Find Academic Supervisors

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +7 more

Dr Estara Arrant is a Cambridge University Library Leverhulme Early Career Research Fellow and Trinity Hall Postdoctoral Research Associate. She merges Semitic linguistics with data science to study medieval Jewish and Islamic textual cultures. PhD (University of Cambridge, 2021): Computational analysis of Cairo Genizah Torah codices MPhil (University of Oxford, 2018): Linguistic structure of Qur’ānic creation narratives BA: History & Modern Hebrew Her groundbreaking work combines machine learning and bioinformatics algorithms to analyze textual evolution. Current projects include: Developing the first NLP model for medieval Judaeo-Arabic Creating digital tools for computerized stemmatology She actively teaches Python and R as an Cambridge Digital Humanities associate, and her research has been published by Open Book Publishers and Brill .

• Semitic Linguistics
• Medieval Jewish Studies
• Medieval Islamic Studies
• +12 more

Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health

• Machine Learning
• Digital Health
• Human Sensing
• +3 more

Tuğba Dalyan is an Associate Professor in the Department of Computer Engineering at Istanbul Bilgi University, Faculty of Engineering and Natural Sciences. She holds a Ph.D. in Computer Engineering from Yıldız Technical University (2014), an MSc from Kocaeli University (2007), and dual BSc degrees in Mathematics and Computer Science and Business Administration (Minor) from Istanbul Bilgi University (2003). She has been a faculty member since 2016 and previously served as a Teaching Staff member and Research Assistant at the same institution. Her research focuses on Natural Language Processing , Machine Learning , Deep Learning , Text Mining , Data Science , and Big Data Analytics . Her work spans computational linguistics, sentiment analysis, author profiling, machine translation, and smart systems. She has led and contributed to numerous research projects, particularly in AI-driven urban solutions and health technologies. The most recent publications show a strong trend in Turkish NLP, zero-shot classification, multimodal AI (image captioning), emotional robotics, and decision support systems using fuzzy logic. Her work combines theoretical rigor with practical applications in smart cities, education, and healthcare. Best Paper Award , CICLing 2012 TÜBİTAK 2209-A student project awards (2022–2024) Horizon2020 Eşik Üstü Ödülü , MIMOSCSA 2024 TÜBİTAK 2242 competition: 2nd and 3rd place (2016, 2018) She has advised numerous student research projects, many of which have received national recognition. She has directed multiple TÜBİTAK and institutional research grants, including projects on smart homes, blockchain crowdfunding, mental health, and AI for social polarization. Her leadership roles include Head of Department, Vice Dean, and Director of Graduate Programs. Tuğba Dalyan leads research in AI and NLP with a strong emphasis on Turkish language technologies. She is involved in interdisciplinary teams working on emotional robots, smart city platforms, and citizen science ecosystems. Her lab activities focus on neural networks, text analysis, and intelligent systems development.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +7 more

Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.

• Artificial Intelligence in Medicine
• Biomedical Natural Language Processing
• Health Informatics
• +3 more

Shawn Miller is a Lecturer in Philosophy at the University of California, Davis, where he also earned his PhD. His work focuses on philosophy of biology, science studies, and ethical inquiries. He maintains a prominent resource at philbio.net , curating a comprehensive reading list for philosophy of biology graduate programs. His research interests include functional analysis in biology, evolutionary theory critiques, and scientific methodology. He has contributed to debates around adaptationism, gene-environment interaction, and feminist approaches in science. Miller's published works appear in Anarchist Review of Books , The Montréal Review , and Crack the Spine . His 2016 article ' There is No Future Tree ' explores temporal ontology, while recent publications reflect interdisciplinary engagement with anarchist thought and cultural critique. He has maintained active involvement in journalism, writing for outlets like Sacramento News & Review and The Pacific Sun , often blending philosophical perspectives with social commentary. His creative writing includes poetic reflections on existence and ethical decision-making.

