Find Academic Supervisors

James MartinView profile

Professor

• Artificial Intelligence
• Cognitive Science
• Natural Language Processing
• +5 more

James Martin is a Professor of Computer Science at the University of Colorado at Boulder and a Fellow in the Institute of Cognitive Science. He holds a B.S. in Computer Science from Columbia University and a Ph.D. in Computer Science from the University of California at Berkeley. His research focuses on computational semantics, particularly how languages convey meaning to humans and computers, with a specific emphasis on metaphor processing and non-literal language analysis. He co-authored the widely used textbook Speech and Language Processing (3rd edition in progress), and his work extends to applications in healthcare and education through projects like the TalkMoves dataset analyzing classroom discourse. Current research includes cross-document event coreference resolution, AMR parsing tools (e.g., X-AMR), and AI-driven educational systems via the Institute for Student-AI Teaming (iSAT). Education History: Bachelor of Science in Computer Science, Columbia University Doctor of Philosophy (Ph.D.) in Computer Science, University of California, Berkeley Research Interests: Natural Language Processing (NLP) and Computational Linguistics Metaphor Analysis in Language and AI Semantic Parsing and Knowledge Representation AI Applications in Healthcare and Education Dialogue Systems and Classroom Discourse Analysis Recent Work Trends: His 2023-2024 publications emphasize multimodal NLP, cross-document semantic analysis (e.g., event coreference resolution), and AI tools for education (e.g., TalkMoves application for teacher feedback). NSF-funded National AI Institutes collaborations highlight strategic technology development. No scientific awards explicitly listed, though his textbook and research contributions are widely recognized in the field. Currently not actively recruiting PhD students. Labs/Teams: Core contributor to NLP initiatives at University of Colorado, particularly in semantic parsing and educational AI through iSAT. Involved with projects like the TalkMoves dataset and AMR annotation tools (CAMRA, X-AMR).

• Educational Technology
• Learning Analytics
• Computer-Supported Collaborative Learning
• +5 more

Minhong Wang is a Professor at the Faculty of Education, University of Hong Kong, with additional affiliation at the University of Edinburgh's Usher Institute. With an extensive publication record spanning over two decades from 2005 to 2025, Wang has established themselves as a leading researcher in educational technology and learning sciences. Their work bridges the gap between computer science and education, developing innovative technology-enhanced learning environments that support complex skill development and knowledge construction. Wang's research interests focus on educational technology, learning analytics, computer-supported collaborative learning, and cognitive mapping approaches. Their work examines how technology can enhance problem-solving processes, support self-regulated learning, and improve educational outcomes across various contexts. Recent research has expanded into multimodal learning analytics, teacher professional development through technology, and the application of advanced computational methods to educational challenges. Wang's approach integrates theoretical frameworks with practical implementations, creating systems that transform how educators and learners interact with digital environments. Analysis of Wang's recent publications (2023-2025) reveals a strategic expansion of research scope while maintaining core educational technology focus. The work demonstrates increasing sophistication in learning analytics methodologies, with growing integration of computer vision techniques and advanced machine learning approaches. This reflects a trend toward more comprehensive multimodal analysis of learning processes, moving beyond traditional text-based interactions to incorporate visual, spatial, and behavioral data in educational contexts. The research maintains strong practical applications while advancing theoretical understanding of how technology mediates learning. Wang has collaborated extensively with researchers across multiple institutions globally, particularly in Hong Kong, mainland China, and international partners. Their work demonstrates consistent funding support through numerous research projects, though specific grant details aren't visible in the publication record. The collaborative nature of the work suggests leadership in research teams focused on developing and evaluating innovative educational technologies.

Waqar AliView profile

Lecturer

• Pattern Recognition
• Graph Neural Networks
• Deep Learning
• +3 more

Mr. Waqar Ali is a Lecturer and Research Assistant at the ZHAW School of Engineering (Zurich University of Applied Sciences), affiliated with the Machine Perception & Cognition Group. His academic journey includes a Bachelor's (2016) and Master's (2019) in Computer Science from Comsats University. He has held teaching roles at Lahore Garrison University (2021) and Abasyn University (2019–2021). His research focuses on Pattern Recognition, Graph Neural Networks, Deep Learning, and Natural Language Processing. Awards include the Shahbaz Sharif Merit Scholarship (2017) and a Bachelor Fellowship from HEC Pakistan (2012). Notable projects include an Evidence-Based Diagnostic Assistance for Echocardiography initiative. His publications span advanced graph learning techniques, sentiment analysis models, and energy optimization systems. He has contributed to peer-reviewed journals and conferences such as the Joint IAPR Statistical Techniques in Pattern Recognition (2024). His work bridges theoretical machine learning with practical applications in healthcare diagnostics, smart grids, and Urdu language processing. Collaborative projects include roles at the University of Alicante (2024).

