Find Academic Supervisors

Niraj JhaView profile

Professor

• Smart Healthcare
• Machine Learning
• Cybersecurity
• +5 more

Niraj Jha is a Professor of Electrical and Computer Engineering at Princeton University, affiliated with the School of Engineering and Applied Science. He joined Princeton in 1987 and became a full professor in 1998. His research focuses on smart healthcare, machine learning algorithms, IoT security, and neuro-symbolic AI. He leads projects in wearable medical sensors for disease detection, transformer synthesis, and cybersecurity for cyber-physical systems. Jha has authored/co-authored over 480 papers, 25 patents, and multiple books, including Testing and Reliable Design of CMOS Circuits and Nanoelectronic Circuit Design . He has received prestigious awards like the ACM and IEEE Fellowships, and the Distinguished Alumnus Award from IIT Kharagpur. Education: Ph.D., University of Illinois at Urbana-Champaign, 1985 M.S., State University of New York at Stony Brook, 1982 B.Tech., Indian Institute of Technology Kharagpur, 1981 Research Interests: Jha’s work bridges AI, hardware design, and healthcare. Key areas include: Predictive healthcare via wearable sensors and ML ensembles Transformer acceleration for edge computing Cybersecurity for IoT and 5G systems Neuro-symbolic AI for small-data learning Optimization via reinforcement learning His lab explores body-area networks, synthetic control for clinical trials, and energy-efficient architectures. Publications & Awards: Over 20 papers have won best paper awards. Notable recognitions include the Princeton Graduate Mentoring Award (2004) and the 2025 ACM recognition for Learning Interpretable Differentiable Logic Networks . Advising & Labs: Jha advises graduate students on topics like neural architecture search (e.g., FlexiBERT) and healthcare AI (e.g., DOCTOR framework). He directs the Andlinger Center for Energy and the Environment and has led the Center for Embedded System-on-a-Chip Design. His lab collaborates on projects like SHF: Small grants for transformer synthesis and NSF-funded initiatives. Labs & Teams: Active in Princeton’s Global Health Initiative and collaborates across disciplines, including robotics and data science. His research group develops tools like CODEBench (co-design frameworks) and TUTOR (decision-rule-based ML).

• Machine Learning
• Ensemble Learning
• Feature Selection
• +4 more

Gavin Brown is a Professor of Computer Science at the University of Manchester, affiliated with the Data Science Institute and the Machine Learning and Optimisation group. His research focuses on machine learning theory, ensemble methods, feature selection, and hardware-accelerated learning. He holds roles in interdisciplinary initiatives like the Centre for Digital Trust and Society, EnnCore project (privacy-preserving neural architectures), and the Robotics and Artificial Intelligence Centre. Education: PhD in Computer Science from the University of Birmingham (2003), with a thesis on 'Diversity in Neural Network Ensembles'. Research interests span theoretical foundations of machine learning, including bias-variance decomposition, ensemble diversity, and algorithmic stability. Applications include healthcare (e.g., outlier detection in vital signs), robotics (low-cost prediction hardware), and ethical AI (conceptual guarding of neural networks). He has pioneered work on feature selection for resource-constrained systems and neuromorphic computing. Recent articles emphasize unifying ensemble theory, hardware-efficient learning (FPGA-based feature selection), and clinical applications of machine learning. Projects include EnnCore (privacy-preserving AI), RAI Centre (robot-AI ethics), and Data Visualisation for clinical trials. He leads or co-leads 8 major projects, including £multi-million initiatives in digital trust, robotics, and AI. Supervised 24 doctoral students, with a focus on interdisciplinary work combining theory and applied machine learning.

