Find Academic Supervisors

• Biomedical Informatics
• Public Health Informatics
• Clinical Research Informatics
• +6 more

Professor Adam Dunn is a leading academic in Biomedical Informatics and Digital Health at The University of Sydney , where he established the Discipline of Biomedical Informatics and Digital Health in 2020. With nearly 20 years of experience, his work integrates machine learning , natural language processing , and computational social science to address challenges in public health , clinical epidemiology , and evidence synthesis . His research programs focus on: (1) improving health information access and trust, (2) analyzing misinformation uptake via digital traces, and (3) developing AI tools for systematic review efficiency. Current projects include generative AI applications in patient discharge instructions , fairness in multimodal health AI , and infodemic burden measurement toolkits for WHO. Recent publications span clinical NLP , vaccine credibility , and social media surveillance . Awards include global recognition in medical informatics and editorial leadership roles at npj Digital Public Health and npj Digital Medicine. He has supervised over 15 PhD scholars and served on NHMRC and MRFF grant review panels. Key Projects: WHO Infodemic Toolkit, NLM R01 grant on ClinicalTrials.gov integration Expertise: AI in health, systematic review methodology, health information trust

• Biomarkers
• Epigenetics
• Aging
• +3 more

Brian Chen is an Assistant Adjunct Professor at the Herbert Wertheim School of Public Health & Human Longevity Science at the University of California San Diego (UCSD). His research focuses on biomarkers of aging, epigenetics, and epidemiological studies , with a particular interest in translating these findings into clinical applications and population health strategies. Education: PhD in Epidemiology, University of California Los Angeles (UCLA) MPH in Epidemiology/Biostatistics, University of California Berkeley Bachelor of Science in Biochemistry/Cell Biology & Psychology, UC San Diego Research Interests: Brian's work centers on understanding biological aging mechanisms through epigenetic changes, particularly DNA methylation patterns. He explores how these biomarkers correlate with age-related diseases (e.g., dementia, cardiovascular disease) and health disparities. His studies often involve large-scale epidemiological cohorts and cutting-edge genomic technologies. Publications: His recent work emphasizes validation of aging biomarkers, epigenetic clock analyses, and translational strategies for clinical implementation. Key contributions include studies on racial/ethnic health disparities linked to epigenetic aging and the role of biomarkers in predicting cardiovascular outcomes. Collaborations: Brian frequently collaborates with leading researchers like Steve Horvath (epigenetic clock pioneer), Luigi Ferrucci (aging epidemiology), and Tamara B. Horvath (neuroendocrinology). His work has been cited over 5,000 times in high-impact journals. Future Directions: Current projects include developing multi-omic aging biomarker panels and investigating interventions to modulate epigenetic aging processes. He also advocates for standardized validation frameworks for aging biomarkers in clinical settings.

• Artificial Intelligence
• Biomedical Informatics
• Drug Design
• +3 more

Marinka Zitnik is an Associate Professor of Biomedical Informatics at Harvard Medical School, Associate Faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence, and Associate Member at the Broad Institute of MIT and Harvard. Her research focuses on advancing artificial intelligence for medicine and science, including AI-driven drug design, genomic medicine, and multimodal data fusion. She leads initiatives such as the Therapeutics Data Commons and the International AI4Science initiative, emphasizing AI's role in scientific discovery and personalized healthcare. Research Interests: Dr. Zitnik's work integrates AI with biomedical challenges, developing geometric deep learning and multi-scale models to address therapeutic design, disease prediction, and precision medicine. Her lab pioneers approaches for contextualized AI systems and large pretrained models to analyze genetic, cellular, and molecular data for clinical insights. Labs/Teams: Zitnik Lab (focusing on AI in Biomedicine) and collaborations across institutions like the Broad Institute and Harvard Data Science Initiative. She also contributes to global initiatives like the AI4Science network.

Ke ChenView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Computational Cognitive Science
• +9 more

Dr. Ke Chen is a Senior Lecturer in the School of Computer Science at The University of Manchester, leading the Machine Learning and Perception (MLP@UoM) Lab. His research focuses on machine learning, deep learning, reinforcement learning, and their applications in intelligent systems, computer vision, and audio/speech processing. He has supervised over 50 PhD students and holds editorial roles in journals like Neural Networks and IEEE Transactions on Neural Networks . Dr. Chen has received awards such as the NSFC Distinguished Principal Young Investigator Award (2001) and JSPS Research Award (1993). Education: PhD in Computer Science (1990), with academic positions at institutions including Peking University, The Ohio State University, and Kyushu Institute of Technology. His professional activities include roles in IEEE Computational Intelligence Society committees and external examiner roles at universities like Essex. Research interests span computational cognitive systems, biometric authentication, and video game AI. Key contributions include advancements in deep architectures, reinforcement learning, and speaker-specific feature extraction. His lab, MLP@UoM, explores topics like explainable AI and transfer learning. Recent publications (2024) include work on goal-conditioned reinforcement learning and bias-resilient algorithms. He is actively involved in international conferences, serving as a keynote speaker and program committee member.

