Find Academic Supervisors

Kaixiong ZhouView profile

Assistant Professor

• Machine Learning
• Graph Representation Learning
• Language Models
• +4 more

Kaixiong Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State). He previously held a postdoctoral position at the Institute for Medical Engineering and Science (MIT) and earned his Ph.D. in Computer Science from Rice University in 2023. His academic journey includes a Master’s in Electrical Engineering from University of Science and Technology of China (USTC, 2018) and a Bachelor’s in Electrical Engineering from Sun Yat-Sen University (2015). Awards : ORAU Ralph E. Powe Junior Faculty Enhancement Award (2025), Student Travel Award (KDD 2022), Outstanding Graduate Awards (USTC 2018), National Scholarship (2017). His research focuses on graph representation learning , language models , and AI for science & engineering , with applications in synthetic biology, drug discovery, and network analysis. Recent work spans graph neural networks, large language models (LLMs), and privacy-preserving AI, emphasizing efficiency and trustworthiness. Publications in ICML, NeurIPS, and ICLR highlight innovations in structured pruning, knowledge editing, and fairness-aware systems. He actively mentors prospective students and collaborates on interdisciplinary projects.

Lina FAHEDView profile

Associate Professor

• Data Mining
• eXplainable Artificial Intelligence (XAI)
• Time Series Analysis
• +5 more

Lina FAHED is an Associate Professor in Computer Science at IMT Atlantique, part of the DECIDE research team within the Lab-STICC laboratory in Brest, France. Her primary research focuses on data mining in complex data, eXplainable Artificial Intelligence (XAI), time series analysis, event sequence prediction, and anomaly detection. She holds a PhD in Computer Science from Lorraine University (2016) and a Master’s degree in Software and Knowledge Engineering from Strasbourg University (2012). Her academic journey includes roles such as Temporary Lecturer and Research Assistant at Lorraine University (2015–2016) and a postdoctoral position at IMT Atlantique (2016–2019). Her work emphasizes interdisciplinary applications of AI, particularly in enhancing transparency and ethical considerations in machine learning systems. Key research interests include: Explainability in AI systems Bias detection and mitigation Pattern mining in sequential data Algorithmic approaches for early event prediction Her publications (15 most recent listed) span topics like automata-based explainable representations, XAI reviews, and event prediction algorithms, reflecting her contributions to both theoretical and applied AI research.

Xixi LuView profile

Assistant Professor

• Process Mining
• Machine Learning
• Artificial Intelligence
• +5 more

Dr. Xixi Lu is an Assistant Professor in the Department of Information and Computing Sciences at Utrecht University’s Faculty of Science. Her research focuses on Process-Aware Data Science, Machine Learning, and Artificial Intelligence, with applications in healthcare, IT, banking, and auditing. She holds a Ph.D. and M.Sc. from Eindhoven University of Technology, followed by a postdoctoral position at Vrije Universiteit Amsterdam. Her work emphasizes aligning organizational processes with practical adaptations made by practitioners, such as optimizing care home operations or improving patient data anonymization. She teaches in the Information Science Bachelor’s and Business Intelligence Master’s programs, fostering interdisciplinary collaboration and student-driven innovation. Lu advocates for team science, leveraging diverse perspectives within her group to tackle complex challenges, such as developing algorithms that bridge theoretical foundations and real-world applications. Her recent research includes reinforcement learning for healthcare process optimization, predictive process monitoring, and explainable AI. She emphasizes societal relevance, striving to ensure her work contributes to tangible improvements in efficiency and ethical practices across industries.

Jeremy BarnesView profile

Assistant Professor

• Cross-lingual Methods
• Transfer Learning
• Sentiment Analysis
• +5 more

Jeremy Barnes is an Assistant Professor in the IXA Group at the University of the Basque Country (UPV/EHU), part of the HiTZ Centre. He holds a PhD in Computational Linguistics and has extensive postdoctoral experience at the University of Oslo. His research focuses on cross-lingual methods, sentiment/emotion analysis, weak supervision, and under-resourced language resources. He has contributed to projects like NorDial and NorLM, developing tools like skweak. Barnes teaches courses in computational syntax, text mining, and NLP. Education: PhD (Pompeu Fabra University), M.A. and B.A. in Linguistics Key Projects: LUMINOUS (multimodal LLMs), SANT (Norwegian sentiment analysis) His work spans structured sentiment analysis, cross-lingual transfer learning, and dialectal feature identification. He has received awards for his book Sonic Ethnography (2021) and photography (2020). Barnes advises numerous Master's theses and collaborates internationally on NLP challenges.

