Find Academic Supervisors

Yixin ChenView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +4 more

Yixin Chen is a Professor at Washington University in St. Louis , specializing in artificial intelligence, machine learning, and computational biomedicine. He joined the faculty in 2005 and has received prestigious awards such as the IEEE Fellow distinction and the DOE Early Career Award. His work bridges AI with healthcare, biotechnology, and interdisciplinary challenges like climate technology. Education: PhD, University of Illinois, 2005 MSc, University of Illinois, 2001 BSc, University of Science and Technology of China, 1999 Research focuses on human-AI collaboration, deep learning, and multi-omics data integration. Notable projects include AI-driven tools for synthetic biology, CO2 capture, and medical imaging improvements (e.g., NICUPose for neonatal care). He also leads clinical trials evaluating AI in surgery risk prediction (e.g., Perioperative ORACLE Trial). Awards include the St. Louis Innovator Award (2025), AAAS Fellowship (2025), and a $5M DOE grant for green fertilizer research. He serves as a NSF panelist and NSF reviewer, emphasizing interdisciplinary impact. Grants & Labs : Active in federal funding, including NSF and DOE projects. Lab work spans AI for healthcare, biotechnology, and environmental science, with collaborations in telemedicine and precision medicine.

Daisy Zhe WangView profile

Professor

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

Dr. Daisy Zhe Wang is a Professor in the Department of Computer & Information Science & Engineering at the University of Florida, affiliated with the Herbert Wertheim College of Engineering. She directs the Data Science Research (DSR) Lab, focusing on advanced data analysis systems using machine learning and probabilistic methods. Her work spans databases, data science, and informatics, with specializations in probabilistic knowledge graphs, multimodal fusion, and healthcare analytics. Education: PhD in Computer Science from the University of California, Berkeley (2011). Awards include the Arnold and Lisa Goldberg Rising Star Professorship in Computer Science (2019-2021) and the UF Term Professorship (2018). Her lab's current projects include developing systems for tree species classification using remote sensing and improving surgical risk prediction algorithms like MySurgeryRisk. Research emphasizes bridging data science with domain-specific applications, including electronic health records (EHR) analysis, environmental remote sensing, and knowledge graph-driven decision systems. She has pioneered frameworks like RAMQA for multimodal QA and M3 for multi-hop retrieval, advancing both theoretical and applied aspects of data science. Grants and collaborations include large-scale data competitions and partnerships with healthcare institutions. The DSR Lab maintains active research in AI ethics, explainable AI, and scalable data management systems, with ongoing work on neuro-symbolic architectures and multimodal learning.

Jie (Jack) YangView profile

Lecturer

• Natural Language Processing
• Large Language Models
• Multimodal NLP
• +4 more

Dr. Jie (Jack) Yang is a Lecturer in Database Systems & Big Data at the School of Computing and Information Technology, University of Wollongong. He holds a PhD from the same institution. His research focuses on Natural Language Processing (NLP), including Large Language Models (LLMs), Multimodal NLP, Machine Reading Comprehension, and Knowledge Graph applications across domains like Education, Healthcare, and Finance. He is affiliated with the Association for Computational Linguistics (ACL) and IEEE. Research Interests Dr. Yang's work emphasizes theoretical advancements and practical solutions in NLP. His recent projects include developing robust document retrieval systems, adversarial detection techniques, and enhancing AI models' robustness through masking and contrastive learning. He collaborates on applications like automated postural assessment using CNNs and AI-driven educational tools. Grants & Projects He leads projects such as AMKD.AI (Knowledge Graph toolkit), Next-Gen AIOT (interactive kiosks), and AI4U (AI competency initiatives). His funded research spans areas like LLM-based tourism data management, cybersecurity for AI models, and healthcare NLP applications. Teaching & Supervision Dr. Yang coordinates courses like Advanced Programming (CSCI851/CSCI251) and supervises PhD/Master’s students in topics like LLM explainability, skeleton-based action recognition, and adversarial learning. He emphasizes practical industry alignment in education and research. Labs & Teams He contributes to interdisciplinary teams advancing AI in education, healthcare, and industry collaboration through initiatives like the Telstra-UOW AIOT Hub and UOW’s Pretrained Language Models (PLMs) for medical document processing.

