Find Academic Supervisors

• Applied Mathematics
• Numerical and Computational Mathematics
• Knowledge Representation and Machine Learning
• +3 more

Kevin Webster is a Senior Teaching Fellow in the Department of Mathematics at Imperial College London, within the Faculty of Natural Sciences. His research focuses on applied mathematics, numerical and computational mathematics, and machine learning, with particular expertise in dynamical systems, ordinary differential equations, and bifurcation theory. He contributes to both teaching and research, bridging theoretical mathematics with practical applications in fields like machine learning and network analysis. His research interests span areas such as symmetry transformations in neural networks, Lyapunov function approximation, and stability analysis in dynamical systems. Notably, he has developed methods for music data generation and structure induction in melodies using generative models. He also investigates the interplay between discrete geometry and graph neural networks, advancing techniques for network rewiring and optimization. Webster’s work often addresses complex systems, including bifurcation phenomena in differential-algebraic equations and stability analysis in cosmological models such as Bianchi systems. His publications reflect a deep engagement with interdisciplinary topics, combining mathematical rigor with computational innovation.

Tony ZhuView profile

Research Fellow

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +3 more

Dr. Tony Zhu is an Honorary Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on biomedical signal processing, machine learning, and medical imaging, with applications in neurology, mental health, and epidemiology. He collaborates extensively on projects involving electromagnetic brain imaging, stroke diagnosis, and predictive modeling for mental health conditions. His work spans interdisciplinary areas such as EEG analysis, wearable sensor technology, and environmental health. Recent publications highlight advancements in prenatal depression prediction, respiratory rate estimation, and stroke localization using electromagnetic methods. Dr. Zhu’s contributions address critical challenges in healthcare diagnostics and public health monitoring. Key collaborations include projects with researchers in biomedical engineering, neuroscience, and computational biology. His research emphasizes practical solutions for clinical and environmental health challenges, leveraging machine learning and advanced signal processing techniques.

• Computer Vision
• Machine Learning
• Object Detection
• +7 more

Du Huynh is an Associate Professor at the School of Physics, Mathematics and Computing, Department of Computer Science and Software Engineering, The University of Western Australia (UWA). She holds a 0.8 FTE appointment and has served as Graduate Research Coordinator since 2013. Her research focuses on Computer Vision, Machine Learning, Object Detection, and Video Analytics with applications in mineral processing, intelligent transportation systems, and medical imaging. She has secured over $3M in ARC grants and is a Chief Investigator in the Australian Centre for Quantum Growth ($18M funding). Education: BSc (Hons) and PhD in Computer Science from UWA. Prior to UWA, she lectured at Murdoch University for five years. Research Highlights Developed algorithms for automated mineral ore analysis, pedestrian trajectory prediction, and surgical instrument detection. Received three Best Paper Awards (ICPR2014, Kenneth Clarke Journal 2019, AusDM2022) and multiple award nominations. Editorial roles include PLOS One, Journal on Artificial Intelligence, and guest editorships for special issues in CVIU and Electronics. Grants & Projects Lead investigator in quantum computing algorithms for real-time optimization ($3M ARC funding). Collaborations with industry (e.g., Main Roads WA) on traffic video analytics and drone-based prediction systems. Academic Contributions Developed courses in Computer Vision, Machine Learning, and Java Programming. Supervised numerous HDR students and served on technical committees for WACV, ICCV, and DICTA conferences.

• Data Analysis
• Data Mining
• Machine Learning
• +6 more

Heikki Mannila is a Professor of Computer Science at Aalto University, with a focus on algorithms for data analysis, data mining, and machine learning. He previously held the title of Academy Professor (2004–2008) and served as Vice President for Research and Education at Aalto University (2009–2012) and President of the Academy of Finland (2012–2022). His research emphasizes the interplay between theoretical computer science and practical applications in fields like environmental science, linguistics, and paleontology. Education: Ph.D. in Computer Science, University of Helsinki, 1985 Research Interests: Algorithmic methods for data analysis and mining Machine learning applications across disciplines Interdisciplinary collaborations in paleontology, linguistics, and environmental science House of AI initiative for AI-driven multidisciplinary research Recent Work: Recent publications explore multilinear transforms, Hadamard decomposition problems, and applications in recommendation systems and environmental data analysis. His work bridges theoretical foundations and real-world problem-solving. Awards: Academy Professor of Finland (2004–2008) Grants & Labs: Initiator of the House of AI at Aalto University Past leadership roles in national research policy and funding Collaborations: Worked with institutions like TU Wien, Max Planck Institute, Microsoft Research, and Nokia Research, emphasizing cross-sectoral innovation.

