Find Academic Supervisors

Abdulhussain E. MahdiView profile

Professor

• Text Analytics
• Knowledge Organisation
• Information Retrieval
• +8 more

Professor Abdulhussain E. Mahdi is a Professor in the Department of Electronic and Computer Engineering at the University of Limerick, Ireland. He leads the Text Analytics & Knowledge Organisation (TAKO) Research Group and serves as Director of the Regional Peer-Supported Learning Centre and Joint Director of the ICT Learning Centre at UL. Education: BSc in Electrical Engineering (1st class) from the University of Basrah (1978) PhD in Electronic Engineering from the University of Wales, Bangor, UK (1990) SEDA Accredited Teacher of Higher Education (SEDA & University of Plymouth, UK, 1998) Research Interests: Professor Mahdi's research spans text data mining & analytics, knowledge organisation & linked data, information retrieval & extraction, and signal, speech, and NLP. He also focuses on learning analytics, inquiry-based learning, and collaborative student-centred active learning. His work integrates advanced computational techniques for semantic analysis and educational applications. Publications Trends: His recent publications (2014-2018) demonstrate a strong focus on text analytics, particularly in Arabic text classification, semantic relatedness, and the application of text mining to educational contexts such as analyzing StackOverflow for computer science learning. Additionally, he has made contributions to sensor technology, especially displacement current contactless sensors for biopotential measurement. Advising and Grants: While specific details of grants and advisees are not provided, Professor Mahdi's extensive publication record (over 150 peer-reviewed articles) and leadership of the TAKO Research Group indicate active supervision of students and successful grant acquisition. Labs and Teams: He leads the TAKO Research Group, which focuses on text analytics and knowledge organisation. Additionally, he directs the Regional Peer-Supported Learning Centre and the ICT Learning Centre, fostering collaborative learning environments and technological innovation in education at UL.

Janna HastingsView profile

Assistant Professor

• Medical Knowledge Systems
• Artificial Intelligence in Healthcare
• Ontology Development
• +4 more

Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.

Asif SalekinView profile

Assistant Professor

• Human-Centered Computing
• Machine Learning
• Cyber-Physical Systems
• +12 more

Asif Salekin is an Assistant Professor at Arizona State University's School of Biological and Health Systems Engineering (SBHSE), where he directs the Laboratory for Ubiquitous and Intelligent Sensing (UIS Lab). He holds additional affiliations with the School of Medicine and Advanced Medical Engineering, serves as a Research Affiliate at the Mayo Clinic, and maintains affiliations with SUNY Upstate Medical University and Syracuse University. Having joined ASU as a tenure-track Assistant Professor in August 2024, he previously served as an Assistant Professor at Syracuse University from 2020-2024. His research spans Human-Centered Computing, Machine Learning, Cyber-Physical Systems, and Usable Sensing Security and Privacy within Ubiquitous Computing, with a core focus on integrating computing solutions to advance health assessment and monitoring. His work addresses natural distribution shifts in human-centered applications, algorithmic fairness and bias mitigation, multimodal integration, interpretability of ML inference in healthcare, scalable edge computing solutions, trustworthiness in human-centered sensing, and security and privacy challenges in IoT applications. His publications demonstrate a strong trend toward health-focused applications of ubiquitous computing, particularly in mental health assessment, substance use disorder monitoring, childhood speech disorders, and chronic disease management. His recent work shows increasing emphasis on robustness, fairness, and privacy in human-centered AI systems, with significant contributions to stress detection, emotion privacy protection, and reliable health monitoring solutions. IAAI Deployed Application Award (2021) Graduate Student Award for Outstanding Research (UVA CS Department, 2018) Nominated for Best Paper Award (AsthmaGuide, Wireless Health 2016) CUSE Grant: Innovative & Interdisciplinary Research Grant (Syracuse University, 2021) Dr. Salekin actively advises multiple PhD students in Computer Science and Biomedical Engineering programs, with several successful doctoral graduates. His research has been funded by two National Science Foundation grants and three National Institutes of Health grants. He currently serves as an Associate Editor for the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) and the UbiComp conference. His work spans multiple labs and collaborative teams including the UIS Lab at ASU, Mayo Clinic research teams, and collaborations with medical professionals at SUNY Upstate Medical University.

