Find Academic Supervisors

• Scientific Visualization
• Data Visualization
• Data Science
• +5 more

Min Chen is a Professor of Scientific Visualization at the University of Oxford, affiliated with the Department of Engineering Science and Pembroke College. He holds fellowships from the British Computer Society, European Computer Graphics Association, and Learned Society of Wales. His career spans over three decades, with previous roles at Swansea University (1984–2011) and current leadership in visualization research. His research focuses on visualization theory, video visualization, visual analytics, and interdisciplinary applications in fields like epidemiology and cybersecurity. He has authored over 200 publications and led projects such as RAMPVIS during the COVID-19 pandemic. Key roles include editor-in-chief of Computer Graphics Forum and associate editor of IEEE Transactions on Visualization and Computer Graphics. Education: BSc and PhD in relevant fields (details not explicitly stated in texts). Awards include the VGTC Visualization Lifetime Achievement Award (2024). His work emphasizes the theoretical underpinnings of visualization and practical tools for data intelligence.

Ralf SchlüterView profile

Lecturer

• Automatic Speech Recognition
• Machine Learning
• Pattern Recognition
• +4 more

Ralf Schlüter is a senior researcher and Lecturer (Privatdozent) at RWTH Aachen University, where he leads the Automatic Speech Recognition Group within the Department of Computer Science. His academic roles include serving as Academic Director and leading research initiatives at Lehrstuhl Informatik 6: Human Language Technology and Pattern Recognition. He holds a Dipl. in Physics (1995), a Dr. rer. nat. in Computer Science (2000), and a Habilitation in Computer Science (2019), all from RWTH Aachen University. His research focuses on advanced speech recognition technologies, including end-to-end architectures, acoustic modeling, discriminative training, and machine learning applications. He has led numerous national and international projects, such as EU-funded initiatives like BABEL, BOLT, and GALE. Schlüter has published extensively in top-tier conferences like Interspeech and ICASSP, contributing to advancements in neural network frameworks, feature extraction, and error analysis. He teaches courses on speech recognition, pattern recognition, and signal processing, and serves as a Subject Editor for Speech Communication . His work also involves developing the RWTH ASR toolkit, emphasizing scalable and efficient speech recognition systems. Schlüter’s contributions to speech technology have earned recognition, including the ISCA Best Student Paper Award (2019).

Kerstin BunteView profile

Professor

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +7 more

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

Michael BurkeView profile

Senior Lecturer

• Robotics
• Computer Vision
• Machine Learning
• +3 more

Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne

Catherine BrooksView profile

Professor

• Instructional Communication
• Online Collaboration
• Language and Identity
• +4 more

Catherine Brooks serves as Professor and Interim Dean of the College of Information Science at the University of Arizona, having previously held leadership roles as associate director (2016-2018) and director (2019-2023). She founded the Center for Digital Society and Data Studies, establishing herself as a key institutional leader in digital society research. Her academic foundation includes a PhD from the University of California, positioning her at the intersection of communication theory and information science. Research Interests Dr. Brooks investigates day-to-day language use in social contexts with emphasis on instructional communication technologies , online collaboration dynamics , and language-identity relationships . Her work examines how digital environments facilitate co-construction of knowledge, relationships, and identities while exploring science communication challenges. This inherently interdisciplinary research bridges communication studies, information science, and sociology through mixed-methods approaches. Publication Trends Recent scholarship reveals evolving focus from classroom communication (2018) to contemporary societal-technological intersections. Her 2022-2024 work increasingly addresses environmental media narratives (Arizona mining, energy transition), emerging technology ethics (quantum computing, deepfakes), and digital governance (student data privacy, algorithmic bias), demonstrating responsive engagement with urgent socio-technical dilemmas. Research Infrastructure As founder of the Center for Digital Society and Data Studies, she cultivates interdisciplinary collaboration examining digital technology's societal impact. This hub supports research on media discourse, data ethics, and communication innovation while connecting academic work with public discourse through outlets like Scientific American and Wired.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +4 more

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Garth V. CrosbyView profile

Associate Professor

• Internet of Things (IoT)
• Industrial Internet of Things (IIoT)
• Cyber-physical Systems Security
• +5 more