Mohamed SedkyView profile

Professor

• Computer Vision
• Video Analytics
• Cyber-Physical Systems
• +4 more

Professor Mohamed Sedky holds the position of Professor of Cyber-Physical Systems at Staffordshire University. He previously founded SKM Communication Systems and served as a lecturer at the Arab Academy for Science and Technology before joining Staffordshire in 2003. His roles include MSc Computer Science Deputy Award Leader and BSc Internet of Things Award Leader. He leads research at AVA Technologies Ltd, focusing on real-time video analytics algorithms such as Spectral-360®, which addresses security, safety, and business intelligence applications. His patented work in object detection and change detection has been globally recognized. He teaches modules like Fundamentals of Computer Networks and Router Security Technologies. Education: BEng (Hons) Electro-Physics and Communications, Alexandria University (1996) MSc Communications and Electronics, Arab Academy for Science and Technology (2002) PhD Computer Vision, Staffordshire University (2009) PGC Research Supervision, Staffordshire University PGC Higher and Professional Education, Staffordshire University Research Interests: Object recognition/segmentation Video surveillance systems IoT forensic processes AI in healthcare and smart environments Commercial Ventures: Founder of Spectral-360® technology Lead Researcher at AVA Technologies Ltd Consultant for Serco, Centralweighing, and WMTN Labs & Projects: Developed AVA Technologies' Detect/Protect/Count/Summarise solutions Designed OpenSHS (Open Smart Home Simulator) Conducted feasibility studies for buried utility detection and industrial inspection His research bridges academia and industry through patented innovations in video analytics and IoT systems, addressing challenges in security, healthcare, and smart infrastructure.

Jin TianView profile

Professor

• Causal Inference
• Machine Learning
• Artificial Intelligence
• +3 more

Jin Tian is a Professor affiliated with the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having previously held a Graduate Faculty position at Iowa State University. His research focuses on causal inference, machine learning, and artificial intelligence. Dr. Tian's work emphasizes causal discovery, causal effect identification, adversarial robustness in machine learning, and graphical models. He holds a Ph.D. in Computer Science from UCLA, an M.S. in Physics from UCLA, and a B.S. in Physics from Tsinghua University. His research interests span causal inference frameworks, including handling hidden variables and developing algorithms for causal model testing. He also explores adversarial machine learning, regularization techniques for robustness, and large-scale data systems like AnnotatedTables. His contributions to causal effect estimation via instrumental variables and combining experimental results highlight his interdisciplinary approach. Recent work includes advancements in polynomial-delay algorithms for causal model testing, unified covariate adjustment methods, and vulnerability-aware adversarial training. His research bridges theoretical foundations (e.g., graph-based complexity) with practical applications in machine learning security and causal reasoning.

Jörn HeesView profile

Professor

• Data Science
• Deep Learning
• Anomaly Detection
• +3 more

Prof. Jörn Hees is a Professor for Data Science at the Department of Computer Science, Hochschule Bonn-Rhein-Sieg (H-BRS), and serves as Chairman of the Computer Science Faculty Council. He is affiliated with the Institute for Artificial Intelligence and Autonomous Systems (A2S) and the Institute of Technology, Resource and Energy-efficient Engineering (TREE). His research focuses on Data & Graph Mining, Deep Learning, Anomaly Detection, Explainable AI, and AI-assisted education (e.g., automated grading systems). He teaches courses such as Stochastics, Data Analysis and Visualization, Artificial Intelligence (Bachelor level), and Deep Learning Foundations, Natural Language Processing (Master level). His work bridges theoretical advancements with practical applications in finance, remote sensing, and education. Notable projects include federated outlier detection systems (Fin-Fed-OD), the TreeSatAI benchmark dataset for tree species classification, and AI-driven grading solutions using LLMs. His publications emphasize anomaly detection techniques, multimodal fusion, and interpretable machine learning. Despite no explicit awards listed, his contributions to AI education and computational methods are evident through his extensive publication record. He coordinates research teams and advises on infrastructure for AI education and infrastructure monitoring via Earth observation data.

