Find Academic Supervisors

• Gender-Based Violence Prevention
• Social Policy and Legislation
• Systems Change and Organizational Culture
• +5 more

Lana Wells is an Associate Professor at the University of Calgary's Faculty of Social Work and holds the Brenda Strafford Chair in the Prevention of Domestic Violence. As Founder/Director of Shift: Primary Prevention Research Hub and Research Fellow at the School of Public Policy, she specializes in preventing gender-based violence through systems change, policy innovation, and community engagement. Key research areas: Gender equity, Indigenous sovereignty, AI ethics for social work, and male allyship in violence prevention Led over $11M in research funding and 22.5M+ in prevention initiatives 2023 Killam Professor Award recipient Recent publications focus on AI applications for violence prevention, gamification in male engagement, and rural/small-town violence reduction strategies. Her work spans interdisciplinary collaborations with public health agencies, Indigenous communities, and international organizations. Major projects: Alberta's Primary Prevention Playbook, Strong Women's Circle with Indigenous communities, ConnectED Parents virtual intervention Active in policy advisory roles for governments and NGOs Scientific awards include: 2023 Killam Annual Professor Award 2022 Queen Elizabeth II Platinum Jubilee Medal 2019 Alberta Inspiration Award 2015 & 2013 Carthy Foundation Leadership Awards 2012 School of Public Policy Fellowship As a graduate supervisor in Social Work and Public Policy, she develops prevention workforce capacity through Shifttolearn.com. Current initiatives include data-driven dashboards for real-time violence prevention and cultural transformation in male-dominated institutions.

Annouchka BayleyView profile

Associate Professor

• Posthumanism
• New Materialism
• Arts-Based Educational Research
• +6 more

Annouchka Bayley is an Associate Professor at the Faculty of Education, University of Cambridge, specializing in posthuman pedagogy, new materialist philosophies, and artistic research methodologies. She chairs the Arts and Creativities Research Group and previously directed the Arts, Creativities and Education MPhil programme (2021-24) and led the Doctoral Research Training Programme. PhD from University of Warwick BA Hons from SOAS MRes from University of Warwick Her research explores: Posthuman and new materialist approaches to education Decolonial epistemologies for educational development Postqualitative research practices in higher education Digital pedagogy and augmented reality applications Embodied and affective learning methodologies Recent publications examine diffractive knowledge production, digital materiality, and postpandemic educational futures. Key collaborative works include: Diffracting New Materialisms (2023, Palgrave Macmillan) Posthuman Pedagogy (2018, Parallax journal) Current teaching focuses on: Postgraduate courses in Arts, Creativities and Education Doctoral research training Advanced research methods Labs/Teams: Arts and Creativities Research Group CRiCLE Network (Creativities in Education) Diffractive Methodologies Collective

Oliver DiesselView profile

Associate Professor

• Reconfigurable Hardware
• Computer Architecture
• Digital Systems
• +6 more

Associate Professor Oliver Diessel is a faculty member at the University of New South Wales (UNSW) within the School of Computer Science and Engineering. He has been actively teaching and conducting research at UNSW since at least 2000, with teaching experience spanning various computer architecture and digital systems courses. Dr. Diessel completed his education at the University of Newcastle, Australia, earning a B.E. (Computer, Hons) and B.Math. in 1991, followed by a PhD in 1998. His academic career has focused on reconfigurable computing systems and computer architecture. His primary research interests center around the design, test, and implementation of digital systems in reconfigurable logic devices called Field-Programmable Gate Arrays (FPGAs). Specifically, he focuses on dynamically reconfigurable digital systems where circuits can be modified while the system is operational. His work aims to develop architectures, design methods, and tools that enhance the benefits and reduce the costs of reconfigurable systems. Current research projects include fault-tolerant FPGA-based systems for space applications and fine-grained accelerators for heterogeneous devices. Dr. Diessel has published extensively with 2 books, 1 book chapter, 16 journal articles, 67 conference papers, 1 edited conference proceedings, and 7 conference presentations to his credit. His publications reflect a strong emphasis on reconfigurable technology, computer architecture, and electronic design automation. As a supervisor, Dr. Diessel currently mentors two MPhil students: Tong Wu working on Runtime reconfiguration and Junning Fan working on Fault-tolerance of FPGA-based applications. He has previously supervised six students to completion. His supervision interests include reconfigurable technology and systems, electronic design automation, and computer architecture. Dr. Diessel is an active member of the professional community, serving on the Editorial Board of ACM Transactions on Reconfigurable Technology and Systems and as an IEEE Member. He has taught numerous courses including Configurable Systems (COMP4601), Computer Architecture (COMP3211, COMP4211), Digital Systems (COMP3222), and FPGA Implementation of Digital Systems using Verilog.

