Find Academic Supervisors

Christopher H. T. LeeView profile

Associate Professor

• Electric motors and drives
• Renewable energies
• Electromechanical propulsion systems
• +7 more

Christopher H. T. Lee is an Associate Professor at the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), with a courtesy appointment at the Lee Kong Chian School of Medicine. He was promoted to Associate Professor with Tenure in March 2024 after joining NTU as an Assistant Professor in October 2018. Dr. Lee holds the prestigious Singapore NRF Fellowship (Class 2020) and was previously awarded the Nanyang Assistant Professorship, recognizing him as a top 1% faculty member at NTU. Dr. Lee's research focuses on electric motors and drives, renewable energies, and electromechanical propulsion systems. His specific interests include: Energy Engineering and Power Technology Smart Cities applications Advanced Control Theory for motor systems Smart Grids integration Smart Energy Conversion Systems Advanced Magnetics for motor design Dr. Lee's extensive publication record shows a strong focus on motor control algorithms, innovative motor designs, and energy efficiency optimization. His recent work demonstrates expertise in predictive control systems, flux-modulated machines, and fault diagnosis techniques for electric motor systems. The research spans both theoretical advancements and practical applications in industrial settings. Dr. Lee has received numerous prestigious awards recognizing his contributions to the field: Nagamori Award (first recipient under 40) IEEE Myron Zucker Award (first recipient outside USA and Canada) NRF Fellowship (Singapore's most prestigious entry award for young researchers) MDPI Energies Young Investigator Award IET Fellow First Place Best Paper Award from IEEE TEC Top Cited Article from IET RPG As a dedicated educator and researcher, Dr. Lee has successfully supervised numerous students and secured significant research funding. He serves as Principal Investigator for grants totaling over SGD 13.5 million (approximately USD 10 million). His innovative ideas have been adopted by major corporations including Panasonic, Rolls-Royce, Magna International, Schaeffler Group, and Jingsu Xinri E-Vehicle Co. Ltd. Notably, he directs the NTU-Panasonic Joint Lab, which has received SGD 5 million investment to support over eight concurrent projects focusing on new motor concepts and 3D-printing technology. Dr. Lee leads the NTU Motor Lab, an interdisciplinary team researching the science and technology of electric motors and drives, renewable energies, and electromechanical systems. The lab explores new materials, motor topologies, and control strategies dedicated to intelligent and efficient generation and conversion of electrical energy.

• Artificial Intelligence
• Electronic Commerce
• Intelligent Agents and Multi-Agent Systems
• +6 more

Thomas Tran is a Full Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Computer Science from the University of Waterloo (2004) and a B.Sc. (Double Major in Mathematics and Computer Science) from Brandon University (1999). His research focuses on Artificial Intelligence, Electronic Commerce, Multi-Agent Systems, Trust and Reputation Modeling, and Recommender Systems. He has published over 80 refereed papers and supervised 27 graduate students (4 PhDs and 23 Masters). Education: Ph.D. in Computer Science, University of Waterloo (2004) B.Sc. (Double Major in Mathematics and Computer Science), Brandon University (1999) Research Interests: AI Applications in E-Commerce and Mobile Business Trust Establishment Models in Multi-Agent Systems Recommender Systems and Deep Learning Clinical Data Analysis for Hidradenitis Suppurativa Awards: Governor General's Gold Medal (2004) NSERC Postgraduate Scholarships (PGS A/B) AAAI Doctoral Consortium Participant (2002) Advising and Grants: Supervised 27 graduate students Recipient of multiple research grants (details unspecified) Labs/Teams: Active in AI and E-Commerce research groups within EECS.