Adam PickensView profile

Associate Professor

• Occupational biomechanics
• Occupational safety
• Mobile Application Development
• +3 more

Adam Pickens serves as an Instructional Associate Professor in the Department of Environmental and Occupational Health within the Texas A&M University School of Public Health. His research and teaching integrate industrial engineering principles with public health practice to address critical workplace safety challenges through evidence-based interventions. His academic foundation includes: PhD in Industrial Engineering from Texas Tech University MPH in Environmental and Occupational Health from Texas A&M University School of Public Health Bachelor of Science in Biomedical Science from Texas A&M University Dr. Pickens' research program focuses on: Developing mobile health applications for workplace safety monitoring Implementing and evaluating sit-stand workstation interventions Improving occupational safety training methodologies Assessing biomechanical risks in manual materials handling His work demonstrates exceptional translational impact, converting engineering solutions into practical workplace safety protocols. Recent publications reveal a strategic shift toward technology-driven interventions, with increasing emphasis on software-based prompting systems and mobile health applications that address sedentary behavior in office environments. The interdisciplinary nature of his research bridges occupational health, human factors engineering, and digital health innovation, consistently targeting measurable improvements in worker safety and productivity.

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +4 more

Felix Björklund Osmark is an Lecturer at the Department of Computer Science , University of Copenhagen. His work is affiliated with the Machine Learning section and the SCIENCE AI Centre, focusing on interdisciplinary applications of artificial intelligence. Current research interests include quantum computing , natural language processing , and sustainable AI . Recent publications highlight hybrid quantum-classical systems, biomedical applications of machine learning, and ethical considerations in AI development. His collaborative efforts span domains like structural biology , climate science , and medical diagnostics , leveraging the department's compute cluster and TreeSense research center resources.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +3 more

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.

Niharika SinghView profile

Research Fellow

• Artificial Intelligence
• Reinforcement Learning
• Multi-agent Systems
• +3 more

Dr. Niharika Singh is a Postdoctoral Research Scientist at the University of Helsinki specializing in Artificial Intelligence and Reinforcement Learning. She concurrently serves as an Assistant Professor at Bennett University in Greater Noida, India since January 2022. Her research focuses on developing innovative algorithms for complex planning and decision-making problems with applications in smart cities, microgrids, and cloud computing security. Her educational background includes: PhD in Electrical and Electronics Communication (Artificial Intelligence) from Universiti Teknologi PETRONAS, Malaysia (2017-2021) M.Tech in Computer Engineering from University Institute of Engineering and Technology, Kurukshetra University, India (2014-2016) B.Tech in Computer Science and Engineering from Kurukshetra University, India (2010-2014) Dr. Singh's research centers on Artificial Intelligence with emphasis on Reinforcement Learning and multi-agent systems. She develops algorithms for smart microgrid communication networks and intelligent vehicle routing for urban environments. Her work bridges theoretical AI advancements with practical implementations in sustainability domains, particularly through her involvement with the Helsinki Institute of Sustainability Science and Helsinki Institute of Urban and Regional Studies. She has also made significant contributions to cloud computing security, developing protective layers for maintaining Multi-Version Concurrency Control (MVCC) in cloud environments. Her recent publications demonstrate a clear trend toward applying AI to urban sustainability challenges. The 2025 vehicle routing paper shows her focus on intelligent transportation systems, while the 2023 Cloud-HPA work addresses critical privacy concerns in data storage. These publications reflect her interdisciplinary approach combining computer science fundamentals with real-world applications in urban planning and cybersecurity. Dr. Singh has served as a reviewer for the Journal of Supercomputing and Behaviour and Information Technology. She has completed professional certifications from MongoDB and Elsevier, demonstrating commitment to scholarly excellence. As an academic leader, she organized a 5-day SERB Faculty Development Program on Natural Language Processing at Bennett University in 2022 and has delivered expert lectures on AI and machine learning at various institutions. She maintains active collaborations through the SMART research group at Universiti Teknologi PETRONAS, cybersecurity groups at NIT Kurukshetra, and serves as an AI Specialist at The DriveSales™ and Principal Consultant at 99Plays. Her IEEE membership since 2017 reflects her engagement with the broader academic community, participating in webinars on machine learning for biomedical applications and nanotechnology for renewable energy.

