Find Academic Supervisors

Jingchen HuView profile

Associate Professor

• Bayesian Statistics
• Data Privacy
• Missing Data Analysis
• +3 more

Jingchen Hu is an Associate Professor of Mathematics and Statistics at Vassar College, where she has been since 2015. She holds a BS in Computing Mathematics from City University of Hong Kong and a PhD in Statistics from Duke University. Her research focuses on Bayesian statistical methodology, particularly in addressing missing data in surveys and generating synthetic datasets for privacy-protected public release. She has led collaborative projects on advancing Bayesian thinking in STEM education and privacy-preserving data synthesis. Education: BS in Computing Mathematics (City University of Hong Kong), MS and PhD in Statistics (Duke University). Research Interests: Bayesian methods for social sciences Privacy-preserving synthetic data generation Data utility and disclosure risk evaluation Curriculum development in Bayesian statistics Grants & Awards: Recipient of a 2023 NSF grant for collaborative research on Bayesian education and privacy methodologies. Teaching: MATH 240: Introduction to Statistics MATH 301: Topics in Advanced Mathematics and Statistics Labs/Teams: Collaborations with University of California, Irvine (UCI) and Duke University on NSF-funded Bayesian education initiatives and privacy-preserving data projects.

• Privacy
• Security
• Algorithmic Accountability
• +5 more

Bogdan Kulynych is a research scientist at Lausanne University Hospital in Switzerland, working within the Clinical Data Science group. He holds a Ph.D. in Computer Science from EPFL (Switzerland), where he was advised by Carmela Troncoso, and a B.Sc. in Applied Mathematics from Kyiv Mohyla Academy in Ukraine. His academic journey also includes a visiting fellowship at Harvard University with Flavio du Pin Calmon, and internships at Google and CERN. His research spans three interconnected domains: Algorithmic Accountability, Verification, and Reliability; Privacy-Preserving Learning and Statistics; and Algorithmic Systems in Healthcare. Kulynych develops methods for obtaining practical guarantees on model stability, robustness, and reliability, while also auditing these properties. His privacy work focuses on systems ensuring practical privacy guarantees with legally legible and interpretable operational risk analyses. In healthcare, he critically studies algorithmic system deployment in clinical practice through collaboration with clinicians and medical informatics practitioners. Kulynych's publication record demonstrates significant impact in top venues including NeurIPS, ICML, ICLR, FAccT, and PETS. His recent work addresses fundamental questions in differential privacy, operational privacy metrics, and healthcare AI applications. His research trend shows increasing focus on translating theoretical privacy guarantees into practical healthcare settings while addressing the social implications of algorithmic systems. Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy (NeurIPS 2025) (ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees (2025) Attack-Aware Noise Calibration for Differential Privacy (NeurIPS 2024) As an active member of the academic community, Kulynych regularly presents at major conferences and seminars, including recent talks at Harvard Privacy Tools Seminar, NeurIPS, and Imperial College London. His work has received media attention in The Guardian, Wired, The Verge, and CNET regarding algorithmic bias challenges. Kulynych maintains an active presence on Bluesky (@bogdankulynych) where he engages in critical discussions about AI ethics, privacy, and the societal implications of technology.

• Artificial Intelligence
• Knowledge Graphs
• Natural Language Processing
• +5 more

Tim Finin is a Professor in the Computer Science and Electrical Engineering department at the University of Maryland, Baltimore County (UMBC), where he serves as Director of the UMBC Center for Artificial Intelligence and holds the Willard and Lillian Hackerman Chair in Engineering. With over 50 years of experience, his research focuses on knowledge graphs, natural language processing, machine learning, and applications to information systems security and social media. Education: Ph.D. in Computer Science from the University of Illinois (1980), M.S. in Computer Science (1977), and S.B. in Electrical Engineering (1971) from MIT Current Roles: Director, UMBC Center for AI; Hackerman Chair; Professor, UMBC Previous Roles: Adjunct Associate Professor at University of Pennsylvania; positions at Unisys, JHU HLT CoE, and MIT AI Lab Research Interests: Dr. Finin's work spans Knowledge graphs and semantic web technologies Natural language processing for cybersecurity Machine learning for data assimilation Privacy and security in distributed systems Social media analysis Quantum computing applications Recent Grant Trends: His funded research includes projects on knowledge graph optimization, AI cybersecurity, semantic manufacturing standards, and quantum machine learning. Grants from DoD, NSF, NIST, and industry partners like IBM and Google demonstrate his interdisciplinary impact. Scientific Honors: ACM Fellow (2018) AAAI Fellow (2013) IEEE Technical Achievement Award (2009) UMBC Presidential Research Professor (2012) Fellow, Foundation for Intelligent Physical Agents (1997) Academic Leadership: Dr. Finin has chaired UMBC's Computer Science department, served on the Computing Research Association board, and held editorial roles including Editor-in-Chief of the Journal of Web Semantics (2005-2016).