Henrik BulskovView profile

Associate Professor

• Natural Logic Systems
• Database Querying
• Machine Learning
• +3 more

Henrik Bulskov is an Associate Professor in the Department of People and Technology (Programming, Logic and Intelligent Systems) at Roskilde University. He holds an MSc and PhD. His work focuses on natural logic systems, database querying, and ontology-based information retrieval. He has contributed to projects such as SEAFACTS (digital maritime history platform) and NorDigHealth (regional digital health solutions). Education: MSc and PhD (specific disciplines not explicitly stated) Research interests include formal logic systems integration with databases, machine learning applications in bioinformatics, and semantic summarization through ontologies. Recent work explores query optimization in natural logic knowledge bases and disparity analysis in neural networks. His publications highlight advancements in computational logic for knowledge management and biomedical text analysis. Bulskov has participated in international conferences and contributed to media discussions on big data applications. Advising: No explicit student advisees listed Grants: Principal/Co-investigator in multiple projects including SIABO (2007–2012) and Duuoo Analysis (2021) Labs/Teams: Involved in interdisciplinary teams focusing on bioinformatics, health tech, and digital humanities through collaborative projects.

Mohammed MabkhotView profile

Research Fellow

• Aerospace Digitalization
• Model-Based Systems Engineering
• Industrial Automation
• +6 more

Mohammed Mabkhot is a Senior Research Associate in Aerospace Digitalization at the AVRRC: Centre for Virtual Engineering at Loughborough University (LBORO). His research focuses on digital-twin architectures, Model-Based Systems Engineering (MBSE), tacit knowledge management, and co-simulation environments in complex aerospace systems. He holds two PhDs: one in Cyber-Physical Production Systems Adaptation from Loughborough University and another in Knowledge-Based Management of Disruptions and Risks in Manufacturing Systems from King Saud University. He has over a decade of academic and industrial experience, including roles at King Saud University and the Intelligent Automation Centre. His work spans R&D projects like MAESTRO and IRaaS, and he has contributed to teaching modules in industrial engineering, operations management, and manufacturing processes. He is a Lean Six Sigma Black Belt and has published extensively in top-tier journals, co-chaired international conference sessions, and serves as a guest editor for Systems journal. Research interests include Industry 4.0 technologies, sustainability integration in manufacturing, and educational frameworks for engineer competency development. His articles highlight trends in digital twin applications, adaptation frameworks for production systems, and mapping Industry 4.0 technologies to UN sustainability goals. Notable awards include the Fourth-place prize in the PGR Research Competition (2019, KSU) and academic honors for top performance in his BSc and MSc programs. He has also built a strong academic-industrial network through collaborations in Europe and the UK.

Abdolreza AbhariView profile

Professor

• Artificial Intelligence
• Agent Systems
• Data Science
• +5 more

Dr. Abdolreza Abhari is a Professor and Undergraduate Program Director in the Department of Computer Science at Toronto Metropolitan University. His research focuses on Artificial Intelligence, Agent Systems, Data Science, Distributed Systems, and Network Simulation. He holds a PhD from Carleton University (2003) and has received notable grants including CFI, NSERC, and OGS funding. His professional roles include serving as Conference/Track Chair for Spring Simulation Conference and as an IEEE Senior Member. He leads the Data Science research group and the Distributed and Multimedia Processing (DSMP) lab. Awards include the Nortel Network Scholarship for Ph.D. studies and industry grants from AutoDesk Inc. His work spans simulation modeling, edge computing, and social network analysis. Education: PhD in Computer Science, Carleton University (2003). Research Interests: Dr. Abhari’s expertise includes developing simulation frameworks for distributed systems, optimizing network architectures, and applying machine learning to social networks. His work integrates theoretical models with practical applications in smart cities, video streaming, and fault-tolerant systems. Awards & Grants: Recipient of major research grants (CFI, NSERC) and industry partnerships. His contributions to simulation methodologies have been recognized through scholarships and collaborative projects. Advising & Labs: Leads the Data Science group and DSMP lab, focusing on edge computing, video analytics, and scalable data management. His research addresses real-world challenges in distributed systems and AI-driven solutions.