Kin Wai Michael SiuView profile

Professor

• Public Design
• Inclusive Design
• Participatory Design
• +5 more

Kin Wai Michael Siu is the Eric C. Yim Professor in Inclusive Design and Chair Professor of Public Design at The Hong Kong Polytechnic University (PolyU) . He founded the Public Design Lab and directs two joint research centers: the PolyU-HIT Inclusive Environment Center and the PolyU-WUT Ship/Marine Aesthetic Design Lab . His roles include PhD/postdoc supervision, coordination of design research methods, and extensive editorial/advisory board memberships. PhD, The Hong Kong Polytechnic University PhD, University of Bath MSc, City University of Hong Kong MA, The Hong Kong Polytechnic University MEd, The University of Hong Kong BA(Hons), Loughborough University of Technology A leading figure in inclusive design, Siu integrates Vygotsky's ZPD and Bruner's scaffolding theory into his 'Scaffolding Innovation' framework. His work balances creative design with technological application, focusing on inclusive public spaces (playgrounds, country parks, recycling facilities) and pandemic prevention strategies (e.g., COVID-19/NID mitigation through design ). Recent projects explore AI-enhanced recycling , bioinspired wearables , and cultural heritage semiotics . His publications span design education , participatory design , and cross-cultural heritage preservation , with a fingerprint highlighting 'Public Design', 'Inclusive Design', 'Visually Impaired', 'Cultural Semantics', and 'Spatial Practice'. Key collaborators include researchers from Hong Kong, China, and international institutions. Scientific Awards : 2024 G Cross Award for Excellent Guidance (Instructor) 2024 Hong Kong Contemporary Design Awards for Exceptional Advisor Multiple international design/invention awards (over 100 total) 2008 Romanian Ministry Invention Awards for Rapid Demountable Platform (RDP) Awarded over 50 patents, Siu has secured numerous external grants for public design innovations. His Public Design Lab collaborates with institutions like Tsinghua University, MIT, and Cambridge, while his editorial roles span design, engineering, and education journals.

Mingyu DingView profile

Assistant Professor

• Robotics
• Robot Learning
• Embodied AI
• +4 more

Mingyu Ding is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges robotics, embodied AI, and computer vision, focusing on building agents that interact effectively with physical environments. PhD in Robotics, University of Hong Kong (2022), advised by Ping Luo Postdoctoral Fellow, BAIR@UC Berkeley (with Masayoshi Tomizuka) Visiting Scholar, CSAIL@MIT (with Joshua Tenenbaum) B.S. in Computer Science, Renmin University of China (under Zhiwu Lu) His work emphasizes robot learning through physical simulation, multimodal foundation models, and self-supervised methods. Key contributions include Embodied Concept Learner (ECL) and Sparse Diffusion Policy frameworks. Recent publications highlight trends in 3D vision, diffusion-based planning, and language-driven robotic behavior synthesis. Awards include ICRA Best Paper (2024), ME Rising Star (2023), and CVPR Doctoral Consortium (2023). Session Chair for ICRA 2025 Associate Editor for IROS 2025 Guest Editor for Robotics Special Issue: Embodied Intelligence

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +7 more

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

• Digital Transformation
• Healthcare Informatics
• Platforms & Innovation
• +5 more

