Find Academic Supervisors

• High Voltage Apparatus
• Electric Ageing Mechanisms
• High Voltage Electric Machines
• +12 more

Pål Stabel Keim is an Associate Professor and Deputy Head of Education at the Department of Electric Energy , Norwegian University of Science and Technology (NTNU) , within the Faculty of Information Technology and Electrical Engineering . His work focuses on high voltage apparatus, electrical machines, and power electronics with a system-oriented perspective. University: NTNU School: Faculty of Information Technology and Electrical Engineering Department: Department of Electric Energy Academic Rank: Associate Professor Keim's research emphasizes insulation design for HVDC, HVAC, and inverter-fed machines, partial discharge analysis, and system-oriented design of electric machines. His work integrates digital tools to enhance electrical system design from generation to consumption. His recent publications explore modular HVDC wind drive trains, multi-agent control systems, aerospace power electronics, and dielectric insulation under combined voltages. These span disciplines including Electrical Engineering , Renewable Energy , and Aerospace Systems . 2015 : Nowitech Innovation award 2016 : Grønn fases energipris Keim has supervised Jaume Martí Cascalló (Master's thesis, 2020) and co-authored extensive research on high-voltage systems. He teaches courses in electrical machines, power systems, and energy infrastructure.

Adam DyskoView profile

Associate Professor

• Power System Protection
• Power System Stability and Control
• Power Quality
• +7 more

Adam Dysko is a Reader (equivalent to Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He has been with the university since 1994, progressing from PhD student to research fellow and then to academic staff. Since 2007, he has served as a lecturer and continues to be an active faculty member. Doctor of Philosophy (PhD), University of Strathclyde, 1998 Master of Science (MSc), Łódź University of Technology, 1990 His research focuses on power system protection, particularly unconventional fault detection methods, stability and control of future power systems with high renewable penetration, power system modelling and simulation (including real-time), and power quality. His expertise includes dynamic and transient simulation, protection system modelling, and performance assessment. The recent publications highlight a strong trend in developing and validating advanced protection schemes for modern power systems, especially those integrating distributed and renewable generation. Key themes include loss-of-mains protection using satellite communication, coordinated PSS design for stability enhancement, and addressing protection challenges in UK distribution networks. The work spans both theoretical development and practical hardware validation, often in collaboration with industry. The IET Best Paper Award 2025 (DPSP APAC 2025) Dr Dysko is actively involved in multiple research projects, often as a co-investigator, collaborating with principal investigators like Dr Qiteng Hong and Prof Campbell Booth. These projects, such as SETTLE-INSIGHT (NIA), Shell-iCase, and SIF BLADE, are funded by industry partners including SSE and Shell, focusing on protection system innovation. He has delivered short courses to industry and contributes to industrial working groups like the Grid Code Review Panel (GCRP), Distribution Code Review Panel (DCRP), and Energy Networks Association (ENA). He also participates in international organizations such as CIGRE, IET, and IEEE, serving on programme committees and as an invited speaker. He is actively engaged in research leadership through participation in international conferences, journal peer review, and advisory roles, such as in the CIGRE Joint Working Group B5-C4.79. His work bridges academia and industry, contributing directly to the evolution of grid codes and engineering standards for safe and reliable power system operation in a renewable-rich future.

Anthony GachaganView profile

Professor

• Ultrasonics
• Non-Destructive Evaluation (NDE)
• Ultrasonic Transducer Design
• +13 more