Brent MacRaeView profile

Academic

• Networking
• IT Security
• User Authentication
• +3 more

Brent MacRae is an Associate Teaching Professor in the Networking and IT Security department at Ontario Tech University, affiliated with the Faculty of Business and Information Technology. He serves as Program Director for the Bachelor of Information Technology (BIT) program (NITS) and manages the university's Networking Lab. MacRae holds a Master of Science in Computer Science (2016) and a Bachelor of IT (2013), both from Ontario Tech University. His expertise includes IT security, networking, and human-factors in user authentication, with active research contributions in these areas. He is certified as a Cisco Certified Network Professional (CCNP) and is pursuing additional certifications in networking and security. MacRae’s research focuses on enhancing network infrastructure and security through practical applications, such as optimizing electric vehicle charging stations and improving supply chain resilience. His work bridges theoretical models (e.g., Benders decomposition for supply chain design) with real-world challenges like disaster recovery and environmental reporting. He collaborates with industry on projects such as scheduling decision support systems and rail hazmat shipment planning. His publications span transportation logistics, supply chain management, and cybersecurity, emphasizing risk mitigation and optimization techniques. MacRae contributes to experiential learning through the Networking Lab and the Faculty of Business and IT’s Experiential Learning initiatives (FBIT XO). He is involved in curriculum development for the BIT program and mentors students in applied IT projects.

Markus BarthView profile

Professor

• Biomedical Engineering
• Medical Imaging
• Neuroimaging
• +3 more

Professor Markus Barth is a Full Professor in Biomedical Engineering at the University of Queensland (UQ), specializing in MRI physics and medical imaging. He holds roles as Deputy Head of School (Research) in the Faculty of Engineering, Architecture and Information Technology, Director of the National Imaging Facility – Queensland Node, and member of the ARC College of Experts. His academic journey includes a Doctorate in Technical Physics from Vienna University of Technology (1999) and prior appointments at the Medical University Vienna, Donders Institute, and Erwin L. Hahn Institute. His research focuses on advancing MRI techniques for high-resolution anatomical and functional imaging, including susceptibility-weighted imaging (SWI), quantitative susceptibility mapping (QSM), and ultra-high field MRI applications. He has pioneered methods to enhance MRI sensitivity for neurodegenerative diseases, cancer, and cognitive neuroscience studies. Barth’s work integrates cutting-edge technologies like deep learning and parallel transmission to improve image quality and clinical utility. His contributions include developing open-source tools (e.g., QSMxT) and large-scale datasets (e.g., HumanBrainAtlas) for reproducible neuroimaging research. Education: Bachelor of Technical Physics, Vienna University of Technology (1995) Doctorate in Technical Physics, Vienna University of Technology (1999) Research Interests: Optimizing MRI for ultra-high field applications, neuroimaging of brain function and connectivity, SWI/QSM for disease markers, and translational imaging techniques for clinical diagnostics. His work addresses topics like layer-specific fMRI, brain-computer interfaces, and decoding neural activity. Grants & Awards: ARC Future Fellowship (2014), leadership in national imaging initiatives, and contributions to MRI software frameworks. Labs/Teams: Leads the Ultra-high Field Human MR Research program at UQ’s Centre for Advanced Imaging and collaborates with interdisciplinary teams in biomedical engineering and neurology. His lab develops novel MRI protocols and open-source tools for global research impact.