• Machine Learning
• Partial Differential Equations
• Computational Fluid Dynamics
• +3 more

Dibyakanti Kumar is a researcher in the Department of Computer Science, specializing in machine learning, computational mathematics, and fluid dynamics. His work focuses on neural networks, partial differential equations (PDEs), and their applications in solving complex physical systems. He has contributed to advancing theoretical guarantees for deep learning models and their application to fluid dynamics problems. His recent research includes developing data-driven methods for aerodynamic simulations and analyzing the stability of neural networks near critical phenomena like finite-time blow-up in PDEs. Key research interests include: Neural network training and generalization Numerical methods for PDEs Physics-informed machine learning Data augmentation techniques Recent academic activities include an oral presentation on the risks of solving fluid dynamics problems with neural networks, delivered in December 2024. His collaborations span interdisciplinary topics, integrating mathematical theory with engineering applications.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +5 more

Jonathan Wu is a distinguished academic and Professor in the Faculty of Engineering at the University of Windsor. He is recognized for his leadership in artificial intelligence, computer vision, and signal processing. His research bridges theoretical advancements with practical applications, including maritime surveillance using High-Frequency Surface Wave Radar (HFSWR) and cybersecurity protocols for vehicular networks. Wu holds notable accolades such as Fellow of the Canadian Academy of Engineering (2019) and a Canada Research Chair (2018). His work spans interdisciplinary areas such as medical imaging, robotics, and data science. Notable contributions include developing novel algorithms for semantic segmentation, authentication protocols, and radar-based target detection systems. Wu has advised graduate students like Ashirbani Saha and Kaushik Ray on projects involving facial recognition and drunk-driving prevention technologies. His research emphasizes real-world impact, with funding for AI integration in manufacturing and contributions to international conferences and journals.

Haohui LuView profile

Research Fellow

• Artificial Intelligence
• Graph Machine Learning
• Health Informatics
• +12 more

Haohui Lu is a Research Fellow at the Molly Wardaguga Research Centre within the Faculty of Health. He is registered to supervise postgraduate research and holds a PhD from The University of Sydney, awarded in March 2024. His work bridges artificial intelligence and public health, with a focus on ethical and explainable AI systems. Research Interests: Artificial Intelligence and Machine Learning Graph Machine Learning and Knowledge Graphs Health Informatics and Clinical Decision Support Bias Mitigation and Fairness in AI Large Language Models for Healthcare Statistical Modeling and Real-World Data Analysis His recent publications demonstrate a strong trend in applying machine learning—particularly graph-based and language models—to public health challenges such as disease progression, policy analysis, and undernutrition. He frequently publishes in high-impact journals like PLoS One , Scientific Reports , and Neurocomputing , and presents at top venues such as EMNLP. His work emphasizes fairness, interpretability, and practical impact for policymakers and communities. Scientific Contributions and Recognition: Active contributor to UN Sustainable Development Goals, particularly in health and well-being. Presented research at EMNLP 2024 on LLMs for disease prediction. Research disseminated through news outlets and widely shared on academic platforms like Mendeley. Advising and Leadership: Registered as a postgraduate research supervisor, indicating a role in mentoring future researchers. Provides technical leadership in AI tool development, research data platforms, and LLM applications, supporting multidisciplinary and international collaborations. Laboratories and Teams: Based at the Molly Wardaguga Research Centre, a hub for health research, where he contributes to projects integrating AI with real-world health data and policy.

Jerzy ŚwiątekView profile

Professor

• Modelling and Identification of Complex Systems
• Decision Support Systems
• Biological Systems Modelling
• +6 more

Prof. Jerzy Świątek is a Professor and Head of the Department of Computer Science and Systems Engineering at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His work focuses on modeling and identification of complex systems, decision support systems, and artificial intelligence applications. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Department of Computer Science and Systems Engineering Research Interests: Modeling and identification of complex systems Biological systems analysis Neural network applications Machine learning for signal processing Decision support systems Recent Publications Trends: His 2021–2024 research covers generative modeling in healthcare, signal processing for sensors, reinforcement learning for neural networks, and systems engineering applications. Key methods include variational mode decomposition, dictionary adaptation, and latent-space decoding for synthetic data generation. Teaching and Leadership: As department head, he oversees academic programs in systems engineering and participates in doctoral education at the university’s Doctoral School.