• Behavioural Toxicology
• Environmental Health
• Exercise Physiology
• +7 more

Keith Tierney is a Professor in the Department of Biological Sciences at the University of Alberta's Faculty of Science. He holds degrees including BSc, MSc, MBA, and PhD. His research focuses on chemical-vertebrate interactions, emphasizing sensory physiology, exercise physiology, and toxicology. Key areas include olfactory mechanisms in fish, climate-driven physiological adaptations, and contaminant impacts on animal behavior. His work integrates ecological and molecular approaches, often involving zebrafish and Arctic fish species. Education: BSc MSc MBA PhD Research Interests: Neuroendocrine regulation of behavior and toxic responses Climate change effects on fish migration and exercise capacity Contaminant impacts on sensory systems and ecosystem dynamics Zebrafish models for studying aging and neurodegenerative diseases Teaching: Zoology 241 (Animal Physiology) Biology 341 (Ecotoxicology) School of Public Health 522 (Principles of Toxicology) Funding & Collaborations: Supported by Fisheries and Oceans Canada, Environment Canada, petrochemical companies, and food manufacturers Research emphasizes translational science linking basic physiology to environmental policy Labs & Teams: Led a multidisciplinary lab investigating chemical-vertebrate interactions Collaborates internationally on projects addressing global climate and pollution challenges

Ryan HenryView profile

Assistant Professor

• Applied Cryptography
• Secure Systems
• Privacy-Preserving Technologies
• +3 more

Ryan Henry is an Assistant Professor in the Department of Computer Science at the University of Calgary. His research focuses on applied cryptography, emphasizing the development of secure systems that prioritize user privacy. His work spans designing privacy-enhancing technologies, implementing cryptographic protocols, and analyzing number-theoretic attacks on cryptographic assumptions. He also explores theoretical aspects of cryptographic efficiency and practical deployment challenges. While specific educational background details are not provided in the text, his research contributions highlight expertise in cryptography, secure systems, and privacy-preserving technologies. His work has addressed topics such as Private Information Retrieval (PIR), secure messaging, and blockchain privacy. Key research interests include: Secure Multiparty Computation Privacy-Preserving Data Access Efficient Cryptographic Protocols Zero-Knowledge Proofs IoT Security Cryptocurrency and CBDC Design His recent publications emphasize advancements in distributed systems security, privacy in recommendation systems, and cryptographic efficiency. Notable contributions include the Grotto and Duoram frameworks for secure computation, and proposals for Canadian CBDC frameworks. Despite extensive research output, no scientific awards or grants are explicitly mentioned in the provided text. Collaborations and lab affiliations are not detailed, though his work suggests involvement in interdisciplinary projects on privacy and security technologies.

Bernhard JennyView profile

Associate Professor

• Geospatial Data Visualization
• Cartography
• Computer Graphics
• +7 more

Bernhard Jenny is an Associate Professor at Monash University's Faculty of Information Technology, specializing in immersive visualization and geospatial data. He holds a PhD in Cartography from ETH Zurich and has held previous roles at Oregon State University and RMIT University. His research focuses on virtual reality (VR), augmented reality (AR), and cartographic innovations for geospatial data representation. Education: Doctor of Sciences in Cartography, ETH Zurich (2010) Postgraduate Certificate in Computer Science, ETH Zurich (2005) Master of Science in Surveying, EPFL (2000) Research Interests: Combines cartography, computer graphics, and human-computer interaction to explore VR/AR applications for geospatial data. Current work includes immersive analytics, terrain visualization, adaptive map projections, and storytelling with geospatial data. Recent Articles: Focus on ambient occlusion for terrain shading, grammars for immersive visualization transitions, and AR/VR interfaces for spatial data. Awards: Henry Johns Award (2007, 2011, 2012) ETH Medal (2010) Best Paper Honorable Mentions (ACM CHI, DIS) Grants & Projects: Leads initiatives like the 'Immersive Analytics' extension and 'National Geographic Relief Shading' project. Collaborates on neural networks for cartographic relief shading and sustainable development goals (SDGs). Labs & Teams: Heads the Embodied Visualisation Lab at Monash, focusing on immersive analytics and geovisualization tools.