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Guy Hembroff serves as an Associate Professor in the College of Computing at Michigan Technological University, where he directs both the MS in Health Informatics program and the interdisciplinary PhD in Computational Science & Engineering program. His academic leadership spans computational health sciences and biomedical data science initiatives. Education: PhD in Computational Science and Engineering, Michigan Technological University MPA in Public Administration, Northern Michigan University BS in Finance and Economics, Michigan Technological University Hembroff's research focuses on human health applications of artificial intelligence, particularly in medical image analysis, natural language processing, and cybersecurity. His work bridges clinical needs with computational solutions through projects like fracture risk prediction from radiographs, lung nodule classification, and mental health intervention modeling. The Biomedical Data Science (BDS) Lab he founded integrates expertise across medicine, data science, and cybersecurity to tackle healthcare challenges. His recent publications demonstrate strong trends in applying deep learning to medical imaging (35%), natural language processing for health literacy (20%), and mental health analytics (15%), with growing emphasis on generative AI applications since 2023. Key methodological approaches include multitask learning, transformer models, and ensemble techniques tailored for healthcare data. Doctoral Advising: Harikrishnan C. (Medical Image Segmentation using AI) Chethana Kadirimangalam (Mental Health Intervention Modeling) Abel Reyes (Medical Image Segmentation and Computer Vision) Tim Van Wagner (Blockchain for Patient Data Auditing) Hembroff leads multiple externally funded research initiatives including the Tick-Talk geographical tick disease monitoring system (MI-SAPPHIRE grant), FHIR-enabled health information exchanges, and mental health intervention optimization models. His BDS Lab maintains active collaborations with Henry Ford Hospital and nonprofit healthcare networks for real-world implementation of health informatics solutions. The Biomedical Data Science Lab operates as a multidisciplinary hub combining clinical expertise with computational innovation. Current projects span physical and mental health domains, with particular focus on disease progression modeling, clinical decision support systems, and secure health data architectures. The lab's work directly informs both graduate curriculum development and healthcare industry partnerships.

John EdwardsView profile

Associate Professor

• Geometric Modeling
• Simulation
• Scientific Visualization
• +4 more

John Edwards is an Associate Professor in the Department of Computer Science at Utah State University. His research focuses on Geometric Modeling, Scientific Visualization, and Learning Science, with a particular emphasis on understanding student cognitive processes during programming tasks. He leads the EdwardsLab, which develops tools like KeystrokeExplorer and CodeKeyz to analyze programming behavior. His work is supported by NSF grants (DUE-2315783 and DUE-2235569) for projects in Learning Science and Physics Simulation. Key contributions include studies on student programming behavior, compiler state analysis, and physics education through interactive simulations. Research highlights include investigating how students approach programming assignments (e.g., average 8-minute focus periods), developing theories on incremental coding practices, and creating visualizations for physics concepts like the Coriolis force. His lab’s software, such as DynamicsLab and MagPhyx, bridges educational tools and scientific inquiry. Edwards also explores AI’s role in education, examining generative models’ impact and policy implications. He has contributed to over 50 publications since 2001, spanning topics from visualization algorithms to educational technology. Edwards collaborates on interdisciplinary projects, including medical device reviews (e.g., spinal fusion cages) and biomechanics simulations. His work emphasizes practical applications of computational methods in both STEM education and real-world problem-solving.