Tao ChenView profile

Professor

• Product and Process Systems Engineering (P2SE)
• Computer Modelling
• Data Analytics
• +9 more

Professor Tao Chen is an academic and administrative leader at the University of Surrey, serving as Associate Vice-President (International) and Professor in the School of Chemistry and Chemical Engineering. He plays a pivotal role in advancing the university's global engagement strategy, focusing on transnational education (TNE) through collaborative projects with internal stakeholders and external partners. His responsibilities include guiding international partnerships, brand development, student recruitment, research collaboration, and mobility initiatives. He holds a PhD and is a Fellow of the Institution of Engineering and Technology (FIET). His research interests revolve around Digital Chemical Engineering, emphasizing computer modeling of chemical processes using multiscale mechanisms and machine learning. Key application areas include skin penetration, food engineering, radiotherapy dosimetry, and manufacturing processes. He also focuses on model-based chemical product design and process optimization, leveraging interdisciplinary approaches in biomedical engineering, environmental sustainability, and industrial systems. Professor Chen's recent publications highlight interdisciplinary work, including machine learning applications in spectroscopy, advanced process control methodologies, fire safety modeling, drug delivery systems, and sustainable aquaculture optimization. His research often bridges theoretical and applied domains, addressing real-world challenges through innovative engineering solutions. In terms of academic contributions, he supervises doctoral student Anukrati Goel (EPSRC-funded) and research fellow Dr. Yuqing Xia (BEIS-funded). He leads significant grants, such as the EPSRC-funded project 'Soilless cultivation for rapid bioenergy willow' (EP/X013294/1, £453,816, 2023–2025). His teaching includes Year 2 modules in Computation for Chemical Engineers and Transfer Processes, plus an MSc module in Advanced Process Control. He annually oversees 4–6 MSc dissertation projects and 1–2 MEng research projects, integrating experimental work with modeling.

D. Mitchell WilkesView profile

Associate Professor

• Digital Signal Processing
• Image Processing
• Computer Vision
• +3 more

D. Mitchell Wilkes is an Associate Professor in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. His research focuses on digital signal processing, image processing, and robotics, with applications in medical imaging, manufacturing automation, and cognitive systems. He holds a Ph.D. in Electrical Engineering from Georgia Institute of Technology (1987), an M.S. from the same institution (1984), and a B.S. from Florida Atlantic University (1981). Education: Ph.D., Electrical Engineering, Georgia Tech, 1987 M.S., Electrical Engineering, Georgia Tech, 1984 B.S., Electrical Engineering, Florida Atlantic University, 1981 Research: His work spans signal modeling, sonar systems, and robotics, with notable contributions to friction stir welding automation and neuromorphic decision-making in robots. Over 40 peer-reviewed articles highlight interdisciplinary applications in computer vision, medical imaging, and autonomous systems. Grants & Patents: He secured NSF funding for adaptive robotics control systems and holds patents in welding process control. His lab collaborations include Vanderbilt's Intelligent Systems Center and interdisciplinary medical engineering projects. Labs/Teams: Active in the Intelligent Robotics Lab and collaborates with biomedical and manufacturing teams, advancing human-robot interaction and industrial automation.

Cong GaoView profile

Professor

• Data Science
• Geospatial data management
• Spatio-temporal data mining
• +7 more

Cong Gao is a Professor and Head of the Division of Data Science at Nanyang Technological University's College of Computing & Data Science. He also holds a courtesy appointment with the School of Physical & Mathematical Sciences. Previously, he served as an Assistant Professor at Aalborg University, Denmark, and worked as a researcher at Microsoft Research Asia. He co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU). His educational background includes: Ph.D. in Computer Science from National University of Singapore (2004) Master of Engineering from Tianjin University, China (1999) Bachelor of Engineering from Tianjin University, China (1996) Professor Gao's research focuses on Data Science, with particular expertise in geospatial data management, spatio-temporal data mining, recommendation systems, and social media data analysis. His work has significantly impacted areas like spatial-textual indexing, point of interest recommendation, and mining social networks. He has published extensively in top venues including VLDB, SIGMOD, ICDE, KDD, and WSDM, with over 14,000 citations and an H-index of 61. His recent publications demonstrate strong trends in applying machine learning to database systems, with particular focus on spatial and trajectory data management. Key research directions include learned indexing techniques, trajectory data analysis, and integrating large language models with database systems for improved query optimization. Professor Gao has received notable scientific recognition including: Best paper runner-up award at WSDM'22 Best paper award runner-up at WSDM 2020 He has advised numerous students who have become significant contributors in their own right, including Xin Cao, Lisi Chen, Kaiyu Feng, and Kaiqi Zhao. His research has been supported by substantial grants from Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft, including a S$42.4 million funding over 5 years for the SCALE@NTU lab. Professor Gao leads the Data Management Research Group (DANTE) and co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU), which develops market-leading AI and data science technologies.