Sunil AryalView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +10 more

Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.

Tim LandgrafView profile

Professor

• Machine Learning
• Robotics
• Computer Vision
• +4 more

Prof. Tim Landgraf is a Professor of Computer Science at Freie Universität Berlin, affiliated with the Dahlem Center for Machine Learning and Robotics. He specializes in artificial intelligence, robotics, and biohybrid systems, focusing on collective behavior, machine learning, and animal-robot interactions. His work bridges computational methods with biological systems, as seen in studies of honeybee communication and fish behavior. Key research interests include self-supervised learning algorithms, robotic swarm dynamics, and applications of AI in education and healthcare. Prof. Landgraf has led projects like the Biorobotics Lab and contributed to initiatives such as the H2020-funded HIVEOPOLIS. He has been honored with the 'Hochschullehrer des Jahres' (Professor of the Year) award in 2020. Recent publications emphasize biohybrid systems, explainable AI, and counterfactual explanations, reflecting his commitment to interdisciplinary innovation. His courses, such as 'Self-Supervised Learning,' highlight theoretical and practical aspects of cutting-edge machine learning techniques.

Enayat RajabiView profile

Associate Professor

• Machine Learning
• Knowledge Graphs
• Data Analytics
• +3 more

Dr. Enayat Rajabi is an Associate Professor of Business Analytics at the Shannon School of Business, Cape Breton University. Holding a PhD in Information and Knowledge Engineering from the University of Alcala (Spain) and a postdoctoral fellowship from Dalhousie University, his research focuses on the intersection of Machine Learning, Knowledge Graphs, and Data Analytics. He actively applies these technologies in healthcare, smart cities, and social media crisis response contexts. Education PhD in Information and Knowledge Engineering, University of Alcala (Spain) Postdoctoral Fellowship, Dalhousie University Research Interests His work bridges Knowledge Graphs with Machine Learning, emphasizing explainability and practical applications. Key areas include: Explainable AI for clinical decision-making Knowledge Graph applications in healthcare systems Social media analytics for emergency response Smart city data integration Generative modeling for tabular data Recent Publications Trends Recent articles highlight: Explainable AI in healthcare settings Industrial breakdown prediction systems Social media influencer detection Smart city infrastructure modeling Advanced data synthesis techniques Continued focus on Knowledge Graph applications

Enayat RajabiView profile

Associate Professor

• Machine Learning
• Knowledge Engineering
• Data Analytics
• +4 more

Dr. Enayat Rajabi is an Associate Professor of Data Analytics at the Shannon School of Business , Cape Breton University , Canada. He also serves as an Adjunct Professor at Dalhousie University and is affiliated with Nova Scotia Health as a scientist. His academic journey included a Ph.D. in Information and Knowledge Engineering from the University of Alcalá, Spain, and he has contributed extensively to machine learning and semantic web domains. Education: Ph.D. in Information and Knowledge Engineering, University of Alcalá, Spain (2015) Master of Software Engineering, Ferdowsi University of Mashhad, Iran (2004) Bachelor of Software Engineering, Razi University, Iran (2001) Dr. Rajabi's research focuses on machine learning , knowledge engineering , and semantic web applications in healthcare and smart cities. His work explores explainable AI frameworks, knowledge graph construction, and data-driven solutions for sustainable transportation and clinical decision support systems. His recent publications highlight trends in knowledge graph integration with large language models for healthcare, graph neural networks , and predictive analytics in urban environments. He has secured significant grants, including the NSERC Discovery Grant and Mitacs Research Training Award , to advance these domains. Scientific Contributions: NSERC Discovery Grant (2020-2025) - Semantic Web Analysis over Nova Scotia Open Data ($156,000) New Health Investigator Grant (2022-2024) - Machine Learning for ALC Patients ($97,418) Mitacs Globalink ($4,250) - Graph Neural Networks CBU RISE grants for Explainable Clinical Decision Support Systems and Multi-Label Text Classification Dr. Rajabi has mentored numerous research assistants across projects and maintains active collaborations with institutions in Canada, Spain, and Iran. His technical expertise spans Python, Tableau, Databricks, and PySpark, with teaching responsibilities in Predictive Analytics , Data Visualization , and Quantitative Methods .