Ariadne Justi BertolinView profile

Lecturer

• Control Theory
• Nonlinear Systems
• Robust Control
• +12 more

Ariadne Justi Bertolin is a Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Centre for Sustainable Energy Systems (SES) and The Foundry: Centre for Digital, Manufacturing & Design. She is actively involved in research and is currently accepting doctoral students. Her research focuses on control theory, particularly in the areas of nonlinear systems, robust control, and stability analysis. Key topics include Lur'e systems, output feedback, Zames-Falb multipliers, and LMI-based design methods. She applies these theories to autonomous systems, energy systems, transportation, and industrial automation. The recent publications show a strong trend in developing LMI-based algorithms for stability and control synthesis of both continuous-time and discrete-time nonlinear systems. Her work emphasizes robustness, performance guarantees (e.g., ℒ2-gain), and practical implementation using finite impulse response and noncausal multipliers. She has been the Principal Investigator on a project titled 'Improving stability and control synthesis conditions for nonlinear systems,' indicating leadership in her research domain. While no formal awards are listed, her publications in IEEE and IFAC journals reflect high-quality contributions to control engineering. Dr. Bertolin advises doctoral students and is engaged in collaborative research, particularly with scholars like G. Valmorbida, R. C. L. F. Oliveira, and P. L. D. Peres. Her work is supported by ongoing research projects and contributes to the theoretical foundations of modern control systems. She is affiliated with key research centers at Bath, including the Centre for Sustainable Energy Systems and The Foundry, which focus on digital manufacturing, sustainable energy, and design innovation.

• Power Electronics
• Control Systems
• Renewable Energy Conversion
• +7 more

Jose Miguel Espi Huerta is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is an active researcher in power electronics and control systems, contributing significantly to grid-connected converters, renewable energy integration, and digital control techniques. His research interests include: Power Electronics and Inverter Control Predictive and Robust Control Strategies Renewable Energy Systems (Photovoltaic and Wind) Induction Heating Technologies Remote and Web-Based Educational Labs The analysis of his recent publications reveals a strong focus on improving the efficiency and reliability of grid-connected power converters using advanced control methods such as predictive current control and MPPT strategies. His work spans both industrial applications and academic education, particularly in developing remote laboratory platforms for control systems. Scientific awards and honors: No awards listed in the provided text. He has supervised academic theses and is affiliated with the LEII (Laboratory of Industrial Electronics and Instrumentation) research group. While no formal grants are listed, his extensive publication record indicates sustained research activity. He has contributed to the development of educational tools such as air levitation systems accessible via PLC and web interfaces, promoting innovative teaching methods in engineering education. The LEII research group focuses on industrial electronics, instrumentation, and power systems, providing a collaborative environment for applied research in energy conversion and control technologies.

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +4 more

Özlem Özgöbek is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU). Her research spans artificial intelligence, machine learning, and recommender systems with a focus on privacy, fake news detection, and educational technology. NTNU - Department of Computer Technology and Informatics Her work explores multimodal fake news detection, privacy implications in recommender systems, and technology-enhanced classroom interaction. Recent publications analyze digital education trends and classroom tools. Özgöbek collaborates with international researchers and contributes to news recommendation workshops. Her projects address ethical AI, environmental sustainability, and real-time information processing.

Philip E. ProtterView profile

Professor

• Mathematical Finance
• Stochastic Integration Theory
• Stochastic Differential Equations
• +10 more

Philip E. Protter is a Professor of Statistics at the Faculty of Arts and Sciences , Columbia University. He is affiliated with the Financial and Business Analytics Center and serves as an Adjunct Professor in related programs. His research spans Mathematical Finance (capital asset pricing, derivatives pricing, liquidity, financial bubbles, insider trading, high-frequency trading, credit risk) and stochastic processes (stochastic integration, SDEs, backward-forward SDEs, Markov processes, filtering theory). He has contributed to both theoretical and applied domains, including numerical methods for stochastic equations. Protter has authored or co-authored two textbooks and two research books . He has held editorial leadership roles, including Editor-in-Chief of Stochastic Processes and their Applications and associate editorships for nine journals. Scientific Awards : Fulbright Distinguished Chair (2007) at the University of Paris (Dauphine) Fellow of the Institute of Mathematical Statistics (IMS) Two "Best Teacher" awards Invited lectures: R. Von Mises (2007), Bullitt (2008), Lundis de la Connaissance (2009) He has been a visiting scholar at numerous institutions globally and maintains active collaborations in financial mathematics and stochastic analysis.