AJ (Ad) FeeldersView profile

Associate Professor

• Data Mining
• Machine Learning
• Applied Data Science
• +5 more

Dr. AJ (Ad) Feelders is an Associate Professor at Utrecht University, affiliated with the Department of Information and Computing Sciences within the Faculty of Science. His primary research focuses on Data Mining, Machine Learning, and Applied Data Science, with a particular emphasis on algorithmic data analysis and fraud detection. He has organized major conferences like the Symposium on Intelligent Data Analysis (IDA 2020) and contributed to editorial roles for journals such as Intelligent Systems in Accounting, Finance & Management . Roles: Associate Professor, Department of Information and Computing Sciences Affiliations: Utrecht University, Faculty of Science His research spans diverse applications, including renewable energy analysis, Bayesian networks, and time series forecasting. Notable projects include developing methods for detecting critical events in energy production and creating graph embeddings for historical data analysis. He has also worked on fraud detection in international shipping and real-time outlier detection in water sensor data. Feelders has authored or co-authored over 80 publications, including influential works on exceptional model mining, isotonic regression, and monotonic classification. His contributions bridge theoretical advancements with practical applications in domains like environmental science, healthcare, and digital humanities.

Mathieu BarthetView profile

Senior Lecturer

• Music Information Retrieval
• Human-Computer Interaction
• Affective Computing
• +5 more

Dr. Mathieu Barthet is a Senior Lecturer in Digital Media at Queen Mary University of London (School of Electronic Engineering and Computer Science). He leads research in Music Information Retrieval, Affective Computing, and Extended Reality (XR), with a focus on AI-driven musical interfaces and human-computer interaction. His work spans music emotion recognition, smart instruments, and participatory music systems. Education: MSc in Electronics & Computer Science (Paris VI University/Ecole Polytechnique de Montréal) MSc in Acoustics (Aix-Marseille II University/Ecole Centrale Marseille) PhD in Acoustics, Signal Processing & Computer Science applied to Music (Aix-Marseille II University/CNRS-LMA) Research: Over 100 peer-reviewed publications in fields like audiovisual interfaces, music recommendation, and AI ethics Key projects include the Internet of Musical Things (IoMT), AudioCommons, and mood-driven interactive systems Develops tools for music production collaboration (e.g., Open Symphony, Playsound.space) Grants & Collaborations: Lead on EU Horizon 2020 grants (IoMT, AudioCommons) Co-director of the EPSRC Centre for Doctoral Training in AI & Music Industry partnerships with companies like Music Tribe and Innovate UK Labs & Teams: Member of the Centre for Multimodal AI Conducts research through the Digital Music Lab (DML) initiative

Khanh LuongView profile

Research Fellow

• Machine Learning
• Data Mining
• Multi-Aspect Data Clustering
• +7 more

Dr. Khanh Luong is a Research Fellow at the School of Communication within the Faculty of Creative Industries, Education & Social Justice at Queensland University of Technology (QUT). She holds a PhD in Computer Science specializing in Data Science (2019) from QUT. Her research focuses on multi-aspect data, addressing challenges in high-dimensional, sparse datasets through innovative machine learning and data mining methods. She contributes to real-world applications in diverse domains like text, image, and bioinformatics data. Teaching roles include tutoring in Discrete Mathematics, Programming Principles, and Database Management for undergraduate/postgraduate students. She also designed the online unit 'Introduction to Programming' for the Master of IT program. Research interests span machine learning, data mining, multi-aspect data clustering, dimensionality reduction, and unsupervised learning. She works on the Applied Data Science project at QUT's Centre for Data Science, developing solutions for complex industry datasets. Publications emphasize multi-view clustering, manifold learning, and domain adaptation. Collaborations include roles in organizing the 2021 AusDM conference and reviewing for IEEE journals.