Garth V. Crosby is an Associate Professor at Texas A&M University's Department of Engineering Technology and Industrial Distribution within the College of Engineering. He is also affiliated with the Multidisciplinary Engineering program. His research focuses on IoT and IIoT security, cyber-physical systems, and STEM education innovation. Crosby holds a Ph.D. in Electrical Engineering from Florida International University (2007), an M.S. in Computer Engineering, and a B.S. in Electronics (Applied Physics) from the University of the West Indies. His work spans cybersecurity frameworks for emerging technologies, including blockchain-based federated learning, post-quantum cryptography, and IoT threat mitigation. He has contributed to educational advancements through online lab design and faculty efficacy studies in hybrid learning environments. Crosby's recent publications emphasize securing robotic IoT systems, supply chain blockchain applications, and ransomware evasion techniques using generative AI. His research portfolio includes over 40 peer-reviewed articles in journals like IEEE Transactions and conferences such as ASEE and FiCloud. Key themes include volunteer cloud reliability models (ProTrust), edge computing security, and educational technology evaluation. Crosby's interdisciplinary approach bridges engineering systems with pedagogical innovation, addressing both technical and human factors in modern technological challenges.

Gaurav SharmaView profile

Professor

• Data Analytics
• Cyber Physical Systems
• Signal and Image Processing
• +3 more

Gaurav Sharma is a Professor at the University of Rochester, holding joint appointments in the Department of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology. He is also a Distinguished Researcher at the Goergen Institute for Data Science and Artificial Intelligence. His research focuses on Data Analytics, Cyber Physical Systems, Signal and Image Processing, Computer Vision, Media Security, and Communications. Previously, he served as Director of the Center for Emerging and Innovative Sciences (2008-2010) and held roles at Xerox Research (1996-2003). He earned a PhD in Electrical and Computer Engineering from North Carolina State University (1996), alongside degrees from Indian institutions including the Indian Institute of Science and IIT Roorkee. Education: PhD (NCSU), M.S. Applied Mathematics (NCSU), M.S. Electrical Communication Engineering (IISc), B.Eng (IIT Roorkee) Awards: Fellow of IEEE, SPIE, and IS&T; Elected Sigma Xi member Editorial Roles: Editor-in-Chief of IEEE Transactions on Image Processing (2018-2020) and Journal of Electronic Imaging (2011-2015) Leadership: Chair of IEEE Signal Processing Society committees and SPIE/EI conferences His research spans interdisciplinary areas like medical imaging analytics, cybersecurity in IoT systems, and computational methods for biomedical applications. Recent work includes studies on cardiac mortality patterns, viral protein inhibition, and federated learning for privacy-sensitive healthcare tasks. Dr. Sharma’s contributions to academic governance include roles on IEEE publication committees and strategic planning boards, reflecting his commitment to advancing technical standards and open science practices.

David BrownView profile

Professor

• General Relativity
• Theoretical Physics
• Gravitational Dynamics
• +3 more

David Brown is a Professor in the Department of Physics at North Carolina State University (NC State), affiliated with the College of Sciences. He holds an office in Riddick Hall 319F and can be reached at jbrown@ncsu.edu. Brown earned his PhD from The University of Texas at Austin in 1985 and held postdoctoral positions at the University of Vienna, Austria, The University of Texas, and the University of North Carolina. He joined NC State in 1992 as a visiting professor, transitioning to a regular faculty position in 1995. His research focuses on general relativity, with particular emphasis on gravitational dynamics, spacetime geometry, and relativistic continuum mechanics. His work explores topics such as hyperelastic models in curved spacetime, singular Lagrangians, and numerical relativity techniques for simulating black hole systems. Key areas of interest include geodesic deviation in cosmological models, Mathisson-Papapetrou-Dixon equations for extended bodies, and the Dirac-Bergmann algorithm for constrained Hamiltonian systems. Brown’s recent publications (2021–2023) highlight advancements in elasticity theory within general relativity and the application of hyperelastic models to Schwarzschild spacetime. His earlier work (2009–2012) includes foundational contributions to numerical relativity, such as BSSN formulations and moving-puncture methods for black hole simulations. While no specific honors are listed, his extensive publication record underscores his expertise in theoretical physics and cosmology. He has advised no formally documented students in the provided materials. His research has been supported by methodologies involving both analytical and computational approaches, though specific grants are not detailed here. No affiliated laboratories or research teams are explicitly mentioned in the text.