• Artificial Intelligence
• Machine Learning
• Data Mining
• +12 more

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

Pengfei LiView profile

Professor

• Finite Mixture Models
• Empirical Likelihood
• Experimental Design
• +5 more

Roles & Affiliations Professor at Department of Statistics and Actuarial Science , University of Waterloo since 2012. Serves as Associate Editor for Canadian Journal of Statistics and Metrika . Previously held roles at University of Alberta (2008-2011) and University of British Columbia (2008 postdoc). Education PhD in Statistics (2007) - University of Waterloo MA in Statistics (2004) - Nankai University BSc in Statistics (2001) - Nankai University Research Focus Specializes in statistical methodologies including finite mixture models, empirical likelihood, experimental design, biased sampling problems, capture-recapture data, and density ratio models. Current interests include statistical inference under constraints and analysis of non-probability survey samples. Awards CRM-SSC Prize in Statistics (2022) Institute of Mathematical Statistics Fellow (2022) Multiple awards for teaching and research excellence from University of Waterloo Students & Grants Advised over 10 PhD/Master’s students. Active grants include NSERC funding since 2009 and startup grants from University of Waterloo. Supervises postdocs in statistical methodology development.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +2 more

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

Nikola SimidjievskiView profile

Research Fellow

• Machine Learning
• Biomedical Data Integration
• Neuroscience
• +4 more

Dr. Nikola Simidjievski is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology. His research focuses on machine learning applications in natural sciences, including oncology, neuroscience, and biomedical informatics. He specializes in multimodal data integration, dynamic systems modeling, and explainable AI techniques. Simidjievski contributes to interdisciplinary projects such as AI-driven space research and Earth observation systems (e.g., AIAtlas). His work emphasizes computational methods for small-sample biomedical data and interpretable neural networks. Education background not explicitly stated, but his research spans computational biology, aerospace telemetry analysis (e.g., Mars Express spacecraft), and environmental modeling. He collaborates on projects like GalaxAI for spacecraft data analysis and PATHS for medical imaging. Key tools developed include Healnet for biomedical data fusion and RO-FIGS for tabular ensemble methods. No scientific awards listed. Advising no listed students, but contributes to academic initiatives like the Human Brain Project's neuroscience training programs. Engages in open-source projects (e.g., AITLAS toolbox for Earth observation). Current research trends include tabular data augmentation methods (TabEBM, TabMDA) and efficient transformer-based models for medical imaging (PATHS).

• Geo-AI (Geospatial Artificial Intelligence)
• XR (Extended Reality)
• Machine Learning
• +6 more

Dr. Abolghasem Sadeghi-Niaraki is an Associate Professor in the Department of Computer Science and Engineering at Sejong University's College of Engineering, where he has served since 2017. He leads the eXtended Reality (XR) Research Center and holds a Research Professor position at the Super-Realistic XR Technology Research Center (2022-2030). Previously, he was an Assistant Professor at INHA University's Department of Geo-Informatics Engineering (2009-2017). Recently appointed as a Fellow at Harvard University's Spatial Data Lab (SDL), he conducts collaborative research in spatiotemporal data analytics and geo-AI. Dr. Sadeghi-Niaraki's research spans Geo-AI, XR technologies, Machine Learning, Metaverse applications, and IoT. His work focuses on applying AI to geospatial problems, developing immersive technologies for education and cultural heritage, and creating predictive models for natural hazards. He has secured significant funding for XR Metaverse research from the Ministry of Science and ICT, focusing on Real-Virtual integration. His interdisciplinary approach combines computer science with environmental science, urban planning, healthcare, and education. His research portfolio demonstrates strong trends in geospatial AI applications for environmental monitoring, particularly in natural hazard prediction (floods, wildfires, earthquakes), as well as innovative educational applications of VR/AR. Recent work shows increasing integration of large language models with immersive technologies and a growing focus on healthcare applications within virtual environments. Dr. Sadeghi-Niaraki has received significant recognition including: Top 2% Scientist Worldwide (Stanford-Elsevier 2024) Fellow at Harvard SDL, Center for Geographic Analysis Australian Endeavour Fellowship Recipient Member of IEEE & AAG (American Association of Geographers) He has led over $9.3 million in research funding, supervised 40+ Master's and 6+ Ph.D. students, and collaborated with 14 universities across 8 countries in immersive technology development. His laboratory work centers on the eXtended Reality Research Center at Sejong University, where his team develops patented methods for context-aware AR/VR systems applied to tourism, cultural heritage, and smart city planning.