Olivier BernardView profile

Professor

• Medical Image Processing
• Artificial Intelligence in Cardiac Imaging
• Domain Adaptation
• +4 more

Olivier Bernard is a Professor of Image and Signal Processing at Université de Lyon (INSA), France, and Deputy Director of the CREATIS laboratory. He leads the MYRIAD research team and holds editorial roles in IEEE Transactions on Medical Imaging and the European Heart Journal . His work bridges artificial intelligence and medical imaging, focusing on cardiac applications. Education: HDR (2019), PhD in Medical Image Analysis (2006), M.S. in Imaging (2003), Engineering Degree (2003) – all from Université de Lyon (INSA). His research explores representation learning , domain adaptation , and physics-guided AI for cardiac imaging. Key areas include uncertainty modeling, echocardiographic segmentation, and clinical data fusion. Recent publications highlight reinforcement learning frameworks (RL4Seg) and diffusion models for cardiac diagnosis. Scientific contributions include domain adaptation techniques, open-source tools like CMRSegTools , and synthetic data generation pipelines. Awards include Senior Membership in the Institut Universitaire de France (IUF).

Xingjie WeiView profile

Associate Professor

• Business Analytics
• Unstructured Data Mining
• Social Computing
• +12 more

Dr. Xingjie Wei is an Associate Professor in Business Analytics and Machine Learning at the Centre for Decision Research (CDR), Leeds University Business School, University of Leeds. Her research bridges data science and business management, focusing on understanding human behavior through unstructured data such as images, text, and digital footprints using machine learning and big data analytics. She leads and advises on multiple funded research initiatives and supervises PhD students in related areas. PhD in Computer Science, University of Warwick Research Associate, Psychometrics Centre, Cambridge Judge Business School, University of Cambridge Lecturer, University of Bath Visiting Researcher, National Lab of Pattern Recognition (NLPR), Chinese Academy of Sciences Her research interests include business analytics, unstructured data mining, social computing, human trait analysis, multimodal data, financial risk analytics, and initial coin offerings. She develops algorithms to extract psychological and behavioral insights from digital footprints, with applications in policy, finance, and public services. Her work emphasizes human-centered decision-making and service optimization. The recent publications reflect a strong trend in using machine learning to analyze human traits from visual and textual data, with applications in finance, marketing, and behavioral science. Topics include CEO image analysis, emotion from EEG, facial similarity in films, and tabular data visualization. Her work spans computer vision, affective computing, and behavioral finance, demonstrating interdisciplinary depth. Winner of the 2014 IGI Global's Excellence in Research Journal Award Published in Journal of Corporate Finance , IEEE Transactions on Affective Computing , Annals of Operations Research , and International Marketing Review Active editorial roles in Sensors and Discover Analytics Xingjie Wei has secured and led multiple research grants, including ESRC, Innovate UK, and LUBS Challenge Fund projects. She supervises PhD students and hosts postdoctoral researchers. Her collaborative work includes partnerships with SR Mailing, Katchr, KAIST, and interdisciplinary teams across Leeds. She actively mentors early-career researchers and contributes to academic service through reviewing, conference organization, and public engagement. She is involved in the Centre for Decision Research and leads projects on digital footprints for policy, climate change impacts, and AI-driven decision support. Her team includes current PhD students and research associates working on topics like credit risk, problem gambling, and life quality measurement. She promotes interdisciplinary collaboration and knowledge transfer between academia and industry.