Ahmed SalmanView profile

Research Fellow

• Immunology
• Vaccinology
• Molecular Biology
• +4 more

Dr. Ahmed Salman is a Senior Immunologist-Vaccinologist and Research Group Leader at the Jenner Institute, University of Oxford, affiliated with Kellogg College. He specializes in developing vaccines against infectious and cancerous diseases, focusing on malaria, COVID-19, and veterinary pathogens. His work employs advanced technologies like viral vectors, viral-like particles (VLPs), and CRISPR-based approaches. He holds a DPhil in Clinical Medicine from the University of Oxford, following degrees from Ain Shams University and collaborations with institutions like the Statens Serum Institut. Research Interests: Dr. Salman’s research centers on identifying vaccine antigens, optimizing vaccine platforms, and advancing gene therapy for retinal diseases. His contributions include patents for malaria vaccines and pivotal roles in the Oxford-AstraZeneca COVID-19 vaccine development. He leads projects funded by the Bill & Melinda Gates Foundation and MRC, focusing on antigen selection for clinical trials. Grants & Awards: His work has been supported by major funders, and he holds patents for malaria vaccine candidates. He is a member of the British Society for Immunology and American Society of Tropical Medicine and Hygiene. Publications: His recent work spans malaria vaccine mechanisms, retinal immunology, and cybersecurity applications of AI. Contributions include studies on CRISPR delivery to the retina and AI frameworks for cybersecurity education.

Manuel GilView profile

Researcher

• Bioinformatics
• Evolutionary Genomics
• Computational Biology
• +3 more

Manuel Gil is a Researcher at the Zurich University of Applied Sciences (ZHAW), affiliated with the School of Life Sciences and Facility Management and the Institute of Computational Life Sciences. His work focuses on computational methods in bioinformatics, including ancestral sequence reconstruction, multiple sequence alignment, and semantic integration of biological databases. He leads projects such as computational literature-based drug discovery for natural products and the development of tools like ProPIP and ARPIP. His research interests span evolutionary genomics, bioinformatics software development, and semantic querying of biomedical data. Notable contributions include advancing indel-aware phylogenetic analysis and enabling semantic access to federated bioinformatics databases through projects like Bio-SODA. Publications emphasize methodological advancements in sequence alignment, ancestral reconstruction, and knowledge graph applications in biomedicine. Collaborations include work with institutions like the SIB Swiss Institute of Bioinformatics and involvement in international conferences on text and data mining.

David J. FleetView profile

Professor

• Computer Vision
• Machine Learning
• Image Processing
• +7 more

David J. Fleet is a Professor in the Department of Computer Science and the Department of Computer and Mathematical Sciences at the University of Toronto. He serves as a CIFAR AI Chair and Faculty Member at the Vector Institute, with additional affiliations at the UofT Robotics Institute. PhD in Computer Science (1990), University of Toronto Research focuses on computer vision, deep learning, visual neuroscience, and computational biology Key contributions in motion analysis, stereopsis, 3D reconstruction, and diffusion models Research Interests: His work explores mathematical foundations for visual motion analysis, human pose estimation, and 3D structure determination in electron cryo-microscopy. Recent projects involve deep learning and diffusion models for video and image synthesis. Scientific Recognition: Recipient of the Koenderink Prize, CIFAR AI Chair, and multiple best paper awards at NeurIPS, CVPR, and BMVC. Former Chair of UTSC's Computer and Mathematical Sciences Department (2012–2017). Students and Collaborations: Mentored numerous PhD and MSc students, including Ali Punjani (co-founder of Structura Biotech) and Sara Sabour (Google Brain). Collaborates with institutions like Google DeepMind (2020–present) and PARC (1999–2003).

