Find Academic Supervisors

Zahraa AbdallahView profile

Senior Lecturer

• Machine Learning
• Data Science
• Time Series Analysis
• +6 more

Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)

Serena BoothView profile

Assistant Professor

• Human-AI Interaction
• Reinforcement Learning
• AI Policy
• +3 more

Serena Booth is an incoming Assistant Professor in Computer Science at Brown University. Previously, she served as an AAAS AI Policy Fellow in the U.S. Senate, advising the Senate Banking Committee on AI policy. She holds a PhD from MIT CSAIL (2023) and a BA from Harvard College (2016). Her research focuses on human-AI interaction, specification design for AI systems, and ethical AI practices. She also worked as an Associate Product Manager at Google, scaling ARCore to 100 million devices. Her research explores how humans specify AI behaviors, assess system success, and mitigate misalignment risks. Key contributions include Bayes-TrEx (model transparency via Bayesian sampling) and RoCUS (robot controller understanding). Her work has been supported by NSF GRFP and MIT Presidential Fellowships. She advocates for science policy equity through MIT's Science Policy Initiative and co-founded initiatives to support women in computing (e.g., GW6 at MIT). Education: PhD MIT CSAIL (2023), BA Harvard College (2016) Awards: Rising Star in EECS, HRI Pioneer, NSF GRFP Key Areas: Reward design pitfalls, human-robot trust, ethical AI curriculum development Her recent publications analyze reward function misdesign (AAAI 2023), human-AI teaching frameworks (HRI 2022), and feature attribution reliability (AAAI 2022). She currently seeks PhD students/postdocs focusing on human-AI alignment, reinforcement learning, and policy implications.

• AI Algorithms
• Economic Inequality
• Pricing Algorithms
• +3 more

Shunyuan Zhang is an Assistant Professor at Harvard Business School with research focusing on AI algorithms, economic inequality, and computer vision applications in business contexts. His work examines how algorithmic systems impact economic outcomes, particularly in sharing economy platforms like Airbnb. His research interests include AI algorithms, economic inequality, pricing algorithms, machine learning, computer vision, and the sharing economy. Zhang's work often combines technical computer vision approaches with economic analysis to understand platform dynamics. Zhang's recent publications demonstrate a strong focus on the intersection of AI, fairness, and economic outcomes. His work analyzes how algorithmic pricing affects racial disparities on platforms like Airbnb, and how visual content impacts demand in the sharing economy. His research employs sophisticated methodologies including deep learning, structural modeling, and causal inference. He has published in top journals and working paper series, with notable work including 'Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb' and 'What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features.' Zhang collaborates extensively with leading researchers at Carnegie Mellon University and University of Toronto, particularly on topics related to algorithmic fairness and platform economics. His work has significant implications for both academic understanding and practical policy recommendations regarding algorithmic systems in marketplace contexts.

Helge SteinView profile

Professor

• Digital Catalysis
• Battery Research
• Materials Science
• +3 more

Prof. Helge Stein is a Professor in the Department of Chemistry at the Technical University of Munich (TUM), leading the Professorship for Digital Catalysis. He holds a doctorate in mechanical engineering from Ruhr University Bochum (summa cum laude) and conducted postdoctoral research at Caltech before joining TUM in 2023. His research focuses on accelerating materials discovery and optimization through digital tools like machine learning, robotics, and data management, with applications in catalysis and battery systems. Stein has pioneered the development of Materials Acceleration Platforms (MAPs) to streamline experimental and computational workflows globally. Education: Bachelor/Master in Physics, Georg August University of Göttingen (2008–2013) Doctorate in Mechanical Engineering, Ruhr University Bochum (2017, summa cum laude) Postdoc, California Institute of Technology (2017–2020) Tenure-track Professor, Karlsruhe Institute of Technology (2020–2023) Research Interests: Integration of robotics and AI in materials research High-throughput experimentation for battery and catalysis systems Data-driven approaches to nonlinear material-behavior analysis Development of decentralized Materials Acceleration Platforms (MAPs) Key Awards: ACS Engineering Au Rising Star (2023) Kit Innovation Award (2023) Masao Horiba Award (2021) Eickhoff Prize (2018) Teaching: Leads courses on high-throughput methods, data management in chemistry, and digital catalysis seminars. Collaborates with interdisciplinary teams across TUM's School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence. Labs/Teams: Directs research groups focused on automated electrochemistry, materials robotics, and AI-driven battery design, with partnerships spanning academia and industry for global MAP implementations.