Larry P. HeckView profile

Professor

• Artificial Intelligence
• Conversational Systems
• Machine Learning
• +5 more

Larry P. Heck is a Professor at the Georgia Institute of Technology with joint appointments in the Schools of Electrical and Computer Engineering and Interactive Computing. He holds the Rhesa S. Farmer Distinguished Chair of Advanced Computing Concepts and is a Georgia Research Alliance Eminent Scholar. His career spans academia, industry, and research leadership, with notable roles at Microsoft, Google, Samsung, and SRI International. BSEE, Texas Tech University (1986) MSEE, Georgia Institute of Technology (1989) PhD in Electrical Engineering, Georgia Institute of Technology (1991) His research focuses on Artificial Intelligence , particularly in Conversational Systems , Machine Learning , and Natural Language Processing . Key subfields include dialogue systems, speech/speaker recognition, multimodal interaction, and large language model (LLM) governance. Recent publications emphasize dataset creation, intent modeling, reinforcement learning, and ethical AI practices. Scientific awards include IEEE Fellow , National Academy of Inventors Fellow (2025) , and multiple accolades from Texas Tech and Georgia Tech. He has led groundbreaking work in deep learning for speech processing and holds over 50 U.S. patents.

• Database Systems
• Data Management
• Machine Learning
• +4 more

Tova Milo is a leading academic in database systems and data management at Tel Aviv University. Her research focuses on advancing automated data analysis, machine learning, and efficient data management techniques. She has contributed to systems like LINX (language-driven data exploration), TabEE (tabular embeddings explanations), and DPClustX (differentially private clustering explanations). Her work bridges database theory and practical applications, addressing challenges in fraud detection, crowdsourcing, and large language model utilization. Key contributions include: Development of systems for automated data exploration (e.g., ATENA, LINX) Foundations for explainable AI in databases (FEDEX, TabEE) Algorithms for category tree construction and cost-effective data processing Techniques for photo archiving under storage constraints She has received the 2022 TCDE Impact Award and actively contributes to top venues like SIGMOD, VLDB, and ICDE. Her research emphasizes balancing theoretical rigor with real-world applicability.

Tim HolzheimView profile

Researcher

• Knowledge Graphs
• Semantic Web
• RDF
• +5 more

Tim Holzheim is a researcher at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, affiliated with Lehrstuhl Informatik 5 (Database and Information Systems Group). He is actively involved in teaching and research, particularly in the areas of Knowledge Graphs, Semantic Web, and data integration technologies. Institution: RWTH Aachen University School: Faculty of Mathematics, Computer Science and Natural Sciences Department: Department of Computer Science Research Group: Lehrstuhl Informatik 5 (DBIS) Email: holzheim@dbis.rwth-aachen.de His research focuses on Knowledge Graphs , Semantic Web technologies , Wikidata , RDF , and GraphQL for semantic data access . He explores methods for knowledge extraction, data integration, and enabling efficient querying and hosting of large-scale semantic datasets. His work bridges theoretical semantic modeling with practical implementation in data systems. The recent publications highlight a consistent research trajectory in semantic data engineering, particularly in making knowledge graphs more accessible, interoperable, and usable through tools for bootstrapping APIs, analyzing property structures, and semantifying academic data. His contributions appear in workshops co-located with top-tier conferences such as ISWC and ESWC. There are no listed scientific awards or honors in the provided information. Tim Holzheim is involved in academic advising and teaching, offering courses such as Datenbanken und Informationssysteme and Knowledge Graph Lab . While no formal students are listed, his supervision likely includes thesis projects and lab participants. He is also engaged in research projects such as NFDI4DS, contributing to national data infrastructure initiatives. He is a member of the Database and Information Systems (DBIS) research group led by Prof. Dr. S. Decker, which focuses on scalable and semantic data management solutions. The group is active in developing tools and methodologies for knowledge graphs, data spaces, and semantic integration.