• Philosophy of Biology
• Philosophy of Science
• Ethics
• +3 more

Mohammed J. Zaki is a Professor and Department Head at the Department of Computer Science , Rensselaer Polytechnic Institute . He co-directs the NSF IUCRC Center for Research Towards Advancing Financial Technologies (CRAFT) and has authored the textbook Data Mining and Machine Learning (2nd Ed, Cambridge University Press, 2020). Research Focus : Novel data mining techniques for graph-structured and textual data with applications in bioinformatics, personal health, and financial analytics. Leadership : Co-chair of BIOKDD workshops, Associate Editor for multiple journals, and Program Co-chair for SIGKDD, SDM, ICDM, and other top conferences. Scientific Awards include: EDBT'24 Test of Time Award NSF CAREER Award DOE Early Career Award HP Innovation Research Award Google Faculty Research Award Fellowships: IEEE, ACM, AAAS, SIAM Recent Publications highlight advancements in: Knowledge Graph Completion with Directed Attention Multi-Sense Embeddings for Language Models Financial Trend Prediction via Graph Neural Networks Health-Guided Recipe Recommendation Systems Triplet Interaction in Molecular Graph Learning Temporal Personal Health Data Analysis

• Data Mining
• Machine Learning
• Graph-structured Data Analysis
• +6 more

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).

• Data Mining
• Machine Learning
• Reinforcement Learning
• +9 more

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +5 more

Andrey Vladimirovich Savchenko is a prominent researcher and educator in computer vision and artificial intelligence at the National Research University Higher School of Economics (HSE) in Nizhny Novgorod. He holds multiple positions including Professor at the Faculty of Informatics, Mathematics, and Computer Science, Leading Researcher at the Faculty of Computer Science and Institute of Artificial Intelligence and Digital Sciences, and Academic Director of the "Artificial Intelligence and Computer Vision" educational program. His educational background includes: 2016: Doctor of Technical Sciences from Nizhny Novgorod State Technical University 2015: Academic title of Associate Professor 2011: Candidate of Technical Sciences 2008: Specialist degree in Applied Mathematics and Computer Science Savchenko's research focuses on computer vision, pattern recognition, and artificial intelligence, with particular emphasis on facial recognition, emotion analysis, and efficient deep learning algorithms. His work bridges theoretical foundations with practical applications, especially in mobile computing environments where computational resources are limited. He has developed innovative methods for making AI systems more efficient without significant loss in accuracy. His recent publications demonstrate a strong trend toward multimodal analysis, combining visual, audio, and textual data for more robust recognition systems. There's a clear emphasis on making AI systems more efficient, especially for mobile devices, and on developing methods that can work with limited computational resources while maintaining high accuracy. His work spans fundamental research on neural network architectures and practical applications in education, healthcare, and human-computer interaction. Among his notable scientific achievements: Gratitude from the Governor of Nizhny Novgorod region (2022) Multiple gratitude awards from HSE (2021-2022) Best Teacher Award (2018-2019) Leaders of IT Industry Award from NEYMARK IT Campus (2023) Academic Success Bonus at HSE (2011-2013) Savchenko has successfully supervised numerous master's students and currently mentors PhD candidates working on cutting-edge topics like large language models for recommendation systems and document analysis. He has secured significant research funding, including projects with Huawei, Sberbank, and the Russian Science Foundation, totaling millions of rubles. His laboratory focuses on developing efficient algorithms for computer vision and multimodal data analysis. He leads the Laboratory of Theoretical Foundations of Artificial Intelligence Models and has established strong industry partnerships that ensure his research has practical impact. His NVIDIA Deep Learning Institute certification demonstrates his commitment to staying current with the latest AI technologies.

• Computer Vision
• Artificial Intelligence
• Pattern Recognition
• +6 more

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

• Data Management
• Graph Computing
• Machine Learning
• +5 more

Ian Dryden is a Professor of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a Ph.D. from the University of Leeds (1989) and has held academic positions at Florida International University, University of Nottingham, University of Chicago, and multiple stints at USC. His research focuses on statistical methodology for highly-structured data, including shape analysis, functional data analysis, and object-oriented data analysis. He has authored over 150 publications and two influential books: Statistical Shape Analysis (2016) and Object-Oriented Data Analysis (2022). Dr. Dryden’s work spans applications in medical imaging, corpus linguistics, environmental science, and Bayesian spatial modeling. Recent projects include using remote sensing for peatland monitoring and Bayesian hierarchical models for urban deprivation analysis in Tanzania. He has secured over $20M in grants as PI/Co-I and is an elected Fellow of the Institute of Mathematical Statistics. His research outputs emphasize methodological innovation, with key contributions to shape trajectory analysis, manifold-valued data regression, and Riemannian manifold smoothing splines. Collaborative work includes interdisciplinary projects in genetics, pharmacology, and environmental science, reflecting his broad statistical expertise and impact across disciplines.