• Data Science
• Computer Vision
• Machine Learning
• +9 more

Patrick Zhang is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland (UQ). His research focuses on applying advanced machine learning techniques to real-world challenges in domains such as medical imaging, environmental monitoring, and structural analysis. Key collaborators include Liangchen Liu, Kun Zhao, Arnold Wiliem, and Brian Lovell. His research interests revolve around data science , with a strong emphasis on computer vision and deep learning . Specific topics include: Wild animal detection and pose estimation Medical slide quality assessment and computational pathology Thermal-to-visible face recognition systems Multi-modal clustering for unsupervised learning Low-resolution image processing and boundary-guided translation Infrastructure corrosion assessment using neural networks Patrick Zhang has produced a steady stream of publications since 2018, demonstrating growth in applying deep learning to healthcare and engineering. His recent work (2022) addresses cell labelling challenges in medical diagnostics, while earlier contributions explored foundational GAN-based approaches and cross-modal recognition systems. The trajectory highlights both technical innovation and cross-domain application of AI. No scientific awards have been listed for Patrick Zhang in the provided information. His advising and grant details are not explicitly mentioned, though his collaborative projects suggest involvement in interdisciplinary research teams.

• Medical Imaging
• Artificial Intelligence in Healthcare
• Deep Learning
• +3 more

Dr. David Ng is an academic researcher affiliated with the Faculty of Science at the University of Sydney. His work focuses on advancing artificial intelligence and deep learning techniques for medical imaging applications, particularly in areas such as radiomics, multimodal data fusion, and healthcare diagnostics. He contributes to innovations in MRI, CT, PET, and endoscopic imaging analysis, developing algorithms for disease detection, segmentation, and predictive modeling. His research interests span medical image reconstruction, federated learning, motion artifact reduction, and dynamic epidemiological modeling. He has published extensively on topics such as neural network architectures for biomedical applications, multimodal data integration, and AI-driven diagnostic tools. Key contributions include advancements in sleep staging via multimodal networks, scleral deformation analysis using OCT images, and automatic segmentation techniques for cardiac and tumor imaging. His work emphasizes practical applications in oncology, cardiology, and public health, aiming to improve clinical decision-making through computational methods. Dr. Ng’s research also explores the intersection of virtual/augmented reality with medical education and intervention. He collaborates on interdisciplinary projects involving biomedical engineering, computer science, and clinical practice to address real-world healthcare challenges.

Irina VoiculescuView profile

Associate Professor

• Medical Imaging
• 3D Geometric Modelling
• Artificial Intelligence
• +4 more

Irina Voiculescu is an Associate Professor and Stipendiary Lecturer in the Department of Computer Science at the University of Oxford. Her research focuses on 3D geometric modeling, medical imaging analysis, and AI-driven clinical decision support. She leads the Medical Imaging research group, developing tools like OxMedIS for automated segmentation of MRI, CT, and ultrasound data. Her work emphasizes GPU-accelerated algorithms and reinforcement learning to reduce reliance on manual annotations. Education: PhD in Constructive Solid Geometry (CSG) from the University of Bath (1999). Research interests include medical image segmentation, GPU computing, cephalometric landmark detection, and infant hip dysplasia screening. Collaborates with orthopaedic clinicians and industry partners. Notable contributions: Developed novel evaluation metrics for segmentation accuracy, pioneered GPU-based watershed algorithms, and advanced AI applications in hip and knee imaging. Her team’s work on automated Graf classification for infant hip screening was recognized at ECCV 2024. Awards: Best Paper Award at DEMI 2023, Best Workshop Paper at HSI 2009. Editorial roles include Associate Editor for SPIE Journal of Medical Imaging. Advising: Supervises PhD students in medical AI, including NIHR-funded researchers. Past students include Nanqing Dong (federated learning) and Ziyang Wang (transformer networks). Runs the Spatial Reasoning group, known for the Oxford Robot Games and robotic sheepdog projects. Publications: Over 80 peer-reviewed papers since 2008, with recent focus on few-shot learning, domain adaptation, and clinical validation of AI models.