Bharanidharan ShanmugamView profile

Associate Professor

• IoT Security
• Cyber Risk Management
• Network Security
• +3 more

Dr. Bharanidharan Shanmugam is an Associate Professor in Information Technology at Charles Darwin University's Faculty of Science and Technology. He specializes in cybersecurity, IoT security, and cyber risk management in microgrids. His research focuses on addressing real-world challenges in IoT, smart grids, and medical devices to enhance community impact. **Research Interests:** - IoT Security - Cyber Risk Assessment in Microgrids - Network and Information Security - Applied Cybersecurity Solutions - Blockchain and Privacy-Preserving Technologies **Key Projects (2019–2025):** - Cyber Territory Skills Hub (2023–2025) - Renewable Energy Microgrid Hub (2021–2024) - Blockchain-Based Digital Identity (2019–2020) **Publications:** Focuses on IoT security frameworks, intrusion detection systems, and AI-driven cybersecurity solutions. Recent works include studies on smart grid load forecasting, medical IoT threat detection, and water leakage detection using machine learning. **Grants & Supervision:** Principal Investigator on multiple ARC-funded projects. Supervises PhD students in DevSecOps and IoT security. Active in organizing workshops on digital awareness for indigenous communities.

Kourosh DavoudiView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

Karim ZKIKView profile

Associate Professor

• Cybersecurity
• Blockchain Technologies
• Artificial Intelligence
• +3 more

Karim ZKIK is an Associate Professor of Cyber Security and Information Systems at ESAIP Graduate School of Engineering, Angers, France. Previously, he served as an Assistant Professor at the International University of Rabat (UIR), Morocco. His roles include Educational Manager of the Cyber Security track, Head of the Cybersecurity Innovation Hub, and committee member for ABET certification and curriculum design. He actively contributes to academic service, organizing conferences such as the International Conference on Cryptology, Coding Theory, and Cyber Security (I4CS 2022), and serves as a Guest Editor for Computers and Industrial Engineering . His research focuses on cybersecurity for connected systems, blockchain technologies, AI-driven security solutions, and cyber resilience in industrial control systems. Recent work explores integrating blockchain and machine learning for threat detection, secure IoT networks, and supply chain resilience. Key contributions include frameworks for cyber resilience in retail and airlines, blockchain-based crowdfunding security, and SDN-based attack mitigation. ZKIK holds a Habilitation (2024) and PhD in Cyber Security from Université d’Angers and Mohamed V University, Rabat. He holds over 20 certifications from EC-Council, IBM, and Cisco. His work bridges theoretical research and industry applications, addressing challenges in smart environments, industrial systems, and sustainable supply chains.

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +6 more

Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.

• 5G/6G Networking
• Network Slicing
• Intent-Based Networking
• +5 more

Adlen Ksentini is a Professor at EURECOM, a leading graduate school and research center in Sophia Antipolis, France, specializing in digital science and communication systems. His extensive research focuses on next-generation mobile networks (5G/6G), network management, and the integration of artificial intelligence with telecommunications infrastructure. Dr. Ksentini actively contributes to major EU research initiatives including 6G-BRICKS and AC3, serving as a key researcher and project leader in the development of future network architectures. Dr. Ksentini's research interests center around network slicing, intent-based networking, edge computing, and the application of machine learning to network management problems. His work bridges theoretical advancements with practical implementations in 5G/6G systems, with particular emphasis on zero-touch network management, energy efficiency optimization, quality of service assurance, and the integration of large language models with network operations. His research has significantly contributed to the development of O-RAN (Open Radio Access Network) frameworks and the evolution of network automation. His recent publication trends reveal a strategic shift toward AI-native network architectures, with increasing focus on integrating large language models (LLMs) with network management systems. His work demonstrates a clear progression from traditional network management approaches to more autonomous, AI-powered systems capable of intent-based configuration, self-optimization, and predictive maintenance. The publications show strong emphasis on practical implementations within the 6G research ecosystem, addressing critical challenges in network slicing, resource allocation, and energy efficiency. As a research supervisor, Dr. Ksentini mentors several PhD students including Abdelkader Mekrache, Karim Boutiba, Bouziane Brik, and Houda Hafi, who frequently appear as co-authors on his publications. His research is primarily funded through major EU research projects such as 6G-BRICKS (Building Reusable Testbed Infrastructures for Cloud-to-Device Breakthrough Technologies) and AC3 (which focuses on Cloud Edge Continuum). Dr. Ksentini is actively involved with the 6G-BRICKS project consortium and the AC3 project team, where he contributes to developing next-generation network architectures that integrate communication, computing, and sensing capabilities. His work within these projects focuses on creating reusable testbed infrastructures and addressing security and trust management challenges in the cloud-edge continuum.