• Security
• Embedded Systems Security
• Cyber-Physical Systems Security
• +13 more

Francesco Regazzoni is a Senior Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). His work bridges embedded systems, cybersecurity, and artificial intelligence, with a focus on securing hardware and cyber-physical systems. Research Interests: His expertise spans embedded and cyber-physical systems security, side-channel attacks, post-quantum cryptography, hardware trojans, random number generators, and the security of AI and approximate computing. He also contributes to hardware/software co-design and operating systems security. The analysis of his recent publications reveals a consistent focus on hardware and system-level security , particularly in resource-constrained environments like IoT and embedded devices. His work integrates machine learning for attack detection and applies formal methods to ensure trust in hardware. A growing emphasis is placed on securing AI systems from physical and adversarial threats. Scientific Contributions: Over 100 peer-reviewed publications One book and one patent Extensive international collaboration (Belgium, Netherlands, USA, Switzerland, Singapore) Advising and Grants: While specific advisees and grants are not listed, his leadership in funded research projects and involvement with ALaRI and IDSIA suggest active mentorship and project coordination. His work has been supported by industry (e.g., ST Microelectronics, HP), the Swiss National Foundation, and the European Union. Labs and Teams: He is part of the Graph Machine Learning Group (GMLG) at IDSIA, which evolved from the Advanced Learning and Research Institute (ALaRI). This group focuses on graph machine learning, reinforcement learning, and dynamical systems, particularly in non-stationary environments.

Christos ThrampoulidisView profile

Assistant Professor

• Machine Learning
• Deep Learning Theory
• Mathematical Optimization
• +4 more

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Vancouver. Previously, he held positions at the University of California, Santa Barbara (UCSB). His research focuses on machine learning theory, deep learning principles, mathematical optimization, and statistical signal processing. He leads the Machine Intelligence and Learning Design (MILD) group at UBC, emphasizing foundational studies of large language models, neural collapse, and high-dimensional data analysis. Education: PhD and MS in Electrical Engineering from the California Institute of Technology (Caltech), with a minor in Applied and Computational Mathematics. Undergraduate degree (Diploma) in Electrical and Computer Engineering from the University of Patras, Greece. Research interests include the implicit geometry of next-token prediction, optimization bias in neural networks, and robust generalization in overparameterized models. Key contributions include work on adversarial training, neural collapse phenomena, and safe reinforcement learning. His group actively explores theoretical frameworks for language models and develops methodologies to analyze their mechanics and limitations. Awards: NSERC Discovery Grant (2021). Teaching includes courses on optimization and signals & systems at UBC. He has advised numerous graduate students (PhD and MSc) and undergraduates, focusing on rigorous mathematical approaches to machine learning problems. Active collaborations span academia and industry, with publications in top venues such as NeurIPS, ICASSP, and AAAI. Current projects include understanding language model geometry, decentralized learning, and mitigating imbalances in training data. The group prioritizes equity and diversity, welcoming students with strong mathematical backgrounds and curiosity about foundational ML questions.

Anya Helene BaggeView profile

Associate Professor

• Programming Language Design and Implementation
• Software Language Engineering
• Semantics of Programming Languages
• +8 more

Anya Helene Bagge is an Associate Professor in the Department of Informatics at the University of Bergen, where she is a researcher at the Bergen Language Design Laboratory. Her work focuses on programming languages and software language engineering, with strong ties to education and tool development. Research Interests: Programming language design and implementation Software language engineering Semantics of programming languages Domain-specific languages and language extensions Program transformation using Rascal, MPL, and Stratego/XT Aspect-oriented programming, especially Domain-Specific Aspect Languages Her recent publications reflect a consistent focus on software language engineering, refactoring, microservice security, and programming education. Themes across her work include language design, program analysis, and the development of educational tools and methodologies. Scientific Awards: The Realist Committee's Teaching Award 2016/17 Lecturer of the Year in Computer Science (Spring 2015) Anya Bagge actively supervises students, including PhD and Master’s candidates such as Tero Hasu, Kristoffer Haugsbakk, and Nina Andersen. She has been involved in numerous academic activities, including organizing the OOPSLE workshop and serving on program committees for conferences like SLE, HILT, and WCRE. She has also taught core courses including INF101, INF225, and INF328, demonstrating a strong commitment to both research and teaching. Labs and Teams: She is affiliated with the Bergen Language Design Laboratory , collaborating with researchers such as Magne Haveraaen, Eva Burrows, and Tero Hasu.