• Statistical Shape Analysis
• Object-Oriented Data Analysis
• Functional Data Analysis
• +3 more

Corrado Loglisci is an Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research focuses on Temporal Data Mining , Machine Learning , and Quantum Computing , with applications in bioinformatics, medical informatics, and cybersecurity. He earned his Ph.D. in Computer Science with a thesis on temporal projection in longitudinal data. Research Highlights : Temporal Learning, Textual Data Mining, Quantum-Classical Hybrid Systems Collaborations : IRSTEA Research Institute (France), Aristotle University of Thessaloniki (Greece) His publications address dynamic network analysis , emotion detection in social media , and quantum-enhanced classification . He contributes to program committees and journal editorial work, including a special issue on Mining Complex Patterns in the Journal of Intelligent Information Systems . Notable contributions include the jKarma framework for change detection and studies on concept drift robustness in intrusion detection systems. His work spans European/National research projects, leveraging machine learning for tasks like mobile crowd sensing trustworthiness prediction (2020) and investor behavior analysis (2023-2025).

• Temporal Data Mining
• Temporal Learning
• Textual Data Mining
• +13 more

Hamza M. Zafer is an Associate Professor of Arabian and African languages at the University of Washington, holding a Senior Lecturer position in the Program in African Studies. His research focuses on the languages and literatures of the Red Sea world from the 6th to 16th centuries, emphasizing pre-modern knowledge networks across Ethiopic, Arabic, Coptic, Sabaic, and Syriac traditions. His monograph, *Ecumenical Community*, examines early Muslim communitarianism in the post-Red Sea war context. Dr. Zafer earned his Ph.D. from Cornell University. His research interests span cross-cultural textual exchanges, linguistic anthropology, and the historiography of the Horn of Africa and Arabian Peninsula. His work bridges historical linguistics with socio-cultural analysis, highlighting trans-regional intellectual exchanges. His recent publications (2003–2009) include machine learning applications in bioinformatics, such as tumor classification and gene function prediction, reflecting an interdisciplinary approach. Earlier work (1997–1999) explored AI-driven crossword puzzle generation, showcasing computational creativity. No academic awards or grants are explicitly mentioned in the provided text. He advises no listed students, though his mentorship role in language studies may imply graduate supervision not detailed here. No lab affiliations or future projects are described in the available data.

• Red Sea World Studies
• Pre-Modern Knowledge Networks
• Ethiopic Literature
• +4 more

Annette ten Teije is a Full Professor at Vrije Universiteit Amsterdam (VU Amsterdam) with appointments in the Faculty of Science, Artificial Intelligence department, the Network Institute, and the Knowledge Representation and Reasoning research group. Her academic career spans several decades with a strong focus on the intersection of artificial intelligence and healthcare applications. Professor ten Teije's research interests center around Knowledge Representation, particularly in medical contexts. Her work bridges multiple domains including Semantic Web technologies, Ontology development, Neuro-Symbolic AI systems, and Clinical Decision Support. She has made significant contributions to the formalization of clinical guidelines, handling multimorbidity in healthcare systems, and developing design patterns for hybrid AI systems. Her research integrates machine learning with symbolic reasoning to create explainable and reliable AI systems for healthcare applications. Analysis of Professor ten Teije's recent publications reveals a strong trajectory toward neuro-symbolic AI approaches that combine the strengths of neural networks and symbolic reasoning. Her work increasingly focuses on explainability in medical AI systems, with numerous publications on feature selection, interaction detection, and narrative-based understanding. She has developed frameworks for shared understanding in multi-agent systems and created design patterns specifically for medical decision-making contexts. Her research consistently bridges theoretical AI advances with practical healthcare applications. Professor ten Teije has supervised 5 PhD theses as indicated in her academic profile and teaches courses including "AI in Health" and "Machine Learning and Reasoning for Health" for the 2024-2025 academic year. Her academic contributions extend to editorial work, including serving as editor for conference proceedings and special issues on Knowledge Representation for Healthcare Processes.

• Artificial Intelligence
• Knowledge Representation
• Medical Knowledge Representation
• +7 more