David B. BlumenthalView profile

Professor

• Biomedical Engineering
• Network Science
• Artificial Intelligence in Medicine
• +6 more

David B. Blumenthal is an Assistant Professor and head of the Biomedical Network Science Lab at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His work focuses on applying network science and AI to study molecular disease mechanisms and developing privacy-aware biomedical AI solutions. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano and has held postdoctoral positions at TU Munich and the Free University of Bozen-Bolzano. His research spans federated learning, graph algorithms, and systems medicine, with a strong emphasis on translational applications in healthcare. Education: BSc/MSc Mathematics, Free University of Berlin & Technical University of Berlin BA/MA Philosophy, Free University of Berlin PhD in Computer Science, Free University of Bozen-Bolzano Visiting PhD student at GREYC Research Lab, France Research Interests: Blumenthal’s work integrates computational methods with biomedical data to uncover disease mechanisms. Key areas include federated learning for medical data privacy, network-based disease module discovery, and AI-driven drug repurposing. He develops tools like ROBUST-Web, NeDRex, and DataSAIL to address challenges in data leakage and heterogeneous data integration. Awards: Best Poster Award (2022), Best Paper Awards (2021, 2017), Best PhD Student Award (2021) Recipient of German Academic Scholarship Foundation support Grants: Leads projects funded by BMBF, DFG, and industry partners, including FLabNet (federated learning in paediatric oncology) and NetMap (dimensionality reduction in single-cell data). His work often bridges FAU’s engineering and medical faculties. Labs/Teams: Heads the Biomedical Network Science Lab, collaborating with institutions like TU Munich, Bitspark GmbH, and Erlangen University Hospital on translational AI projects.

• Machine Learning
• Signal Processing
• Pattern Recognition
• +3 more

Mário A. T. Figueiredo is an IST Distinguished Professor and Feedzai Professor of Machine Learning at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST) . He leads the Lisbon Unit for Learning and Intelligent Systems (LUMLIS) within ELLIS and coordinates research clusters at Instituto de Telecomunicações. His research focuses on machine learning , signal processing , and image restoration , particularly using sparsity-based methods , wavelet transforms , and Bayesian algorithms . Recent work includes 3D shape correspondence , hyperspectral sharpening , and adaptive optimization techniques . Key article themes: Biclustering , ADMM algorithms , sparsity regularization , and generative embeddings Scientific recognition: EURASIP Fellow , Clarivate Highly Cited Researcher , IEEE/IAPR Fellow , and multiple best paper awards He has mentored numerous PhD/MSc students and contributed to information-theoretic kernels , feature discretization , and multimodal data analysis in biomedical and computer vision applications.

Alexandra M. CarvalhoView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Bayesian Networks
• +3 more

Alexandra M. Carvalho is an Assistant Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Universidade de Lisboa. She is also a researcher at the Pattern and Image Analysis Group (PIA) within the Instituto de Telecomunicações (IT). Her academic journey includes a five-year degree in Applied Mathematics from IST (1998), a Master's in Computer Science (2004), and a PhD in Computer Science (2011), both from IST. Her research focuses on machine learning, bioinformatics, Bayesian networks, and medical informatics, with applications in healthcare, genomics, and longitudinal data analysis. Education: Bachelor’s in Applied Mathematics, IST, 1998 Master’s in Computer Science, IST, 2004 PhD in Computer Science, IST, 2011 Research interests emphasize structured motif discovery, probabilistic graphical models, survival analysis, and time-series data mining. Key contributions include algorithms for motif extraction in DNA sequences, dynamic Bayesian networks for disease progression modeling, and tools like GRISOTTO and RISO for bioinformatics. She has been awarded the Sartorius Innovation Award and recognition from BioMed Central for impactful publications. Her work spans interdisciplinary collaborations, including projects on cancer systems biology, pharmacokinetic modeling, and clinical decision support. She advises numerous MSc students and has secured grants from FCT and EU initiatives. Notable software contributions include AliClu for clinical data clustering and METEOR for outlier detection in time series. Labs/Teams: Active in the PIA Group and KDBio Group (INESC-ID), focusing on computational methods for biomedical data analysis.

Giuliano GrossiView profile

Associate Professor

• Sparse representation models
• Signal processing
• Face recognition
• +5 more

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

• Digital Forensics
• Cybercrime Analysis
• Social Network Analysis
• +3 more

Jan William Johnsen is a Researcher at the Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. He specializes in digital forensics, cybercrime analysis, and social network analysis, with a focus on applying big data techniques to criminal investigations. His research interests include identifying cybercriminals through text and network analysis, preprocessing natural language for open-source intelligence, and investigating technological factors affecting child sexual abuse material distribution. He has contributed to interdisciplinary studies on criminal network dynamics and has co-authored reports on digital forensics for major institutions. Recent publications highlight trends in criminal network analysis, automated forensic frameworks, and the application of big data in cybercrime investigations. His work bridges technical cybersecurity methods with criminal justice applications. Johnsen teaches courses such as IMT4114 (Introduction to Digital Forensics) and IMT4130 (Investigation of Cybercrime), and has participated in international workshops like the IEEE Big Data conference. His contributions include developing tools like Reccoon for network footprinting and analyzing organized crime groups' communication patterns.