Professor Anthony Gachagan is a leading academic at the University of Strathclyde, serving as Head of Department and Research Director in the Department of Electronic and Electrical Engineering (EEE) within the Faculty of Engineering. He has been Director of the Centre for Ultrasonic Engineering (CUE) since 2010, leading a multidisciplinary team of around 55 researchers with over £5M in active funding. He also holds leadership roles in RCNDE as Academic Chair and Management Board member, and participates in BINDT committees. His research spans ultrasonic transducer design, non-destructive evaluation (NDE), robotics, high-power ultrasound, and industrial process control. His work is highly collaborative, involving national and international partnerships with industry and academia, and contributes to sectors such as energy, aerospace, nuclear, and healthcare. He is actively involved in major research initiatives aimed at net zero, structural integrity, and advanced manufacturing. Recent publications highlight his focus on robotic ultrasonic inspection, adaptive signal processing, phased array techniques, and in-process monitoring of additive manufacturing. These works demonstrate a strong trend toward automation, real-time defect detection, and integration of ultrasonic systems in industrial and safety-critical applications. Scientific Awards: The BINDT Annual Conference Award (2019) Prof Gachagan has secured significant grants from EPSRC, Innovate UK, and industry partners, including projects on future ultrasonic engineering, lightning impulse testing, and robotic inspection for offshore wind. He supervises numerous research students and leads large collaborative teams. He also contributes to research commercialization through IAA projects. He leads the Centre for Ultrasonic Engineering (CUE), a vibrant research unit integrating electronic, mechanical, and biomedical engineers, physicists, and material scientists. The centre focuses on next-generation ultrasonic technologies, robotics integration, and industrial deployment.

• Renewable Energy Integration
• Power Systems Optimization
• Energy Storage
• +7 more

Daniel Fernández-Muñoz is an Associate Professor at the Universidad Politécnica de Madrid (UPM) , affiliated with the Department of Physical Electronics, Electrical Engineering and Applied Physics. He earned his PhD in 2021 with a thesis on "Generation scheduling in isolated power systems with high variable renewable generation and pump-storage," receiving both the Extraordinary Doctoral Award and Carlos González Cruz Award. He has held academic roles since 2006, including Assistant Professor positions from 2016-2021 and a current permanent Associate Professor appointment. Education : PhD in Electrical Engineering (UPM), DEA in Electrical Engineering (2010), Civil Engineering degree (2006) Research Focus : Renewable energy integration, power system optimization, battery degradation modeling, and frequency control in isolated grids Collaborations : Instituto de Sistemas Eléctricos de Potencia (Austria), EERA Joint Programme on Energy Storage, H2020 project eNeuron His work emphasizes hybrid wind-battery systems , pumped-storage hydropower , and virtual power plants , with notable publications in JCR Q1 journals. He has contributed to the Energy2Win project on sustainability education and served as a peer reviewer for multiple scientific journals. Scientific Awards Premio Extraordinario de Doctorado (UPM) Premio Carlos González Cruz Teaching activities include Physics for Biomedical Engineering and Energy Systems for Telecommunications. He has participated in international conferences as an invited speaker and contributed to projects funded by the Spanish government and private entities.

Samarjit ChakrabortyView profile

Professor

• Distributed Embedded Systems
• Hardware/Software Co-design
• Embedded Control Systems
• +9 more

Samarjit Chakraborty is the William R. Kenan, Jr. Distinguished Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. He previously held the Chair of Real-Time Computer Systems at the Technical University of Munich (2008–2019) and was an assistant professor at the National University of Singapore (2003–2008). His research spans real-time embedded systems, cyber-physical systems (CPS), and automotive security. Research Interests include distributed embedded systems, hardware/software co-design, low-power systems, energy storage, electromobility, and sensor network-based information processing. His work addresses challenges in scheduling algorithms for autonomous vehicles, timing predictability in automotive networks, and safety-critical controller implementations. Recent Publications highlight advancements in Timing analysis for automotive networks Energy modeling of Bluetooth Low Energy Autonomous vehicle perception computing Security in automotive systems Flexible manufacturing with process dynamics Scientific Awards include the ETH Medal, European DAAD Outstanding Doctoral Dissertation Award, multiple best paper awards at conferences (ISLPED, ICCD, RTCSA, etc.), the 2023 Humboldt Professorship, and IEEE Fellowship. Advising active PhD students: Clara Hobbs, Shengjie Xu, Sharmin Aktar, and postdoc Enrico Fraccaroli. His research is supported by NSF grants and industry partnerships with General Motors, Intel, Google, BMW, Audi, Siemens, and Bosch.