Jaya ChaturvediView profile

Researcher

• Natural Language Processing
• Pain and Mental Health
• Health Informatics
• +3 more

Dr Jaya Chaturvedi is a Research Associate in Health-Related Natural Language Processing at King’s College London’s Department of Biostatistics & Health Informatics, part of the Institute of Psychiatry, Psychology & Neuroscience (IoPPN). She holds a BDS from India, an MSc in Health Informatics from City, University of London, and a PhD from King’s College London. Her research focuses on applying NLP to mental health records, pain analysis, and improving healthcare data utilization. Research Interests: Natural Language Processing (NLP), Pain and Mental Health, Health Informatics, and Electronic Health Records (EHR). She has taught 'Natural Language Processing' and 'Python' modules at the MSc level. Jaya has contributed to projects like the Advance Choice Documents Implementation (ACDI) aiming to bridge research-to-practice gaps. Her recent work includes developing NLP models for pain mention detection in mental health records and knowledge graph embeddings for pain analysis. She has published extensively on EHR analysis, antipsychotic side effects, and multimorbidity in severe mental illnesses. Jaya is also involved in initiatives addressing mental health inequities and data linkage for population health studies.

Adele MarshallView profile

Professor

• Data Mining
• Bayesian Modelling
• Survival Analysis
• +8 more

Adele Marshall is a Professor at Queen's University Belfast (QUB), affiliated with the School of Mathematics and Physics, Mathematical Sciences Research Centre, and Intelligent Autonomous Manufacturing Systems. She holds roles such as Chair of the Statistics and Operational Research Examination Board and Programme Director of the MSc Data Analytics course, launched in 2017. Her research focuses on Survival Analysis, Bayesian Modelling, and healthcare applications, with notable contributions to Coxian phase-type distributions and infrastructure modelling. She has secured grants from national/international bodies and co-leads the Cumberland Initiative Group for healthcare policy impact. Education Pathways: Developed Mathematics, Statistics & Operational Research pathways (2005) and the MSc Data Analytics program. Research Interests: Combines statistical methodologies with real-world applications in healthcare, infrastructure, and data science. Recent work includes real-time patient flow simulations, knowledge graph expansions, and disease modeling for bovine tuberculosis. Awards: Elected member of the International Statistics Institute (2011). Grants & Projects: Leads projects like the Nudge-based intervention for ventilator wean (2019–present) and KTP collaborations with industry partners (e.g., SciLeads Limited, Terex GB Ltd). Outreach: Organized MathsQUBe, a public engagement initiative, and participated in events like the IEEE Computer-Based Medical Systems Symposium. Labs/Teams: Active in CenSSOR (Centre for Statistical Sciences and Operational Research) and collaborates with regional health groups on consultancy work.

Anton FeenstraView profile

Associate Professor

• Structural Bioinformatics
• Protein Structure Prediction
• Computational Biology
• +3 more

Anton Feenstra is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Bioinformatics department, as well as AIMMS and Integrative Bioinformatics. He holds a PhD (dr.) and an engineering degree (ir.). His research focuses on structural bioinformatics, protein structure prediction, computational biology, and bioinformatics algorithms. Key interests include protein-protein interactions, molecular dynamics, and knowledge graph applications in health and microbiota studies. Feenstra leads projects such as ELIXIR-NL (Digital Research Infrastructure) and has contributed to initiatives like BIOEXE and ENFIN. He teaches courses including Algorithms in Sequence Analysis and Fundamentals of Bioinformatics. His work spans over 86 publications, with recent contributions on protein interface prediction (PIPENN-EMB), microbiota-gut-brain axis analysis, and structural bioinformatics tools. Notable achievements include developing the PRALINE alignment toolkit and advancing machine learning methods for protein function prediction. Collaborations include work on Mycobacterium tuberculosis and SARS-CoV-2 protein analysis. His research aligns with UN Sustainable Development Goals, particularly in health and innovation.