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +6 more

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

Ozgur YilmazView profile

Professor

• Applied and Computational Harmonic Analysis
• Signal Processing
• Compressed Sensing
• +5 more

Ozgur Yilmaz is a Professor in the Department of Mathematics at the University of British Columbia (UBC). He is the Director of the Pacific Institute for the Mathematical Sciences (PIMS) and has held roles such as Interim Deputy Director at PIMS and Deputy Director at the Banff International Research Station (BIRS). His research focuses on applied harmonic analysis, signal processing, compressed sensing, and seismic signal processing. Education: PhD in Applied and Computational Mathematics from Princeton University (2001), B.Sc. in Mathematics and Electrical Engineering from Boğaziçi University (1997). Research Interests: Mathematical problems in analog-to-digital conversion, blind source separation, sparse approximations, compressed sensing, and their applications in seismic exploration. He has contributed to advancements in sigma-delta quantization, low-rank matrix recovery, and compressed sensing algorithms. Funding: Recipient of NSERC Discovery Grants, UBC Data Science Institute grants, and leadership in collaborative research groups (CRGs) on high-dimensional data analysis and applied harmonic analysis. His work bridges theoretical mathematics with practical applications in signal processing and AI-driven medical imaging. Students and Postdocs: Supervised numerous PhD and MSc students in areas like compressed sensing, seismic data reconstruction, and machine learning. Current advisees include Aaron Berk and Xiaowei Li. Former students hold positions at academic institutions and tech companies. Labs and Collaborations: Affiliated with UBC’s Data Science Institute (DSI), Centre for Artificial Intelligence Decision-making and Action (CAIDA), and the Institute of Applied Mathematics (IAM). Collaborates on projects integrating AI with scientific discovery, such as retinal biomarker identification using deep learning.

Eman El-SheikhView profile

Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +6 more

Dr. Eman El-Sheikh is a Professor and Associate Vice President at the University of West Florida (UWF), affiliated with the Hal Marcus College of Science and Engineering, the Department of Computer Science, and the Center for Cybersecurity. She is a globally recognized leader in artificial intelligence, machine learning, and cybersecurity, with over 30 years of experience and more than $26 million in competitive research funding. Ph.D. in Computer Science and AI, Michigan State University (2002) M.Sc. in Computer Science, Michigan State University (1995) B.Sc. in Computer Science, American University in Cairo (1992) Her research centers on the integration of Artificial Intelligence and Machine Learning in cybersecurity, with a strong emphasis on education, workforce development, and ethical AI. She leads the development of AI-Cyber curricula for national adoption and is a pioneer in experiential learning models such as the Cybersecurity for All® and CyberSkills2Work® programs. The trends in her recent publications reflect a consistent focus on AI-driven cybersecurity solutions, secure AI systems, national workforce development, experiential education, and inclusive cyber training. Her work bridges technical innovation with policy, leadership, and pedagogy, demonstrating a holistic approach to strengthening the cybersecurity ecosystem. Dr. El-Sheikh has received numerous prestigious honors, including: 2024 GISEC Global Educator of the Year 2024 and 2025 Cybersecurity Woman of the Year (World Finalist) UWF Million Dollar Research Hall of Fame ($25M and $1M levels) 2020 Women Leaders in Cybersecurity, Security Magazine Achievement Award for Advancing Cybersecurity Education (SAM 2016) NSF/NSA AI-Cyber Task Force Member (2024–2025) Founder, Women in Cybersecurity (WiCyS) Florida Affiliate She has led major grant-funded initiatives such as the National Cybersecurity Workforce Development Program and CyberSkills2Work®, securing over $26 million to advance cybersecurity education and training. She mentors students and emerging professionals, particularly through diversity and inclusion efforts. Dr. El-Sheikh also serves as Chief Strategic Alliance Officer and USA Ambassador for the Global Council for Responsible AI, influencing national and global AI policy and education standards. She founded and leads the Artificial Intelligence Research Group at UWF and is actively involved in national task forces and advisory boards, including the Florida Cybersecurity Task Force and the WiCyS Florida Affiliate Advisory Board. Her leadership extends to shaping the future of AI and cybersecurity through collaborative research, innovation in higher education, and strategic partnerships.