Juho RousuView profile

Professor

• Machine Learning
• Metabolomics
• Biomedicine
• +6 more

Juho Rousu is a Professor in the Department of Computer Science at Aalto University. His research focuses on developing machine learning methods for computational and data science, with a strong emphasis on kernel methods, structured prediction, and applications in metabolomics, biomedicine, and synthetic biology. He leads efforts in multi-view learning, sparsity-driven models, and optimization techniques for complex data analysis. Key research areas include drug combination prediction, metabolite identification, and biomarker discovery through canonical correlation analysis. His work integrates chemical reaction analysis, graph learning, and deep learning approaches to tackle challenges in systems biology and precision medicine. Recent projects involve scalable methods for drug synergy modeling and computational tools like MassSpecGym for molecular discovery. Rousu’s methodologies often bridge algorithmic innovation with real-world applications, such as improving protein production in biotechnology and optimizing metabolic flux analysis. His contributions span from foundational machine learning theory to practical software tools like CamOptimus and SIRIUS 4. He collaborates across disciplines to address challenges in environmental microbiology, clinical diagnostics, and systems pharmacology. His research has been applied to critical areas like tuberculosis drug efficacy prediction and metabolic network reconstruction, demonstrating impact in both academic and industrial contexts. Despite no listed awards here, his extensive publication record reflects sustained leadership in computational science and its biomedical applications.

Shilpa TanejaView profile

Lecturer

• Sustainable Marketing
• Digitalization in Business
• Branding Strategy
• +4 more

Dr. Shilpa Taneja is a Lecturer in Marketing at the Sheffield University Management School (University of Sheffield), specializing in sustainable marketing, digitalization, and branding. With over 8 years of academic experience, she focuses on research methodologies and mentoring students. Her work bridges sustainability, digital platforms, and strategic business practices. Education: PhD in Marketing. Research interests include sustainable consumer behavior, FinTech, and the interplay between digitalization and environmental goals. She has published in journals like Business Strategy and the Environment and IEEE Transactions on Engineering Management , exploring topics such as sustainable banking, circular economy frameworks, and SME digital transformation. Teaching: Uses innovative methods (e.g., case studies, flipped classrooms) to foster inclusive learning. Supervised 30+ postgraduate projects and mentored 40 students. Part of the Marketing and Creative & Cultural Industries (MCCI) research group. Labs/Teams: Engaged with MCCI group, contributing to interdisciplinary research on marketing, creativity, and industry collaboration.

James A. JonesView profile

Associate Professor

• Software Engineering
• Software Testing
• Debugging Techniques
• +5 more

James A. Jones is an Associate Professor in the Department of Informatics at the Bren School of Information and Computer Sciences, University of California, Irvine. He leads the Spider Lab and focuses on software engineering, debugging, and testing. His research emphasizes practical techniques for improving software development efficiency, with notable contributions like the Tarantula fault localization method. Jones has advised numerous graduate and undergraduate students and holds significant awards including the ACM SIGSOFT Impact Award and NSF CAREER Award. Educations: Ph.D. in Computer Science (Georgia Tech, 2008), B.S. in Computer Science (Ohio State University, 1996). Employment history includes roles as an Assistant Professor (UCI, 2008–2015) and Graduate Research Assistant (Georgia Tech, 2000–2008). Research Interests: Software fault localization, testing methodologies, program analysis, and visualization tools. His work bridges human comprehension challenges in debugging with algorithmic solutions. Key areas include spectra-based techniques, test-suite management, and developer collaboration tools. Awards include the ACM SIGSOFT Impact Award (2015) for Tarantula, NSF CAREER Award (2014), and Best Paper honors at multiple conferences. He has mentored over 20 students and serves on editorial and program committees for top-tier conferences like ICSE and FSE. Labs/Teams: Spider Lab (http://spideruci.org), focusing on software analysis and debugging tools. Active in organizing workshops such as VISSOFT and WODA.