Dennis EschView profile

Assistant Professor

• Brand Management
• Consumer Behavior
• Marketing Strategy
• +4 more

Dennis Esch serves as Lecturer (Assistant Professor) in Marketing and Behavioural Science at Cranfield School of Management, Cranfield University, where he acts as the Strategic Marketing and Sales Group's liaison for research matters. Previously, he directed the Cranfield Customer Management Forum, one of the School's premier research practice clubs. Dr. Esch holds award-winning academic qualifications in marketing, psychology and behavioural science from Lancaster University Management School and Cranfield School of Management, establishing a strong interdisciplinary foundation for his research. His research program centers on three interconnected domains: the psychological mechanisms of brand influence in consumer decision-making, the transformative impact of emerging technologies on consumer perceptions and preferences, and the complex relationship between social consumption experiences and human happiness. Methodologically, he integrates behavioral science principles with marketing theory through quantitative approaches including experiments, field studies, and big data analysis to address questions with both theoretical significance and practical managerial relevance. Dr. Esch's publication record reveals an evolving research trajectory from foundational consumer behavior studies (2016-2019) examining product assortment effects, sports consumption impacts, and social identity dynamics, toward contemporary methodological innovations in mobile research platforms (2022-2025). His recent work demonstrates particular expertise in evaluating mobile-first platforms for crowdsourcing behavioral, advertising, and personality research, addressing critical questions about digital research validity and ecological relevance. While specific major scientific awards beyond his noted 'award-winning degrees' aren't detailed in available information, his research has been published in reputable outlets including Behavior Research Methods and Academy of Marketing Science proceedings, indicating scholarly recognition. Dr. Esch brings extensive international teaching experience across multiple contexts, having instructed postgraduate students and executives in brand management, marketing strategy, consumer behavior, and research methods in the UK, Germany, and Switzerland. His pre-academic industry background includes significant roles at Audi, Henkel, Vaillant Group, and Swiss Post, providing practical grounding for his academic work and enhancing the real-world applicability of his research findings. His research methodology reflects a commitment to bridging the gap between laboratory rigor and field relevance, particularly through his investigations of mobile research platforms that can capture authentic consumer experiences while maintaining scientific validity. This approach addresses contemporary challenges in behavioral research methodology while generating actionable insights for marketing practitioners.

Bradley HayesView profile

Associate Professor

• Human-Robot Interaction
• Explainable AI (XAI)
• Collaborative Robotics
• +5 more

Bradley Hayes is an Associate Professor of Computer Science and Director of the Collaborative AI and Robotics (CAIRO) Laboratory at the University of Colorado Boulder. His work focuses on enabling autonomous agents and robots to collaborate safely and effectively with humans through techniques in human-robot interaction, explainable AI, and learning from demonstration. PhD in Computer Science, Yale University (2015) Postdoctoral Associate, MIT Interactive Robotics Group Research interests span collaborative robotics, dependable AI systems, and imitation learning for human-robot teaming, with applications in manufacturing, healthcare, and autonomous vehicles. His lab develops methods for task planning, motion prediction, and trust calibration in human-machine partnerships. Recent publications emphasize explainable sequential decision-making, neuromorphic learning architectures, and augmented reality interfaces for robotic training. Awards include Sustainability Recognition (2025) for motion planning efficiency Best Student Paper Runner-up (AAMAS 2022) Best Technical Paper Runner-up (HRI 2019) As lab director, he has mentored 12 PhD/Master students to completion, including Matthew Luebbers, Aaquib Tabrez, and Christine Chang. Funding sources include NSF grants and industry partnerships like Circadence, where he serves as Chief Technology Officer.

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +5 more

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

Grzegorz ChrupałaView profile

Associate Professor

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +3 more

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

Yves Le TraonView profile

Professor

• Machine learning systems
• Software testing
• Software security
• +8 more

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.