Tan Chuan Hoo is an Associate Professor (tenured) and Deputy Head of the Department of Information Systems and Analytics at the National University of Singapore's School of Computing. With a distinguished academic career spanning multiple continents, he brings expertise in digital transformation, healthcare informatics, and enterprise systems to his teaching and research. His educational background includes: B.Sc. (1st Class Honours, National University of Singapore) M.Sc. (Accelerated, National University of Singapore) Ph.D. (National University of Singapore) Professor Tan's research focuses on digital transformation, particularly designing, deploying, and evaluating technological innovations. His work centers on two critical areas: digital commerce (provision of digital services such as online shopping aids) and digital organization (ensuring operational efficiency and performance). His research has significant implications for healthcare institutions, corporations, and crisis preparedness and response organizations. He conducts comprehensive analyses using various scientific methodologies including field experiments and mixed methods to understand how digital technologies reshape business operations and enhance societal well-being. His publication record shows a consistent focus on information systems, with recent articles (2020-2025) emphasizing healthcare informatics, digital transformation, and open innovation. His work demonstrates a clear trajectory toward understanding how technology intersects with organizational effectiveness and societal implications, with increasing attention to healthcare applications and digital crisis management. Professor Tan has received numerous prestigious awards for his contributions to the field: Faculty Teaching Excellence Award, NUS (2024, 2017) Information Management Research Award, China Information Economics Society (2023) Reviewer Hall of Fame, Journal of AIS (2020) Outstanding Associate Editor Award, MIS Quarterly (2016) Best Reviewer Award, Journal of AIS (2016) INFORMS ISS Design Science Award (2013) Honorable Mention, Journal of AIS (2015) He has successfully advised PhD students and collaborated with public and private entities on research projects. His editorial service as Associate Editor for Information Systems Research and MIS Quarterly, along with board memberships at other leading journals, demonstrates his significant influence in the field. Professor Tan has secured research grants supporting projects on digital crisis preparedness, disaster response technology, and healthcare digitalization. His research group focuses on understanding how technology can be designed and implemented to support organizations in crisis situations, enhance healthcare services, and improve digital commerce. Current projects include examining digital crisis management, technology for disaster response, and the digital transformation of healthcare services.

Wenhua ZhaoView profile

Associate Professor

• Offshore Hydrodynamics
• Renewable Energy Technologies
• Wave-Structure Interactions
• +12 more

Associate Professor Wenhua Zhao is a globally recognized expert in offshore hydrodynamics and renewable energy technologies at The University of Queensland , School of Civil Engineering. With over 110 publications and 30 million AUD in secured research funding, his work bridges theoretical and practical advancements in marine engineering. Research focuses on Clean Energy , Artificial Intelligence , and Climate Change , specifically floating wind energy, floating solar, offshore aquaculture, and green hydrogen production. His 15 most recent articles (2024-2025) emphasize wave-structure interactions, gap resonance dynamics, and AI-driven wave prediction, published in top journals like Journal of Fluid Mechanics and Ocean Engineering . Scientific awards include the prestigious ARC Future Fellowship (2024-2028) and DECRA Fellowship (2019-2022) , recognizing his contributions to academia and industry. He teaches the 'Design of Offshore Energy Systems' course , training hundreds of students in coastal and ocean engineering, and serves as Deputy Editor for Ocean Engineering and Associate Editor for ASME's Journal of OMAE . Available for research supervision, Zhao actively collaborates with editorial boards of Applied Ocean Research and other Q1 journals.

Stephen MeisenbacherView profile

Researcher

• Privacy-preserving Natural Language Processing (NLP)
• Differential Privacy
• Hybrid, Expert-Driven Classification Systems
• +5 more

Stephen Meisenbacher is a Research Associate at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Department of Computer Science, I19 . He has been part of the Software Engineering for Business Information Systems (SEBIS) chair since March 2022. His research focuses on Privacy-Preserving Natural Language Processing (NLP) , Differential Privacy , and Privacy-Enhancing Technologies (PETs) , with a particular interest in their integration into software development and business applications. His work also explores Hybrid, Expert-Driven Classification Systems and Usable Privacy solutions. Stephen’s recent publications address trends in AI Privacy Risks , Text Rewriting with DP , Legal AI Use Cases , and Data Protection Compliance . He has contributed to GDPR-related research and PETs adoption in small enterprises. His teaching includes Natural Language Processing seminars and Software Engineering lecture courses for Master’s and Bachelor’s students at TUM. He also organizes Entrepreneurship for Small Software-Oriented Enterprises seminars. Stephen holds a Master’s in Informatics from TUM (DAAD Graduate Scholarship) and a Bachelor’s in Computer Science from the University of Notre Dame, with additional studies in German Language and Literature. Contact: stephen.meisenbacher@tum.de | LinkedIn