• Biomedical Engineering
• Electrical Impedance Tomography (EIT)
• Medical Device Design
• +6 more

Professor Alistair McEwan, affiliated with the University of Sydney's School of Biomedical Engineering, specializes in biomedical devices and instrumentation for neurological and cardiovascular diagnostics. As the Cerebral Palsy Chair of Technology and Engineering and Associate Head (External Engagement), he focuses on low-cost medical solutions for resource-limited settings, including neonatal care and stroke detection. His research bridges biophysics and clinical translation, with applications in electrical impedance tomography, wearable sensors, and neuroprosthetics. PhD from University of Oxford Member of the Sydney Nano Institute, Brain and Mind Centre, and Charles Perkins Centre His work explores how tissue electrical properties can enhance understanding of neural signaling and implant design. Clinical collaborators include the Bill & Melinda Gates Foundation for newborn nutrition monitoring. Recent projects involve AI-assisted critical care modeling, bionic devices for stress measurement, and adaptive control systems for neonatal resuscitation equipment.

• Internet of Things (IoT)
• Cybersecurity
• Sensor Networks
• +3 more

Shiwei Fang is an Assistant Professor in the Department of Computer & Cyber Sciences within the School of Computer and Cyber Sciences at the University of North Georgia. He holds a Ph.D. in Computer Science from the University of North Carolina (2021) and a B.E. in Computer Science from the State University of New York (2015). His research focuses on IoT systems, cybersecurity, sensor networks, and edge computing, with notable contributions to multimodal analytics, privacy visualization tools, and geospatial tracking datasets. He advises the Graduate Student Organization and serves on the SCCS Academic Web Oversight Committee. Education: Ph.D., Computer Science, University of North Carolina, 2021 B.E., Computer Science, State University of New York, 2015 Research interests include IoT security, context-aware systems, and sensor fusion. His work on IoBT-MAX and GDTM datasets highlights expertise in experimentation frameworks and geospatial tracking. Recent publications explore privacy risks in IoT, AR-based privacy visualization, and efficient inference models for edge computing. He has contributed to over 25 peer-reviewed articles since 2015, with a focus on real-world IoT applications and hardware-software co-design. Service roles include faculty advising and committee participation. He teaches courses like CSCI 3170/5170 on Computer Organization, bridging theoretical computer science with practical hardware concepts.

• Immersive Media
• Extended Reality (XR)
• Virtual Reality (VR)
• +6 more

Prof. Jan-Niklas Voigt-Antons is a Professor of Applied Computer Science specializing in Immersive Media at Hamm-Lippstadt University of Applied Sciences. His work focuses on Extended Reality (XR), Virtual Reality (VR), Augmented Reality (AR), and their applications in healthcare, education, and human-machine interaction. He holds a Dr.-Ing (Engineering Doctorate) and has extensive industry experience with organizations like Daimler AG, Telekom Innovation Laboratories, and the German Research Center for Artificial Intelligence (DFKI). Research interests include user experience (UX) design, emotion analysis in virtual environments, and accessibility for aging populations. He has led projects on wearable health tech, AR/VR usability, and immersive training systems. Notable areas of impact include developing guidelines for inclusive XR interfaces and advancing telemedicine through immersive technologies. Prof. Voigt-Antons has collaborated with Charité – Universitätsmedizin Berlin on healthcare applications and has contributed to standardization efforts in telecommunications. His technical competencies include agile project management, statistical analysis, and mobile application development for iOS/Android. He advises on UX evaluations, usability testing, and physiological data analysis for industry partners. Key achievements include creating the Story Time Dataset for video quality research and developing the ColorTable system for flavor perception studies. His work emphasizes real-world deployment, with projects addressing mobility challenges for seniors in rural areas and improving dementia care through tablet-based interventions.

Iti ChaturvediView profile

Lecturer

• Signal Processing
• Artificial Intelligence
• Social Media Analysis
• +5 more

Iti Chaturvedi is a Lecturer in the Department of Information Technology at James Cook University (JCU). She holds a Ph.D. in Computer Engineering from Nanyang Technological University, Singapore. Her research focuses on signal processing and AI applications in social media, including emotion recognition, speech analysis, and sentiment analysis. She has been recognized as a Top 2% Most Cited Researcher globally (2022) and received the JCU CSE Early Career Researcher Award (2020). She teaches courses such as Machine Learning and Data Science, Programming III, and Design Thinking I. Current research projects include sentiment prediction from social media (since 2020). She serves as an Associate Editor for the Expert Systems journal (2023) and has been an ARC Assessor (2020). Key contributions include work on speech emotion recognition, constrained manifold learning for videos, and multimodal emotion recognition systems. Her research outputs span journals like Expert Systems , Signal Processing , and conferences including IJCNN and AAAI.