Bennett A. LandmanView profile

Professor

• Medical Imaging
• Biomedical Engineering
• Machine Learning
• +6 more

Bennett A. Landman is the Stevenson Chair of Engineering and a Professor of Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He holds additional professorships in Computer Science, Biomedical Engineering, Radiology, Psychiatry, Biomedical Informatics, and Neurology. Landman leads the Vanderbilt Lab for Immersive AI Translation (VALIANT) and has been instrumental in advancing medical image processing and AI-driven personalized medicine. Education: B.S. & M.Eng. in Electrical Engineering and Computer Science from MIT (2001-2002) Ph.D. in Biomedical Engineering from Johns Hopkins University (2008) Research Focus: Landman's work centers on applying image-processing technologies to large-scale medical imaging studies, particularly in neuroimaging and radiology. His research integrates machine learning, big data analytics, and AI to improve diagnostic accuracy and personalized healthcare. Key areas include diffusion MRI harmonization, functional MRI analysis, and automated medical image segmentation. Recent Trends in Articles: Landman's latest publications focus on spatial smoothing techniques in white matter imaging, super-resolution eye atlases, and scalable medical data processing. His work emphasizes cross-disciplinary collaboration and the development of robust preprocessing methods for multi-site datasets. Awards: Fellow of the SPIE (2021) Grants & Advising: Secured grants from NIH, NSF, and DoD. Advised over 340 co-authors across institutions, resulting in 450+ peer-reviewed publications and 24,000+ citations. Served as editor for IEEE Transactions on Medical Imaging and co-chair of SPIE Medical Imaging conferences. Labs & Teams: Director of VALIANT and founding director of Vanderbilt's Center for Computational Imaging. Active in the Vanderbilt Brain Institute and Kennedy Center, advancing interdisciplinary research in surgery, engineering, and neuroscience.

Kevin LeachView profile

Assistant Professor

• Cybersecurity
• Software Engineering
• Dependable Systems
• +3 more

Kevin Leach is an Assistant Professor of Computer Science at Vanderbilt University's School of Engineering. His research focuses on designing dependable cybersecurity systems and software engineering practices that withstand sophisticated adversarial attacks. He holds a PhD from the University of Virginia (2016), where he received the Louis T Rader Outstanding Research Award. Prior to Vanderbilt, he was a research scientist and lecturer at the University of Michigan. Education: Ph.D. in Computer Engineering, University of Virginia MS in Computer Science, George Mason University BS with Distinction in Computer Engineering and Computer Science, University of Virginia His research investigates two core areas: leveraging hardware capabilities like Trusted Execution Environments for low-overhead security instrumentation, and understanding how developers and operators make decisions in adversarial environments. He has published over 30 refereed articles, 3 of which have received awards. Recent work includes studies on cognitive biases in cybersecurity competitions, AI-driven code assessment, and malware detection techniques. Awards: Louis T Rader Outstanding Research Award (2016) 3 award-winning papers (specific awards unspecified) His research bridges technical security mechanisms with human decision-making processes, emphasizing both system dependability and developer/operator awareness. Current projects include work on federated learning defenses, runtime security optimization for embedded systems, and ethical considerations in crowdsourced data labeling.

Gustavo CarneiroView profile

Professor

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +4 more

Professor Gustavo Carneiro is a faculty member at the University of Surrey's School of Computer Science and Electronic Engineering, leading the Centre for Vision, Speech and Signal Processing (CVSSP). His primary role is as Professor of AI and Machine Learning, with a focus on medical image analysis, computer vision, and theoretical machine learning. He holds a PhD from the University of Toronto and has extensive international experience, including roles at Siemens Corporate Research and academic positions in Brazil and Australia. Research interests span applied medical imaging (e.g., mammogram analysis, colonoscopy, and histology) and theoretical advances in deep learning, semi-supervised learning, and robust models under label noise. Notable projects include the People-centred Mammogram Analysis (PecMan) initiative, aiming to improve AI trustworthiness and fairness in clinical settings. Prof. Carneiro has supervised over 30 students and postdocs, including PhD candidates in Adelaide and Surrey. His work bridges academia and industry, with publications in top venues like CVPR, MICCAI, and IEEE Transactions. Awards include Dean’s Commendation for Doctoral Thesis Excellence for multiple students. Key collaborations involve medical institutions and tech partners, addressing challenges in endometriosis diagnosis, Barrett’s esophagus detection, and AI-driven surgical tools. His lab develops open-source datasets and frameworks, such as the tumor hypoxia analysis dataset.