Daniel RoyView profile

Professor

• Machine Learning
• Statistical Learning Theory
• Foundations of Statistics
• +11 more

Daniel Roy is a Full Professor at the University of Toronto, holding cross-appointments in the Department of Statistical Sciences, Computer Science, Electrical and Computer Engineering, and the Department of Computer and Mathematical Sciences at UTSC. He is also a Canada CIFAR AI Chair and Research Director at the Vector Institute, reflecting his leadership in AI and machine learning research. His educational background includes a PhD, MEng, and BSc in Computer Science from MIT, where his doctoral work earned the MIT EECS Sprowls Award. Prior to joining Toronto, he was a Newton International Fellow at the Royal Society and a Research Fellow at Emmanuel College, University of Cambridge. His research centers on foundational principles in machine learning, statistics, and probabilistic reasoning. Key interests include statistical learning theory, Bayesian nonparametrics, probabilistic programming, and information-theoretic generalization. His work bridges theoretical computer science, mathematical logic, and applied probability. His recent publications, appearing in ICML, NeurIPS, COLT, and JMLR, reflect a strong focus on theoretical advances in generalization, online learning, and stochastic optimization. Themes include minimax rates, conditional mutual information, and the role of data in PAC-Bayes bounds. His group has made foundational contributions to probabilistic programming, including work on Church and the computability of conditional probability. NSERC Discovery Accelerator Supplement Ontario Early Researcher Award Google Faculty Research Award Newton International Fellowship MIT EECS Sprowls Award Daniel Roy advises numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. His group actively collaborates with leading researchers in machine learning and statistics. He is also an Action Editor for the Journal of Machine Learning Research and Transactions of Machine Learning Research, underscoring his role in shaping the field. He leads a vibrant research group focused on theoretical machine learning and probabilistic modeling, and maintains active collaborations with institutions such as MIT, Cambridge, and the Vector Institute. He is also the founder and maintainer of the probabilistic-programming.org wiki, a key resource in the community.

Vassilis ChristophidesView profile

Professor

• Databases
• Web Information Systems
• Big Data Processing
• +9 more

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Gonzalo NápolesView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Cognitive Science
• +5 more

Gonzalo Nápoles is an Assistant Professor at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. He holds a Doctoral Degree in Computer Science from Rough Cognitive Networks (2014–2017). His research focuses on AI applications in cognitive modeling, pattern classification, and neural networks with interdisciplinary applications in healthcare, finance, and social sciences. Key research areas include Fuzzy Cognitive Maps, data augmentation techniques for neuroimaging, and interpretable machine learning systems. He actively contributes to UN Sustainable Development Goals related to education and innovation. Recent work explores AI ethics, financial risk assessment using dynamic networks, and sensory processing disorder analysis through neural networks. Education: Doctoral Degree in Computer Science, 2017 (Thesis: Rough Cognitive Networks) Prize-winning research includes Best Paper Awards at CIARP 2021 and IWAIPR 2023. He collaborates internationally, hosting academic visitors and serving on multiple PhD committees. Current projects involve stock prediction using graph neural networks and fMRI data augmentation methodologies. Awards: Best Paper Award - CIARP 2021 Best Paper Award - IWAIPR 2023 Nápoles advises on PhD theses in cognitive science and AI applications. His work bridges theoretical advancements with practical implementations in healthcare, finance, and urban systems.