial ischemia
• coronary heart disease
• +2 more

Matthias Friedrich, MD, is a Professor in the Department of Medicine, Faculty of Medicine and Health Sciences at McGill University, and serves as a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the Glen site. He leads the Cardiovascular Magnetic Resonance (CMR) Research Centre within the Cardiovascular Health Across the Lifespan Program at the Centre for Translational Biology. Dr. Friedrich's research focuses on novel techniques for optimizing and simplifying clinical cardiovascular magnetic resonance (CMR) protocols that require minimal interaction and reduce the need for contrast agents or stress protocols. His work includes the application of novel CMR image reconstruction techniques, machine learning, automated scanning, and the validation of novel imaging biomarkers in clinical studies. Several current projects explore new options for assessing coronary vascular function using breathing maneuvers instead of traditional pharmacological vasodilators or stress protocols as vasoactive triggers. Dr. Friedrich's recent publications demonstrate a strong emphasis on advancing oxygenation-sensitive CMR techniques and applying them to understand myocardial ischemia, myocarditis, and various cardiomyopathies. His work increasingly integrates artificial intelligence and machine learning approaches to enhance diagnostic capabilities, with recent studies focusing on automated detection of hypertrophic cardiomyopathy and other cardiac conditions using deep learning analysis of CMR images. Fellow of the European Society of Cardiology (FESC) Fellow of the American College of Cardiology (FACC) Dr. Friedrich's research program receives significant support from the Family Courtois Foundation through the McGill University Health Centre Foundation. He maintains close collaborations with invasive and non-invasive cardiology, cardiac surgery, and radiology teams at the MUHC, as well as active collaborations with other hospitals in Montreal and academic centers worldwide, including cohort research with the Canadian Alliance for Healthy Hearts and Minds. The CMR Research Centre at McGill University Health Centre, led by Dr. Friedrich, has access to two clinical MRI scanners (Siemens Skyra 3.0T, GE Artist 1.5T) and full-time access to a dedicated MRI system (GE Premier 3.0T) for CMR research. The team is actively developing and validating novel imaging techniques that aim to significantly change diagnostic cardiology practice through improved accessibility, safety, and diagnostic accuracy.

Michael LemmonView profile

Professor

• Networked Control Systems
• Event-Triggered Control
• Smart Grid Technology
• +5 more

Michael Lemmon is a Professor in the Department of Electrical Engineering at the University of Notre Dame's College of Engineering. He has been a faculty member at Notre Dame since 1990, contributing significantly to the field of networked control systems and related applications. Education: Ph.D., Electrical Engineering, Carnegie Mellon University, 1990 M.S., Electrical Engineering, Carnegie Mellon University, 1990 B.S., Mathematics, Stanford University, 1979 Professor Lemmon's research focuses on understanding the interrelationship among communication, computation, and control in large-scale sensor-actuator networks. He is particularly known for his pioneering work on event-triggered control systems and for deploying one of the first municipal scale sensor-actuator networks for wastewater management. His current research explores deep learning applications for adaptive control of complex dynamical systems. His work spans theoretical foundations of networked control systems to practical applications in critical infrastructure including smart grids, power systems, and water management systems. Analysis of Professor Lemmon's recent publications reveals a strong focus on event-triggered and self-triggered control methodologies for networked systems. His work bridges theoretical control theory with practical applications in power systems and sensor networks. Key themes include communication efficiency in control systems, stability analysis of networked systems, and the application of these principles to real-world infrastructure challenges. Current Research Projects: "Using Data Science to Protect Tap Water Quality" (Lucy Family Institute, 2022-2023) - Using data science to identify homes at risk for unhealthy tap water and develop mitigation strategies "CPS: SMALL: Learning How to Control - A Meta-Learning Approach for the Adaptive Control of Cyber-Physical Systems" (NSF, 2023-2026) - Developing machine learning algorithms for adaptive control of IoT-enabled manufacturing systems Professor Lemmon teaches several courses including Systems Theory and Applications (EE 30122), Advanced Control (EE 60655), and Introduction to Deep Learning (EE 60572). His teaching spans both undergraduate and graduate levels, with a focus on control systems theory and emerging applications of machine learning in control engineering.