Hendrik HamannView profile

Professor

• Artificial Intelligence
• Machine Learning
• Physics-Based Simulations
• +5 more

Hendrik Hamann is a Professor at Stony Brook University's School of Marine and Atmospheric Sciences and Chief AI Scientist at EBNN, Brookhaven National Laboratory. He holds adjunct roles at the University of Illinois Urbana-Champaign and Yamagata University. His research bridges physical and computational sciences, focusing on AI, machine learning, high-performance computing, and geoinformatics for climate, sustainability, and energy applications. Education: Ph.D. in Physics from the University of Göttingen (1995). Prior Roles: IBM Research (1995–2024), leading initiatives in data center efficiency, geospatial analytics (PAIRS), and climate modeling. Spearheaded AI foundation models for energy grids and climate. Research interests include AI-driven climate forecasting, geospatial data fusion, and sustainable energy systems. His work has led to 180+ patents and transformative technologies like IBM PAIRS and the thermally assisted magnetic recording (HAMR). He has been recognized with the AIP Prize and IBM's Master Inventor title. Awards: 2016 AIP Prize, APS Fellowship, IEEE Senior Member, and IBM Academy of Technology membership. Grants: $20M DOE grant (2024) for methane quantification and multi-million DOE projects in energy systems. Labs/Teams: Led global IBM Research Climate & Sustainability teams (2021–2024) and co-developed geospatial foundation models for weather and earth observation.

Marten van DijkView profile

Professor

• Computer Security
• Cryptography
• Secure Processor Design
• +7 more

Dr. Marten van Dijk is a Full Professor in the Computer Security department at Vrije Universiteit Amsterdam (VU) since 2022 and a Group Leader for Computer Security at CWI since 2020. He also holds a Gratis Full Research Professor position at the University of Connecticut's ECE Department since 2020. Previously, he served as Associate and Full Professor at the University of Connecticut and held research roles at MIT CSAIL, RSA Laboratories, and Philips Research. PhD in Mathematics (1997, Eindhoven University of Technology) M.S. in Mathematics (Cum Laude, 1993) M.S. in Computer Science (Cum Laude, 1991) His research focuses on foundational computer security problems using cryptographic principles, including secure processor design, oblivious computation, and privacy-preserving machine learning. Notable contributions span Physical Unclonable Functions (PUFs), Aegis secure processor architecture, and oblivious RAM protocols. 15+ publications in 2023-2025 address topics like PUF cryptanalysis, differential privacy in federated learning, and Byzantine fault tolerance Key journals: IEEE Transactions on Computers, Journal of Cryptology, ACM CCS Conference Award highlights include: IEEE Fellow (2022) for secure processor design and encrypted computation IEEE Technical Achievement Award (2023) Intel Test of Time Award (2022) ACM CCS Best Paper (2013) A. Richard Newton Technical Impact Award (2015) His technical leadership spans hardware security (blu-ray error correction codes), cryptographic protocol design, and machine learning privacy frameworks. Current projects focus on secure processors with hardware-enforced isolation and differential privacy optimization.

Ian LaneView profile

Associate Professor

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +5 more

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

• Blockchain Technology
• Distributed Systems
• Cybersecurity
• +3 more

Aad P. A. van Moorsel is a faculty member at the School of Computing Science, University of Newcastle , with a focus on blockchain technology, cybersecurity, and privacy-preserving machine learning systems. His work bridges theoretical analysis with practical implementations in decentralized systems and financial technologies. Research Interests : Blockchain systems, smart contract security, federated learning, verifiable fairness in AI, and privacy-preserving technologies. Key Contributions : Development of the BlockSim simulation framework for blockchain, quantitative analysis of Ethereum's verifier dilemma, and frameworks for fairness-as-a-service in machine learning. Collaborations : Regularly works with researchers in cybersecurity (e.g., Mhairi Aitken, Ehsan Toreini), financial technology (e.g., Karen Elliott, Kovila Coopamootoo), and distributed systems (e.g., Han Wu, Lydia Chen). Publications : Over 170 works since 1992, with recent emphasis on blockchain scalability, AI ethics, and secure financial services. Tools : Co-designed OpBench for Ethereum opcode benchmarking and ADaCS for analyzing data collection strategies in security contexts.