Guido ZucconView profile

Professor

• Information Retrieval
• Health Search
• Formal Models of Search
• +15 more

Guido Zuccon is a Professorial Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland (UQ), where he leads the Information Engineering Lab (ielab) . He serves as the AI Director for the Queensland Digital Health Centre (QDHeC) and is an Affiliate Professor at the UQ Centre for Health Services Research . He was previously a Lecturer and Senior Lecturer at Queensland University of Technology and a Postdoctoral Fellow at CSIRO. His research spans Information Retrieval , Health Search , Formal Models of Search , and Health Data Science , with a strong focus on consumer health search, cohort identification, clinical decision support, and systematic review automation. He has pioneered work on search interaction, semantic models, and the evaluation of retrieval systems in health contexts. His recent publications highlight a strong trend toward leveraging large language models (LLMs) for zero-shot retrieval, federated search, dense retrieval, and query formulation. His work integrates advanced neural methods with practical applications in healthcare, including systematic review automation and clinical AI. He frequently publishes at top venues such as SIGIR, ECIR, and WSDM, often in collaboration with key researchers like Bevan Koopman, Shengyao Zhuang, and Harry Scells. ARC DECRA Fellow (2018–2020) Best Paper Awards at AIRS 2017, CLEF 2016, ALTA 2015, ECIR 2012 Best Reviewer Award at ECIR 2014 Principal Investigator on ARC Discovery Projects and MRFF grants Guido Zuccon actively supervises a large cohort of PhD students, primarily in areas related to neural information retrieval, health search, and systematic review automation. He has led significant research projects funded by the ARC, Google, Microsoft, GRDC, and CSIRO. He is a key organizer of international evaluation labs such as the CLEF eHealth Consumer Health Search task and the TREC 2019 Decision Track. He leads the ielab , a vibrant research group focused on information retrieval and data science, and contributes to major open-source initiatives like Big Brother , a tool for logging user interactions in web studies.

David A. JohnsView profile

Professor

• Analog Integrated Circuits
• High-Speed Data Communications
• Data Converters
• +15 more

David A. Johns is a Full Professor at the Department of Electrical and Computer Engineering within the University of Toronto . His career spans academic and industrial contributions to analog integrated circuits and high-speed communication systems. Co-founder of Snowbush Microelectronics (integrated circuit IP company) IEEE and Canadian Academy of Engineering Fellow Recipient of Darlington Best Paper Award and multiple teaching/design accolades Research Interests include analog integrated circuits for digital communication, data converters, phase-locked loops, and signal processing. His work bridges theoretical concepts with hardware implementation, focusing on full-duplex coax/twisted-pair systems, optical wireless links, and MEMS sensor interfaces. Teaching Contributions cover analog/digital electronics courses like ECE331, ECE354, ECE1371, and ECE1392. He emphasizes hands-on design and simulation tools like LTSpice. Awards & Recognitions : IEEE Fellow (2001) CAE Fellow (2012) Darlington Best Paper Award (1999) University of Toronto ECE Teaching Award (1999) CMC Design Award (1993) Advising involves 19 PhD/MASc theses on topics ranging from oversampling filters to low-power ADCs. His group has produced innovations in continuous-time modulators, MEMS accelerometers, and optical interconnects.

Jilles VreekenView profile

Professor

• Causal Inference
• Unsupervised Learning
• Exploratory Data Analysis
• +7 more

Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.

Emese LazarView profile

Professor

• Risk Measurement
• Model Risk
• Financial Econometrics
• +4 more

Professor Emese Lazar serves as Professor of Finance and Deputy School Director of Teaching and Learning at the ICMA Centre, Henley Business School, University of Reading. She joined the institution in 2005 and is an active member of the Econometrics with Data Science research cluster, focusing on quantitative finance applications. Her educational background includes: PhD in Finance from the University of Reading BSc in Finance and Banking from the University of Economic Studies, Bucharest BSc in Computer Science from the University of Bucharest Research interests span risk measurement and management , model risk , financial econometrics , derivatives pricing , green finance , climate risk in finance , and machine learning applications . Her work bridges theoretical finance with practical risk management challenges, particularly in climate-related financial risks and algorithmic risk modeling. Recent publications reveal a pronounced shift toward integrating climate risk metrics with traditional financial models and developing neural network-based forecasting for tail risk measures. Her research demonstrates consistent innovation in volatility modeling, model risk quantification, and climate finance applications across top-tier journals. Professor Lazar teaches postgraduate modules in Market Risk and Climate Change and Risk Management, supervising PhD students in her specialized research areas. She actively contributes to the Econometrics with Data Science research cluster, fostering interdisciplinary collaboration between finance, data science, and climate risk modeling.