Son Lam PhungView profile

Professor

• Image and Signal Processing
• Pattern Recognition
• Machine Learning
• +4 more

Professor Son Lam Phung is a faculty member at the University of Wollongong (UOW), holding the position of Professor in the School of Electrical, Computer and Telecommunications Engineering (SECTE) since 2022. He earned his B.Eng. (First-Class Honours) and Ph.D. in Computer Engineering from Edith Cowan University, Australia, where he was awarded the University Medal for academic excellence in 2000. His research focuses on image and signal processing, machine learning, and artificial intelligence, with applications in defense, healthcare, and autonomous systems. He has secured over 22 external grants from organizations such as the Australian Research Council (ARC), Qatar National Research Fund, and the Department of Foreign Affairs and Trade. Research Interests: Image and video processing Pattern recognition Machine learning and deep learning Assistive navigation systems for vision-impaired individuals Radar and sonar imaging for defense and environmental monitoring Grants & Projects: ARC Discovery Projects: 'Assistive Micro-navigation for Vision Impaired People' and 'Dynamic Visual Scene Gist Recognition' Qatar National Research Fund: 'Big Crowd Data Analytics' Defence Science and Technology Group: 'New Deep Networks for Iris-based Post-Mortem Identification' Awards & Recognition: Recipient of multiple best paper awards at IEEE conferences (ICASSP-2016, DICTA-2014) Highly Commended Supervisor Award (2012 Canon Extreme Imaging Competition) UOW Outstanding Contribution to Teaching Award (2010) Editorial Roles: Associate Editor, IEEE Access (IF: 3.4) Section Editor, Sensors Journal (Sensing and Imaging Section) Teaching & Supervision: Supervised 30 HDR students (20 PhD, 10 MPhil) to completion Teaching awards include the OCTAL Early Career Award (2010) Subjects include Digital Signal Processing, Embedded Systems, and Image Processing Labs & Teams: He leads the Centre for Signal and Information Processing (CSIP), focusing on algorithm development for defense, healthcare, and autonomous systems. His lab collaborates on projects involving AI-driven solutions for agriculture, environmental monitoring, and medical imaging.

James Z. WangView profile

Professor

• Bioinformatics
• Computer Networks
• Medical Imaging
• +6 more

James Z. Wang is a Professor in the School of Computing at Clemson University. He holds a B.S. and M.S. in Computer Science from the University of Science and Technology of China, and a Ph.D. in Computer Science from the University of Central Florida. He is a Senior Member of IEEE and ACM. His research focuses on bioinformatics, distributed systems, medical imaging, and data mining, with notable projects including G-SESAME for gene similarity analysis, ontology-based P2P information retrieval, and non-invasive skin cancer detection systems. He has taught numerous courses on databases, data mining, and multimedia systems since joining Clemson in 2005. Research Highlights: Developed tools for semantic similarity measurement in biomedical contexts (G-SESAME) Pioneered ontology-based approaches for P2P networks and proxy caching systems Advanced algorithms for distributed systems, self-stabilizing networks, and energy-efficient cloud computing Collaborated on medical imaging applications including melanoma detection and brain image analysis Publications reflect contributions to algorithms, distributed computing, bioinformatics, and multimedia systems, with over 100 peer-reviewed articles since 2000. His work bridges theoretical computer science with applied domains in healthcare and network infrastructure.

Alexander KotovView profile

Associate Professor

• Information Retrieval
• Natural Language Processing
• Health Informatics
• +3 more

Alexander Kotov is an Associate Professor in Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2011) and specializes in large-scale textual information analysis, particularly information retrieval, natural language processing, and health informatics. His research develops neural architectures for conversational entity retrieval (2024), clinical outcome prediction (2024), and behavioral intervention analysis. Key themes include knowledge graph integration, multimodal retrieval systems, and computational methods for health communication analysis, with applications in weight loss counseling and precision medicine. Research consistently bridges NLP/IR techniques with healthcare applications, exploring conversational AI for clinical support, knowledge graph retrieval, and behavioral pattern mining in therapeutic contexts. Recent projects include NIH-supported work on weight management chatbots.

Lihui LiuView profile

Assistant Professor

• Natural Language Processing
• Knowledge Graph Reasoning
• Large Language Models
• +2 more

Lihui Liu is an Assistant Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research focuses on neural-symbolic AI, integrating knowledge graphs with large language models for enhanced reasoning capabilities. Research Focus: Dr. Liu develops methods for complex knowledge graph reasoning, conversational question answering, and neural-symbolic integration. His lab creates algorithms that combine structural knowledge representations with language model capabilities for interpretable AI systems. Recent work demonstrates strong emphasis on knowledge graph applications (60% of publications) and language model enhancement (30%). Notable contributions include logical query decomposition techniques and attention-based graph neural network architectures. Mentoring: Actively recruiting PhD students to work on NSF-funded projects involving knowledge-guided AI systems. Professional service includes program committees for major AI conferences and editorial contributions.

