Find Academic Supervisors

Paola FlocchiniView profile

Professor

• Distributed Computing
• Distributed Algorithms
• Sense of Direction
• +3 more

Paola Flocchini is a Professor and University Research Chair on Intruder Agents and the Decontamination of Communication Networks at the University of Ottawa's School of Electrical Engineering and Computer Science. She also serves as Associate Director of the OCICS Program. Her PhD is from an unspecified institution. Her research focuses on distributed computing, algorithms for autonomous robots, distributed systems, and cellular automata. She has contributed to advancing theoretical frameworks for mobile robotic networks, fault-tolerant systems, and dynamic network exploration. Research Interests: Distributed Computing Mobile Entities and Autonomous Robots Algorithm Design for Dynamic Networks Black Hole Detection and Network Decontamination Asynchronous Algorithms and Schedulers Her recent work emphasizes computational power of energy-constrained robots, pattern formation under sequential schedulers, and fault localization in dynamic topologies. She has published extensively in venues like ICALP, IWOCA, and DISC. Her research bridges theory and application, addressing challenges in distributed systems, robotics, and network security.

Petr GolovachView profile

Research Professor

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +6 more

Petr Golovach is a Research Professor at the Department of Informatics, University of Bergen. His research focuses on Discrete Mathematics and Theoretical Computer Science, particularly graph theory, algorithms, parameterized complexity, and clustering. He has held academic positions at Syktyvkar State University (1991–2007), Durham University (2009–2011), and currently teaches advanced courses like Advanced Algorithms Techniques and Enumeration Algorithms at the University of Bergen. His research interests include graph algorithms, parameterized complexity, matroid theory, and algorithmic enumeration. He has organized notable events like the Dagstuhl Seminar 2018 and serves on program committees for STACS, IPEC, and SWAT. Notable contributions include work on hybrid clustering algorithms, graph cuts under matroid constraints, and parameterized tractability of path and cycle problems. He has supervised PhD students like Nidhi Purohit and master’s students including Øyving Stette Haarberg and Andreas Steinvik. His publications span over 200 peer-reviewed papers in top venues such as Journal of the ACM and conferences like SODA and ICALP. His research bridges extremal combinatorics with algorithm design, emphasizing practical and theoretical advancements in graph-based problems.

Davide BacciuView profile

Professor

• Machine Learning
• Graph Neural Networks
• Continual Learning
• +5 more

Davide Bacciu is a Full Professor of Machine Learning at the University of Pisa, where he leads the Pervasive Artificial Intelligence Laboratory (PAILab) and coordinates the EIC-Pathfinder EMERGE project. He is also Director of AI Research at Aptus.AI and founder of ContinualIST and QuantaBrain startups. His academic roles include Vice President of AIxIA (Italian Association for Artificial Intelligence) and former Senior Editor of IEEE Transactions on Neural Networks and Learning Systems. He holds a PhD in Computer Science from IMT Lucca and is affiliated with the Department of Computer Science at the University of Pisa. His research focuses on machine learning fundamentals, graph neural networks, continual learning, and applications in robotics and healthcare. Key projects include the EMERGE initiative exploring collective awareness in AI systems and QuantaBrain’s diagnostic tools for autism spectrum disorders. Bacciu’s work emphasizes interdisciplinary collaboration, integrating AI with cyber-physical systems and societal impact. His contributions span foundational theory (e.g., causal abstractions, graph structure learning) and applied domains (e.g., autonomous systems, medical AI). He has organized workshops on AI for Space exploration and Geometric Deep Learning, showcasing his commitment to advancing AI frontiers. Education: PhD in Computer Science (IMT Lucca), MSc & BSc from University of Pisa. Labs/Teams: PAILab, EMERGE project, QuantaBrain startup. Grants/Projects: EIC Pathfinder EMERGE, TEACHING_H2020, AI for Space initiatives. His recent publications address challenges in dynamic graph learning, continual learning, and causal discovery. Bacciu advocates for a national AI strategy in Italy, stressing long-term vision and ethical AI governance aligned with EU regulations like the AI Act.

James CaverleeView profile

Professor

• Machine Learning
• Recommender Systems
• Information Retrieval
• +7 more

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.

Marya BazziView profile

Assistant Professor

• Network Science
• Time-Dependent Networks
• Multilayer Networks
• +7 more

Marya Bazzi is an Honorary Assistant Professor at the University of Warwick. Her research focuses on developing methods for analyzing large time-dependent data, with a primary focus on Network Science. She investigates mesoscale structures in networks such as community detection, core-periphery structures, and multilayer networks, applying these tools to domains like online media, biology, finance, and digital humanities. Her work includes contributions to scalable graph representation learning (Local2Global framework), semantic change detection using the UK Web Archive, and computational journalism. She co-organizes academic initiatives like Data Science for Social Good, Data Study Group, and workshops on media in the digital age. Her research bridges algorithmic development with practical applications, emphasizing interdisciplinary collaboration. Key publications span Machine Learning, Network Science, and Computational Biology, reflecting her expertise in both theoretical frameworks and applied problem-solving. While no awards are explicitly mentioned, her active participation in organizing academic activities demonstrates leadership in fostering collaborative research environments.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .

• Optimization and Metaheuristics
• Supply Chain Management
• Behavioral and Cognitive Sciences
• +3 more

Dr. Hanane El-Raoui is a Lecturer in Management Science at the University of Strathclyde, affiliated with the Strathclyde Business School. Her research focuses on interdisciplinary applications of optimization, behavioral modeling, and AI to enhance occupational safety and supply chain efficiency. She holds a PhD from the University of Granada (2022) in Models and Computational Intelligence for Food Supply Chain Delivery. Her work integrates behavioral and cognitive sciences, Bayesian statistics, and machine learning to predict human behavior in safety contexts. Key projects include agent-based modeling for decision-making processes and serious games for risk preference elicitation. She co-leads the EPSRC-funded project on sustainable garment operations (2022–2026). El Raoui’s recent research emphasizes smart factory safety via spatio-temporal worker movement analysis and digital twin technologies. She received the 2025 FAIM Sullivan Best Paper Award for contributions to circular business models. Her advisory roles include managing grants and overseeing PhD student development in organizational safety and efficiency.

He LiView profile

Assistant Professor

• Multiscale Computational Modeling
• Artificial Intelligence in Medicine
• Biomedical Engineering
• +3 more

Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.

Tony JebaraView profile

Professor

• Machine Learning
• Computer Vision
• Graph Theory
• +3 more

Professor Tony Jebara is a faculty member in the Department of Computer Science at Columbia University, where he chairs the Center on Foundations of Data Science and directs the Columbia Machine Learning Laboratory. His research focuses on machine learning with applications in vision, graphs, and spatio-temporal data. He holds a PhD from MIT (2002) and has advised startups including Sense Networks, Evidation Health, and Agolo. Notable awards include the NSF Career Award (2004), Best Paper at ICML 2009, and recognition as one of Esquire's Best and Brightest (2008). His work has been featured in major media outlets. Jebara's academic contributions span over 100 peer-reviewed papers and a textbook on machine learning. He served as General Chair for ICML 2017 and Program Chair for ICML 2014. His research explores generative and discriminative models, Bayesian inference, and optimization techniques. Current projects include neural ensemble analysis in neuroscience and robust learning algorithms for environmental modeling. Recent publications emphasize scalable methods for collaborative filtering, survival analysis in online experiments, and graphical model applications in neuroscience. His work bridges theoretical advancements with practical systems, including contributions to privacy-preserving algorithms and recommendation systems.

Shenghua GaoView profile

Associate Professor

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +4 more

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

David S. ChoiView profile

Associate Professor

• Statistics
• Machine Learning
• Network Analysis
• +5 more

David S. Choi is an Associate Professor at Heinz College, Carnegie Mellon University, which houses the Schools of Public Policy and Information Systems. He maintains a courtesy appointment in the Department of Statistics at the same institution. His academic credentials include a Ph.D. in Electrical Engineering from Stanford University under Benjamin Van Roy. Professional experience encompasses roles as a technical staff member at MIT Lincoln Laboratory, postdoctoral research at Harvard University's School of Engineering, and a visiting postdoc at UC Berkeley's Department of Statistics. Choi's research centers on statistical and machine learning methodologies for network-structured data. His work pioneers community detection techniques, latent variable network modeling, unsupervised learning frameworks, and causal inference methods that account for interference in social networks. These innovations address fundamental challenges in analyzing interconnected systems across public policy, healthcare, and social sciences. Publication analysis reveals consistent contributions to causal inference in networked experiments, particularly in characterizing interference and spillover effects. His scholarship spans network clustering algorithms, biological network modeling, and policy evaluation methodologies, with impactful work published in Journal of the American Statistical Association, PNAS, and Annals of Statistics. No scientific awards were documented in the source material. The available information does not reference doctoral students supervised or research grants managed by Professor Choi. Specific research laboratories or collaborative teams were not mentioned in the provided documentation.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +4 more

Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.

• Semantics
• Knowledge Graphs
• Natural Language Processing
• +4 more

Vanessa Lopez is a Senior Research Scientist and manager of the AI for Health and Social Care team at IBM Research Ireland since 2012. Her work focuses on enhancing AI systems through semantics and knowledge graphs to improve healthcare and scientific discovery. IBM Research Ireland, AI for Health and Social Care team Research interests: Semantics, Knowledge Graphs, NLP, Drug Discovery, Clinical Trials Optimization Her recent publications emphasize multimodal knowledge integration for accelerated discovery, biochemistry model evaluation, and peptide bioactivity prediction frameworks. She has contributed to open-source tools like Zshot and Otter-Knowledge. 2022 IBM Outstanding Technical Achievement Award 2017 US-Ireland Research Innovation Award 2022 IBM Patent Master Inventor Vanessa leads projects like Claims Audit (fraud detection), Cognitive Care Mentor (patient-centric analytics), and BlueLENS (knowledge graph querying). She has co-organized AI/semantic web challenges and served on editorial boards.

Jason J. JungView profile

Professor

• Computer Science
• Knowledge Engineering
• Social Media Analysis
• +3 more

Jason J. Jung is a Professor in the Department of Computer Engineering at Chung-Ang University, Seoul, Korea. His academic work focuses on knowledge engineering , social media analytics , and data mining within the Knowledge Engineering Laboratory. Research Interests : Computer Science, Social Knowledge, Data Modeling, Sentiment Analysis Projects : IoT-based cultural systems, real-time social event detection, transmedia storytelling models The 15 most recent publications (2014-2018) demonstrate expertise in social network analysis , multimodal data processing , and context-aware systems applied to urban services, cultural tourism, and digital storytelling. Key trends include real-time analytics , trust modeling , and collaborative frameworks for O2O services. Professional activities include editorial contributions, invited talks, and patent developments. Students at all levels (PhD/MSc/BSc) conduct research under his supervision at the Knowledge Engineering Laboratory. Personal interests include travel, film, painting, literature, and music.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +11 more

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.