• Technology Ethics
• AI and Data Ethics
• Neuroethics
• +6 more

Prof. Saskia Nagel is a University Professor and Head of the Applied Ethics Department at RWTH Aachen University's Faculty 7, focusing on interdisciplinary research at the intersection of ethics, technology, and cognitive sciences. She leads the Human-Technology-Center and is affiliated with the Medical Science and Technology (MedST) and Information & Communication Technology (ICT) profiles, as well as the Center for Artificial Intelligence and 4TU Centre for Ethics and Technology. Her expertise spans neuroethics, AI ethics, biomedical ethics, and the ethical implications of human-technology interaction. Education: She holds a BSc and MSc in Cognitive Science (University of Osnabrück), with a focus on neuroscience, and a PhD on neuroethics and brain surveillance/manipulation. As a postdoctoral researcher, she led a group exploring brain plasticity's societal impacts. Previously, she was an Assistant Professor of Philosophy and Technology Ethics at the University of Twente, Netherlands. Research focuses on ethical challenges posed by emerging technologies like neurotechnologies, AI, and data science, particularly their effects on human autonomy, trust, and societal values. Current projects address ethical frameworks for AI in healthcare, explainability standards, and digital sovereignty. She advises the European Commission and international institutions on ethical technology governance. Labs/Teams: Her interdisciplinary group collaborates globally to explore how technology shapes human self-perception and values. Key collaborations include The Neuroethics Collective and international academic partners. Her work emphasizes fostering human flourishing through responsible technological advancement.

Vidul Ayakulangara PanickanView profile

Researcher

• Natural Language Processing
• Healthcare Data Processing
• Machine Learning
• +3 more

Vidul Ayakulangara Panickan is a Researcher at Harvard Medical School’s Department of Biomedical Informatics (DBMI) and a Research Scholar at the Harvard-MIT Center for Regulatory Science. He joined DBMI in October 2019 as a Research Associate. Education: B.Tech in Computer Science and Engineering from Amrita Vishwa Vidyapeetham (Kollam, India), 2015 Master’s in Computer Science with focus on Machine Learning from the University of Massachusetts Amherst Research Interests: Panickan specializes in leveraging Natural Language Processing (NLP) for information extraction from Electronic Medical Records (EMRs) and optimizing algorithms for large-scale healthcare data processing. His work aims to develop automated tools for data aggregation to accelerate healthcare research. Labs & Affiliations: He is affiliated with the Cai Lab and contributes to projects under the Harvard-MIT Center for Regulatory Science.

Corbin JonesView profile

Professor

• Genetic Adaptations
• Molecular Evolution
• Population Genetics
• +8 more

Corbin Jones is a Professor in the Department of Genetics at the University of North Carolina at Chapel Hill (UNC), School of Medicine. He serves as Faculty Director of the UNC High-Throughput Sequencing Facility and is affiliated with the Carolina Center for Genome Sciences (CCGS). His academic roles include dual appointments in Biology and Genetics, and he contributes to graduate programs in Biology, Genetics and Molecular Biology, and Biomedical Engineering (BCB). Dr. Jones' research focuses on evolutionary genetics, particularly using Drosophila species to study adaptive traits. Key projects include analyzing Drosophila sechellia’s genetic resistance and attraction to Morinda citrifolia, investigating mechanisms of new gene formation through chimeric gene studies, and developing statistical tools for QTL mapping accuracy. His lab integrates computational methods, molecular genetics, and genomic data to explore how genetic changes—both regulatory and structural—drive evolutionary adaptations. Collaborations with Dr. Sally Otto and Dr. Philip Awadalla highlight his contributions to theoretical and applied genomics. His work extends to translational research, including studies on human disease genetics (e.g., schizophrenia, lupus) and microbiome assembly dynamics. Recent publications reflect diverse applications of his expertise in virology (SARS-CoV-2 treatment efficacy), cancer biology (evolutionary rescue models), and AI-driven clinical data analysis. Funding for his lab comes from the NSF and NIH, supporting projects that bridge basic and applied sciences. Grants: NSF and NIH-funded research on genomic evolution and disease mechanisms Lab Affiliations: Corbin Jones Lab in the Genome Sciences Building, UNC High-Throughput Sequencing Facility Tools Developed: QTL-based estimator method for genetic mapping, computational pipelines for genomic data analysis Notable research themes include the molecular basis of behavioral adaptations in Drosophila, the role of chimeric genes in evolutionary innovation, and statistical improvements in detecting quantitative trait loci. His studies on D. sechellia’s olfactory-driven behavior reveal insights into gene function beyond mere sensory detection, emphasizing behavioral genetics.