Riccardo CantiniView profile

Assistant Professor

• Deep Learning
• Large Language Models
• Sustainable AI
• +3 more

Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).

Mohammad Al HasanView profile

Professor

• Data Mining
• Machine Learning
• Social Network Analysis
• +6 more

Mohammad Al Hasan is a Professor of Computer Science at the Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis. He previously worked at eBay Research Labs and holds a Ph.D. from Rensselaer Polytechnic Institute. His research focuses on data mining, machine learning, and social network analysis, with contributions to graph embeddings, network sampling, and natural language processing for knowledge graphs. He leads the DDML Research Group at IU Indy. Education: Ph.D. Computer Science, Rensselaer Polytechnic Institute (2009) M.S. Computer Science, University of Minnesota Twin Cities (2002) B.Sc. Computer Science and Engineering, Bangladesh University of Engineering and Technology (1998) Research Interests: Bioinformatics, Information Retrieval, Machine Learning, Social Network Analysis, and Algorithm Development for Text and Relation Embeddings. Al Hasan has authored over 100 research articles and received prestigious awards including the NSF CAREER Award. His work emphasizes interdisciplinary applications of data science. Labs/Teams: Founder of the Database, Data Mining & Machine Learning (DDML) Research Group.

Noorbakhsh Amiri GolilarzView profile

Research Professor

• Artificial Intelligence
• Image Processing
• Deep Neural Networks
• +2 more

Noorbakhsh Amiri Golilarz is Assistant Research Professor in Computer Science and Engineering at Mississippi State University. His research focuses on artificial intelligence applications in image processing, computer vision, and medical diagnostics. Key research areas include deep neural networks for image denoising, medical image analysis, and cybersecurity for AI systems. Recent work explores adversarial robustness, medical knowledge graphs, and generative model evaluation. Publications demonstrate strong methodological innovations in optimization algorithms and deep learning architectures applied across domains including healthcare, renewable energy, and industrial quality control.

Ashraf AboulnagaView profile

Professor

• Database Systems
• Cloud Computing
• Big Data
• +3 more

Ashraf Aboulnaga is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA). He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison and has held prior roles as Chief Scientist at the Qatar Computing Research Institute, Associate Professor at the University of Waterloo, and Research Staff Member at IBM Almaden Research Center. His research focuses on database systems, cloud computing, and graph analytics, with recent emphasis on cloud-native database architectures and scalable graph processing. Education: Ph.D. (2002), M.S. (1999) from University of Wisconsin-Madison; B.S./M.S. (1993/1996) from Alexandria University. Affiliations: ACM Distinguished Scientist (2015), IEEE Senior Member (2009). Research interests include cloud database systems, distributed transaction processing, and knowledge graph integration. He has authored over 100 publications in top venues like SIGMOD, VLDB, and ICDE, and received Best Paper Awards at SoCC 2015 and VLDB 2011. Grants: Current funding includes NSF travel support for ICDE 2024 and a $1.4M NSF MRI grant for DiscourseLab (2025). Past grants include DOE support for soil microbiome data integration. Service: Associate Editor for VLDB Journal, PVLDB, and Distributed and Parallel Databases. Served as Program Committee member for SIGMOD, ICDE, and EDBT. Industry collaborations include advising on cloud DBMS architectures and distributed systems.

• Data Mining
• Machine Learning
• Network Science
• +3 more

Tina Eliassi-Rad is the Joseph E. Aoun Professor at Northeastern University's Khoury College of Computer Sciences. She serves as core faculty at the Network Science Institute and external faculty at the Santa Fe Institute and Vermont Complex Systems Institute. Her research bridges data mining, machine learning, and network science with emphasis on trustworthy AI systems and societal impacts. Her current research programs focus on: Trustworthy Network Science: Enhancing explainability, robustness, and fairness in graph-based machine learning Just Machine Learning: Analyzing AI within broader sociotechnical systems to mitigate systemic risks Dr. Eliassi-Rad teaches graduate courses in Machine Learning with Graphs and has organized numerous workshops including the 2024 ICML Workshop on Humans, Algorithmic Decision-Making and Society. Her interdisciplinary work connects computer science with social science, biology, and policy studies.