John H. MaceView profile

Professor

• Autobiographical Memory
• Involuntary Memory
• Cognitive Psychology
• +3 more

John H. Mace serves as Professor and Chair of the Psychology Department at Eastern Illinois University, concurrently holding a professorship in Aging Studies (Gerontology). Based in Klehm Hall, Charleston, his contact email is jhmace@eiu.edu. His research centers on autobiographical memory systems , specifically: Involuntary vs. voluntary memory recall mechanisms Organization principles of autobiographical memories Semantic-to-autobiographical priming under varied conditions (non-verbal cues, subliminal stimuli, cue repetition) Functional purpose of involuntary memories Methodological innovations in diary-based memory research Recent publications (2023-2024) demonstrate rigorous experimental approaches examining how semantic cues trigger autobiographical memories, revealing critical insights about memory accessibility thresholds and unconscious processing. His work bridges cognitive psychology with consciousness studies through innovative paradigms like vigilance tasks and subliminal stimulation protocols. Dr. Mace maintains active teaching responsibilities for Cognitive Psychology and Human Memory courses while leading departmental operations. His public profile includes media recognition as the 82-year-old Boston Marathon qualifier in 2020, featured across eight media outlets including The Boston Globe .

Thomas WöhlingView profile

Researcher

• Integrated environmental systems modeling
• Surface water-groundwater interactions
• Braided river systems
• +9 more

PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.

Sina JamaliView profile

Research Fellow

• Electrochemistry
• Materials Science
• Corrosion Science
• +3 more

Dr. Sina Jamali is an ARC DECRA Fellow at the School of Chemistry, University of New South Wales, specializing in the electrochemistry of materials and cells. He collaborates closely with the Smart Materials and Surfaces Research Group led by Scientia Professor Justin Gooding. His research focuses on exploiting biological noise for next-generation electrochemical biosensors. Education: PhD (2016) - Intelligent Polymer Research Institute, University of Wollongong M.Sc. (2008) & B.Sc. (2006) - Department of Polymer Engineering & Color Tech., Tehran Polytechnic Research Interests: Dr. Jamali's work centers on fundamental and applied electrochemistry, with particular expertise in: Stochastic electrochemical measurements and noise analysis Corrosion protection mechanisms for biomedical and industrial materials Development of nanozymes and biosensors Surface engineering of bio-implants Novel electrochemical characterization techniques Awards & Honors: ARC Discovery Early Career Researcher Award (2021) Bill Wheeler Award for Medical Bionics Research (2013) Professional Affiliations: Member of the Smart Materials and Surfaces Research Group, Australian Centre for NanoMedicine (ACN), and Illawarra Health and Medical Research Institute. Teaching: Delivers courses in Analytical Chemistry (CHEM2041) and Materials Chemistry (CHEM3061) at UNSW.

• Data Mining
• Privacy-Preserving Computation
• Artificial Intelligence
• +3 more

Onur KARDEŞ serves as Assistant Professor in the Department of Computer Engineering at Beykent University's Faculty of Engineering and Architecture since 2020. His prior academic appointments include: Research Assistant, Department of Computer Science, Stevens Institute of Technology (2004-2008) Lecturer, Department of Mathematics and Computer Science, Faculty of Arts and Sciences, Beykent University (2000-2004) His research integrates data mining, privacy-preserving computation, and artificial intelligence with practical applications in educational technology and urban systems. Key contributions include developing privacy-enhancing protocols for distributed data mining and pioneering generative AI solutions for automated student assessment and digital teaching assistants. Analysis of his 2023-2025 publications reveals accelerating focus on generative AI implementations, particularly in educational evaluation (automated examination paper analysis) and urban planning (AI-driven smart city frameworks). This evolution demonstrates strategic adaptation to emerging technologies while maintaining core expertise in secure computation and data mining.

• Electrical Engineering
• Wind Turbine Systems
• Electric Vehicle Powertrains
• +5 more

Torbjörn Thiringer is a Professor in Electrical Engineering at Chalmers University of Technology. His research focuses on electrical systems for wind turbines and electric vehicles, with particular emphasis on system-level analysis and component-level studies of electrical machines, power electronics, and battery systems. Key research areas: Wind turbine systems, Electric vehicle drives, Battery degradation, Power electronics optimization Recent work explores graphene-based thermal management, fuel cell hybrid vehicles, and direct current building distribution efficiency His publications demonstrate interdisciplinary engagement with topics spanning: Finite element analysis of motor designs Life cycle assessment of energy systems Thermal modeling of SiC inverters Wave energy converter optimization Core loss measurement techniques Hydrogen fuel cell integration Professor Thiringer's collaborations span multiple institutions and industry partners, focusing on both theoretical modeling and practical implementation of advanced energy systems.