Zhaohan XiView profile

Assistant Professor

• AI Security
• Clinical AI
• Large Language Models (LLMs)
• +6 more

Zhaohan Xi is an Assistant Professor in the School of Computing at Binghamton University, SUNY, focusing on AI security/privacy and clinical AI in the context of large language models (LLMs). His research spans cybersecurity strategies, healthcare applications, and advanced AI techniques like graph learning and AutoML. He holds a PhD from Pennsylvania State University, with a visiting scholar stint at Stony Brook University's Computer Science Department. Education: PhD: Pennsylvania State University (2020–2024) Visiting Scholar: Stony Brook University (2023–2024) Master’s: Lehigh University (2016–2018) Bachelor’s: Nanjing University of Aeronautics and Astronautics (2012–2016) Research interests include: AI Security/Privacy: Backdoor attacks, adversarial defense, and LLM vulnerabilities Clinical AI: Cardiologist-level diagnostics, drug repurposing, and ECG analysis Cybersecurity: Threat hunting, red/blue teaming, and threat intelligence Graph Learning: Knowledge graphs, GNNs, and decision-making systems Recent publications emphasize LLM robustness (e.g., standardized testing benchmarks), adversarial knowledge extraction (e.g., stealing knowledge graphs via APIs), and cybersecurity applications (e.g., LLMs as threat intelligence tools). His work bridges theoretical AI security with practical healthcare and defense systems. Notable awards include ICLR Notable Reviewer (2025) and Binghamton’s Outstanding Service and Support Award (2025) . He has served as a NSF panelist (AI/cybersecurity) and reviewer for venues like ARR/EMNLP, ACL, and KDD. Internships include roles at Sony AI (2024), Microsoft (2023), and Uber (2022), focusing on AI research and software engineering. No advising details or grants are explicitly mentioned in the provided texts.

Berkant SavasView profile

Associate Professor

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +3 more

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

Rik SenguptaView profile

Part-Time Lecturer

• Graph Theory
• Algorithms
• Fair Allocation
• +3 more

Rik Sengupta is a Part-Time Lecturer at the University of Massachusetts Amherst, located in the Lederle Graduate Research Center. His research focuses on graph theory, algorithms, combinatorics, and fair allocation mechanisms. He has contributed to theoretical computer science, discrete mathematics, and algorithmic economics. His work spans dynamic graph streaming algorithms, fair division under structured constraints, and optimization problems with fairness metrics. He also explores graph reconstruction techniques, quantifier complexity in boolean functions, and Ramsey theory applications. Recent publications highlight trends in graphical allocation models, probabilistic graph analysis, and algorithmic solutions for resource distribution challenges. Notable areas include EFX allocations over graphs, time-aware fairness in knapsack problems, and approximation algorithms for housing markets. Rik holds no explicitly listed academic awards or grants. His advising activities are not detailed in the provided texts. He is affiliated with computational theory research groups but no specific labs or teams are mentioned.

Prathyush SambaturuView profile

Research Fellow

• Computational and Spatial Epidemiology
• Network Science
• Combinatorial Optimization
• +1 more

Dr. Prathyush Sambaturu is a Research Fellow in the Department of Biology at the University of Oxford, affiliated with the Kraemer research group. His work focuses on computational and spatial epidemiology, network science, combinatorial optimization, and complex systems. He designs agent-based network models to study infectious disease dynamics and develops surveillance strategies like genomic or wastewater monitoring to detect outbreaks and infer epidemiological parameters such as the reproduction number. His research interests include understanding how human mobility and population structure influence disease spread, as well as creating scalable algorithms for epidemic control. He also explores the impacts of climate change on migration patterns and associated disease risks. His articles span advanced computational methods applied to public health challenges, including graph-based active learning, fair disaster containment policies, and GPU-optimized intervention strategies. No scientific awards are explicitly mentioned. His research advisory role and grants information are not detailed in the provided texts. He is part of the Kraemer group, contributing to interdisciplinary projects at the intersection of epidemiology, network science, and algorithm design.