Stefania DumbravaView profile

Associate Professor

• Formal methods for databases
• Graph databases
• Property graphs
• +7 more

Stefania Dumbrava is an Associate Professor of Computer Science at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise) and a permanent member of the ACMES team in the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. She is also actively involved in the Property Graph Schema Working Group and the European Research Network on Formal Proofs (EuroProofNet). Education PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Interests Dumbrava's research lies at the intersection of formal methods and data management . She designs and verifies algorithms and systems for graph databases , with emphasis on property graphs , schema discovery , query optimization , and distributed graph processing . Recently, her work focuses on certifying large-scale distributed graph systems under the ANR JCJC VERDI project (2025–2029). Awards & Honors SIGMOD Best Paper Award 2023 – “PG-Schema: Schemas for Property Graphs” SIGMOD Research Highlight Award 2023 – “Threshold Queries” VLDB 2022 Best Regular Research Paper Runner-Up – “Threshold Queries in Theory and in the Wild” SIGMOD 2025 Distinguished Reviewer Award ICDE 2025 Best Program Committee Member Award EASST Best Software Science Paper Award, ICGT 2025 Students & Grants Dumbrava has supervised numerous research interns and is actively recruiting PhD students for her ANR VERDI project on verified foundations of large-scale distributed graph systems. She has also served on six PhD thesis committees as examiner since 2021. Labs & Teams She leads the ACMES research group within the SAMOVAR laboratory (Télécom SudParis, Institut Polytechnique de Paris), where her team develops formally verified graph-database engines and tools such as GRASP, VerDILog, and DatalogCert.

Douglas H. WernerView profile

Professor

• Computational Electromagnetics
• Antenna Theory and Design
• Metamaterials
• +7 more

Douglas H. Werner is the John L. and Genevieve H. McCain Chair Professor in the Department of Electrical Engineering at Pennsylvania State University's College of Engineering. He directs the Computational Electromagnetics and Antennas Research Lab (CEARL) and holds a faculty position at the Materials Research Institute (MRI). His education includes B.S., M.S., and Ph.D. degrees in Electrical Engineering, along with an M.A. in Mathematics, all from Pennsylvania State University. Werner's research encompasses computational electromagnetics, antenna systems, metamaterials, and AI-driven electromagnetic design. Current work focuses on developing deep learning techniques for rapid simulation and inverse-design in electromagnetics/optics. Key areas include: Advanced computational methods (FDTD, FEM, MoM) Next-generation antenna systems (wearable, reconfigurable, RFID) Metamaterial physics and transformation optics Evolutionary optimization algorithms Awards and honors include: IEEE Antennas and Propagation Society Educator Award (2019) DoD Technical Achievement Award (2018) R.W.P. King Paper Award (2006) Fellowships in 5 professional societies 14 additional research/teaching awards He leads CEARL research group, holds 20 patents, and has supervised numerous graduate students. His publication record includes 900+ papers and 6 books.

Claire DonnatView profile

Assistant Professor

• Statistics
• Machine Learning
• Graph Neural Networks (GNNs)
• +7 more

Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.

• Cognitive Science
• Behavioral Economics
• Decision Making
• +3 more

Professor Nick Chater is a leading figure in the field of Behavioural Science, affiliated with the University of Warwick at Warwick Business School since 2010. He has held previous chairs in psychology at Warwick and UCL. His research spans cognitive and social foundations of rationality, with applications to business and public policy, and he has authored over 200 papers and six books. He is a fellow of the British Academy, Cognitive Science Society, and Association for Psychological Science. His research interests include cognitive science, behavioral economics, decision making, and computational psychology, with a focus on reasoning, language, and mathematical modeling of mental processes. He has been recognized with prestigious awards such as the Spearman Medal, Experimental Psychology Society Prize, and the David E Rumelhart Prize for lifetime achievement in cognitive science. His work extends to practical applications through co-founding Decision Technology and advising the UK government's Climate Change Committee and the Behavioural Insight Team. Recent publications highlight his interdisciplinary approach, bridging economics, cognitive science, computational modeling, and behavioral public policy. Topics include thermal macroeconomic theory, Bayesian sampling, paradoxes in cognition, and language emergence via social interaction. These works emphasize probability judgments, moral cognition, and computational limitations in human inference. British Psychological Society's Spearman Medal (1996) Experimental Psychology Society Prize (1997) David E Rumelhart Prize (2023) PROSE Award (2019) Chater's academic contributions include collaborations with researchers like Adam N. Sanborn, Hossam Zeitoun, and Morten H. Christiansen, focusing on Bayesian inference, behavioral public policy, and cognitive modeling. He has also been a resident scientist on BBC Radio 4's The Human Zoo.

Vincent FortuinView profile

Assistant Professor

• Bayesian Deep Learning
• Uncertainty Quantification
• Probabilistic Inference
• +5 more

Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.

Massimo MischiView profile

Professor

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +4 more

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.