Jingchao NiView profile

Assistant Professor

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +9 more

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

Lizhen QuView profile

Lecturer

• Natural Language Processing (NLP)
• Privacy-Preserving Methods
• Neuro-Symbolic AI
• +4 more

Dr. Lizhen Qu is a Lecturer at Monash University’s Faculty of Information Technology, part of the AIM Lab. His research focuses on robust and privacy-preserving neuro-symbolic methods for NLP and multimodal applications, including causal reasoning in dialogue systems, legal AI, digital health, and social NLP. Previously, he worked at Data61/CSIRO and completed his PhD at Saarland University and the Max-Planck-Institute for Informatics. Education: PhD in Computer Science from Saarland University and Max-Planck-Institute for Informatics. Research interests include integrating deep learning with logical reasoning, causal discovery, and ethical AI applications. He leads projects like TMLGenAI (Trusted Generative AI) and HARNESS (Neuro-Symbolic Systems), addressing model robustness and societal impact. Projects: TMLGenAI (2024–2026), HARNESS (2023–2027), and Accessible Data Exploration for Blind People (2023–2027) Contributions: Developed benchmarks like LazyReview and ACCESS, and co-organized ACL and IJCNLP workshops Research trends span causal discovery in NLP, federated learning for legal systems (e.g., FedLegal), and multimodal security. His work aligns with UN SDGs for innovation and health.

Yalong YangView profile

Assistant Professor

• Immersive Analytics
• Virtual Reality
• Data Visualization
• +3 more

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

• Artificial Intelligence
• Extended Reality
• Computer Science
• +3 more

Pasquale Cascarano is a fixed-term Assistant Professor at the University of Bologna , affiliated with the Department of the Arts . His research bridges computer science with creative industries (cinema, art, fashion) and biomedical applications, focusing on Artificial Intelligence and Extended Reality paradigms. He collaborates with national and international institutions on interdisciplinary projects. Institutional Affiliation: University of Bologna Academic Role: Assistant Professor (fixed-term) Research Interests center on integrating AI and XR into creative sectors and healthcare, with emphasis on: Generative AI for immersive environments Medical imaging and diagnostics Digital heritage preservation XR-based educational tools 3D visualization techniques Creative technology applications Recent Publications highlight trends in: Medical XR for surgical training AI-driven image restoration LLM integration with AR/VR Privacy in collaborative AR Fashion and cultural heritage digitization Deep learning for video enhancement

Samah FodehView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Machine Learning
• Biomedical Informatics
• +7 more

Samah Fodeh is an Associate Professor in the Department of Emergency Medicine at Yale School of Medicine, with a secondary appointment in Biostatistics and affiliation with the VA Connecticut Healthcare System. Her research focuses on leveraging AI and machine learning to improve patient-centered care, particularly in healthcare communication, opioid addiction, HIV outcomes, and pandemic response. She leads the Fodeh Lab and collaborates on NIH-funded projects like the Bridge2AI Program. Education: PhD in Biomedical Informatics from Michigan State University (2010). Research Interests: Her work integrates AI techniques to analyze patient-generated and clinical data, including social media and electronic health records. Key areas include detecting patient preferences, improving pain care quality, and addressing public health crises like opioid misuse and suicide risk through Twitter-based analyses. She also develops predictive models for fall risks in aging populations with HIV. Publications: Over 39 peer-reviewed articles, emphasizing AI-driven solutions in healthcare. Recent work includes risk models for HIV patients, opioid misuse detection on social media, and pandemic-related sentiment analysis. Labs/Teams: Director of the Fodeh Lab, part of Yale’s Data Mining and Health Informatics initiatives. Collaborates with interdisciplinary teams at the Yale School of Public Health and computational biology programs.

Hattie ChungView profile

Assistant Professor

• Systems Biology
• Computational Biology
• Single-Cell Genomics
• +4 more

Hattie Chung is an Assistant Professor of Medicine and Molecular, Cellular and Developmental Biology at Yale University, affiliated with the Cardiovascular Research Center. She holds appointments in multiple departments including Internal Medicine, Molecular Cell Biology, and the Yale Center for Research on Aging. Her research focuses on cellular heterogeneity and tissue organization using cutting-edge single-cell and spatial genomics technologies, addressing ovarian aging, cardiovascular disease, and drug perturbation modeling. Education: PhD in Systems Biology (Harvard University, 2016); BS in Biological Engineering (MIT, 2011). Postdoctoral training at the Broad Institute of MIT/Harvard. She leads an interdisciplinary team developing novel computational and experimental methods for genomic analysis. Research Interests: Systems biology approaches to cellular organization, single-cell genomics, spatial transcriptomics, translational medicine for cardiovascular diseases, and aging mechanisms. Her lab integrates computational models with experimental data to predict therapeutic responses and study disease mechanisms. Lab affiliations include the Yale Cardiovascular Research Center (YCVRC), Yale Center for Research on Aging (Y-Age), and the Program in Physical and Engineering Biology. Teaching includes MCDB 370: Biotechnology.

• Transportation modeling
• Applied operations research

Jee Eun (Jamie) Kang is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo's School of Engineering and Applied Sciences. Research focuses on transportation modeling and applied operations research, with applications in urban mobility, shared autonomous vehicles, and sustainable transportation systems. Education includes a PhD from UC Irvine. Research emphasizes data-driven approaches to travel behavior, electric vehicle adoption, and humanitarian logistics. Publications consistently address mobility innovation, including pricing strategies for emerging services, predictive analytics for transit, and optimization of shared transportation systems.