Flavio Bezerra CostaView profile

Assistant Professor

• Smart Grid
• Renewable Energy Systems
• Power System Protection
• +10 more

Flavio Bezerra Costa serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University's College of Engineering. His research focuses on critical areas of modern power systems, including smart grid technologies, renewable energy integration, power system protection, and advanced applications of signal processing and artificial intelligence in electrical power networks. Dr. Costa's research interests span a comprehensive range of power system topics with particular emphasis on Smart Grid technologies, Integration of Renewable Energy Systems, Power System Protection, Control, and Monitoring, Power Quality analysis, Power Systems and Power Electronics, AC/DC Microgrids, High-Voltage Direct Current (HVDC) Electric Power Transmission Systems, and the application of Signal Processing and Artificial Intelligence (including Machine Learning) in power systems. His work bridges traditional power engineering with modern computational techniques to address contemporary grid challenges. Analysis of Dr. Costa's recent publications reveals a consistent focus on wavelet transform applications for power system protection and monitoring, particularly in the areas of fault detection, classification, and location. His research demonstrates strong integration of machine learning techniques with traditional power system protection methods, with significant contributions to transformer protection, transmission line fault analysis, and microgrid stability. The work shows an evolving trajectory from fundamental wavelet-based protection techniques toward more sophisticated AI-enhanced approaches for modern power grid challenges. Dr. Costa maintains an active research program with numerous publications in top-tier IEEE journals and conferences, demonstrating his significant contributions to the field of power systems engineering and protection.

Hongsheng HuView profile

Lecturer

• Data Privacy
• Machine Unlearning
• Trustworthy Machine Learning
• +5 more

Dr. Hongsheng Hu is currently a Lecturer in the School of Information and Physical Sciences at the University of Newcastle, Australia, specializing in the Data Science and Statistics focus area. Prior to this position, he served as a Postdoc Research Fellow at CSIRO's Data61 from October 2022 to August 2024. His academic journey includes a Doctor of Philosophy in Computer Systems Engineering from the University of Auckland in New Zealand, establishing his foundation in advanced computing systems. Dr. Hu's research centers on enhancing the trustworthiness of machine learning systems, with particular emphasis on identifying critical privacy vulnerabilities within machine learning models and developing robust defensive strategies. His work spans several key domains including adversarial machine learning (30% focus), statistical data science (30% focus), and data and information privacy (40% focus). He investigates membership inference attacks, machine unlearning techniques, and privacy-preserving mechanisms in federated learning environments. His research addresses fundamental challenges in AI security, exploring how machine learning models can be compromised through sophisticated privacy attacks and developing methods to mitigate these vulnerabilities while maintaining model utility. Analysis of Dr. Hu's publication record reveals a strong research trajectory focused on machine learning security and privacy. His work consistently addresses vulnerabilities in machine learning systems, particularly examining membership inference attacks, machine unlearning mechanisms, and privacy-preserving techniques in federated learning. The research spans top-tier venues including IEEE Security & Privacy, USENIX Security, NDSS, NeurIPS, IJCAI, AAAI, and WWW, demonstrating both technical depth and recognition by the research community. His publications show an evolving focus from foundational privacy attacks to developing more sophisticated unlearning techniques and robust defense mechanisms, with increasing citation counts indicating growing impact in the field. Active Program Committee member for USENIX Security, NDSS, ICLR, IJCAI, WWW, ICDM, ECML, and PKDD Invited reviewer for IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (IPAMI), and ACM Computing Surveys (CSUR) Dr. Hu currently serves as Course Coordinator for STAT6020 and STAT2020 Predictive Analytics at the University of Newcastle. As an academic supervisor, he co-supervises one PhD student working on 'Identifying and Mitigating Vulnerability in Recommender Systems' at Macquarie University. His research collaborations span multiple countries, with significant publication counts in Australia (18), China (12), New Zealand (10), and the United States (7), reflecting an active international research network focused on AI security challenges.