• Machine Vision
• Supply Chain Optimization
• Optical Imaging
• +5 more

Professor Sang-Heon Lee is a faculty member at the University of South Australia, affiliated with the UniSA STEM division. His research spans interdisciplinary domains including machine vision, supply chain optimization, and material science. His research interests focus on developing advanced imaging systems for food safety (e.g., aflatoxin detection in almonds), optimizing reverse supply chains for electronic waste and solar panels, and engineering nanocomposites for construction materials. He also investigates biomedical imaging techniques for mucociliary transit assessment and clinical coding methodologies. The 15 most recent publications highlight his work in multispectral/hyperspectral imaging, fuzzy logic optimization, and nanomaterials. Key trends include non-destructive food testing, sustainable e-waste management, and biomedical imaging analysis. As a research degree supervisor, he contributes to academic training in these areas. No scientific awards or lab affiliations were explicitly mentioned in the provided text.

Sourav MedyaView profile

Assistant Professor

• Machine Learning
• Graph Mining
• Data Science
• +3 more

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). Prior to joining UIC, he was a research assistant professor at the Kellogg School of Management and the Northwestern Institute on Complex Systems (NICO) at Northwestern University. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2019) and a Master of Engineering in Computer Science and Automation from the Indian Institute of Science, Bangalore, India. Ph.D., University of California, Santa Barbara, 2019 M.Eng., Indian Institute of Science, Bangalore, India His research lies at the intersection of graphs, machine learning, and data science, with a focus on bias, fairness, and interpretability in AI. He also develops machine learning techniques with applications in healthcare, infrastructure, and computational social science. His recent publications address topics such as global counterfactual explainers for GNNs, game-theoretic node selection, and interpretable logical rule extraction from graph models. Medya has advised students in areas like multimodal learning, graph neural networks, explainable AI, and efficient AI. His scientific awards include the UIC Outstanding MS Thesis Award (2024) and Best Paper Award at the Machine Learning on Graphs (MLoG) Workshop at WSDM'23. He has reviewed for top conferences including NeurIPS, ICML, KDD, and AAAI, and recently taught courses on Machine Learning on Graphs (CS 519) and Introduction to Data Science (CS 418) at UIC.

Haim DubossarskyView profile

Lecturer

• Natural Language Processing
• Artificial Intelligence
• Historical Linguistics
• +3 more

Haim Dubossarsky is a Lecturer in Computer Science at the School of Electronic Engineering and Computer Science , Queen Mary University of London , and an Affiliated Lecturer at the Language Technology Lab , University of Cambridge. His work bridges Natural Language Processing (NLP) , Artificial Intelligence (AI) , and interdisciplinary studies in linguistics , cognitive neuroscience , and mathematical modeling . Dr. Dubossarsky's research explores semantic change and language variation through computational methods, focusing on: Detecting historical and contemporary linguistic shifts Integrating neurocognitive insights into NLP systems Developing interpretable AI models for language tasks Recent publications highlight his contributions to multilingual NLP , word-in-context (WiC) tasks , and robust semantic analysis . His work includes collaborations on hate speech detection , topological representations , and temporal modeling in language evolution. Dr. Dubossarsky leads projects funded by: UKRI AI Hub (£1,026,205, 2024–2029) RJ Riksbankens Jubileumsfond (£381,511, 2022–2027) Innovate UK (£320,887, 2024–2025) He supervises PhD students at Queen Mary's Centre for Human-Centred Computing , including Alaa Bazaid , Roksana Goworek , and Dina Pisarevskaya , working on topics like multilingual polysemy disambiguation and language variation analysis .