• Remote Sensing
• Machine Learning
• Environmental Science
• +3 more

Mingzhe Jiang is an Adjunct Assistant Professor at the University of Waterloo, focusing on remote sensing and machine learning applications in environmental science. His work emphasizes sea ice classification, SAR imagery analysis, and deep learning models for geospatial data. He is affiliated with the Adjunct Faculty group at the university. Research interests include developing advanced algorithms for sea ice dynamics, uncertainty quantification in climate models, and automated environmental monitoring systems. His contributions span Bayesian neural networks, graph convolutional networks, and semi-supervised learning frameworks for polar and maritime applications. Key publications (2016–2025) highlight innovations in SAR data processing, including hierarchical pipelines for sea ice segmentation, dual-polarized imagery analysis, and unsupervised clustering techniques. His work often integrates satellite data from platforms like RADARSAT-2 with machine learning to improve environmental parameter estimation. No scientific awards or grants are explicitly listed in the provided information. He has not reported advisees or laboratory affiliations in the text.

Jorn van der PolView profile

Lecturer

• Matroid Theory
• Combinatorics
• Discrete Mathematics
• +3 more

Jorn van der Pol is a Lecturer in the Department of Mathematics of Operations Research. His research focuses on matroid theory, combinatorics, and discrete mathematics, with notable contributions to the study of matroid enumeration, properties of matroids, and their applications in graph theory and optimization. His work often intersects with extremal combinatorics, hypergraph structures, and algorithmic enumeration techniques. Key research interests include Turán densities in hypergraphs, binary geometries, and the analysis of matroid minors. Recent collaborations explore the interplay between matroid theory and stochastic simulation methods. His publications span prestigious journals such as SIAM Journal on Discrete Mathematics and the Electronic Journal of Combinatorics, with an emphasis on open-access research. Van der Pol’s research trends highlight advancements in enumerative combinatorics, symmetry analysis in matroids, and probabilistic approaches to matroid structures. His work contributes to foundational questions in discrete mathematics and operations research, with applications in graph theory and algorithm design. No scientific awards are explicitly mentioned. His advising and grant activities remain unspecified in the provided texts. Research collaborations include studies on Earth and Planetary Sciences, though the primary focus remains matroid theory and combinatorial structures.

Kasimir ForthView profile

Researcher

• Circular Engineering
• Sustainable Building Design
• Building Information Modeling (BIM)
• +7 more

Kasimir Forth is a Researcher at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Professorship for Circular Engineering for Architecture. He is completing his PhD at Technical University of Munich (TUM) on BIM-based semantic enrichment using Large Language Models under Prof. André Borrmann. His research focuses on automating environmental assessments for sustainable buildings using digital methods like BIM and LLMs, particularly in early design stages. Currently, he contributes to the SWIRCULAR project, developing automated Digital Product Passports via BIM. Education: B.Sc. in Engineering Science (TUM) M.Sc. in Energy-Efficient and Sustainable Building (TUM, Distinction) Research Interests: BIM integration in sustainability analysis AI-driven semantic enrichment for environmental metrics Disassembly potential and circular economy applications Automated material passport generation Professional Experience: Former Managing Director of TUM’s Leonhard Obermeyer Center (Digital Methods for Built Environment) Consultant at Drees & Sommer (Energy Design/Sustainable Building) Key Projects: SWIRCULAR (automated Digital Product Passports), BIM4EarlyLCA (uncertain LCA visualization). Labs/Teams: Active in ETH Zurich’s Circular Engineering group and TUM’s Computational Modeling & Simulation lab.

Hongxu ChenView profile

Research Fellow

• Machine Learning
• Data Mining
• Recommender Systems
• +3 more

Dr. Hongxu Chen serves as an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. His research focuses on machine learning, data mining, and network analysis, particularly in the context of recommendation systems and graph-based models. He has contributed to advancements in graph neural networks, network representation learning, and social network analysis through collaborative projects with international researchers. Dr. Chen's work spans theoretical and applied domains, including social-boosted recommendation systems, graph convolutional networks for centrality analysis, and scalable tensor factorization models. His publications address challenges in link prediction, user behavior modeling, and mobility demand forecasting using graph-based and probabilistic approaches. While no formal awards are listed, his contributions reflect significant engagement with high-impact conferences like NeurIPS, ICDE, and KDD. No advising or grant details are provided in the text. His research activities are centered within the School of Electrical Engineering and Computer Science, though specific lab affiliations are not mentioned.

Wen HuaView profile

Research Fellow

• Data Science
• Machine Learning
• Data Mining
• +5 more

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis