Find Academic Supervisors

• Semantic Web
• Knowledge Representation
• Artificial Intelligence
• +6 more

Deborah L. McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer, Cognitive, and Web Sciences at Rensselaer Polytechnic Institute (RPI). She serves as founding director of the Web Science Research Center, focusing on next-generation ontology-enabled research infrastructure for interdisciplinary applications. Leaders in Semantic Web and AI research Bridging AI with eScience and healthcare Developer of semantic frameworks for data-intensive resource efforts Her research spans artificial intelligence, knowledge representation, clinical decision support systems, and ontology engineering. She investigates how semantic technologies can enhance AI explainability, data interoperability, and interdisciplinary science. Her recent publications highlight trends in AI explainability frameworks Knowledge graph construction Clinical decision support Commonsense reasoning benchmarks Health informatics Data harmonization Scientific awards include Fellow of the American Association for the Advancement of Science (AAAS) Robert Engelmore Award from the Association for the Advancement of Artificial Intelligence (AAAI) She leads large data-intensive research efforts and has contributed to projects involving blockchain integration with semantic web standards, FAIR data principles, and incentivized research data sharing mechanisms.

• Cyber-physical systems
• Green and sustainable computing
• Mobile and pervasive computing
• +7 more

Sandeep Gupta is a Professor and Director of the School of Computing and Augmented Intelligence at Arizona State University's Ira A. Fulton Schools of Engineering. He also serves as a Senior Global Futures Scientist. His work bridges computer science, engineering, and healthcare applications with a focus on creating reliable cyber-physical systems that interact safely with humans. Education: Ph.D. from The Ohio State University (1995) Research Interests: Professor Gupta's research spans cyber-physical systems, green and sustainable computing, mobile and pervasive computing, and parallel and distributed computing. His work increasingly focuses on human-in-the-loop systems where AI and humans collaborate safely, particularly in healthcare contexts. Recent research integrates large language models with cyber-physical systems to enhance safety and operational effectiveness in critical applications like medical monitoring and industrial automation. Research Trends: Analysis of Professor Gupta's recent publications reveals a strong focus on operational safety in human-AI collaborative systems, particularly in healthcare applications. His work combines physics-guided models with machine learning to detect "unknown-unknowns" in safety-critical systems, develops LLM-based approaches for medical image classification, and creates frameworks for ethical human-AI collaboration. The research increasingly addresses real-world challenges in diabetes management, epilepsy diagnosis, and industrial automation. Professional Service: IEEE Communication Letters Editorial Board, Area Editor (2008-Present) IEEE Journal on Special Areas in Communications - Issue on Body Area Network, Co-editor (2006-Present) Elsevier COMNET - Computer Network Journal, Reviewer (2008-Present) Technical Advisory Committee Chair for Networks (2005-Present) Advising and Grants: Professor Gupta has secured numerous research grants from NSF, NIH, Intel, Raytheon, and other organizations, totaling millions of dollars. His research portfolio includes projects on smart stadiums, mobile ECG sensing, power-aware scheduling, medical device verification, and sustainable data center management. He actively advises PhD and Master's students through thesis courses and research supervision, focusing on cyber-physical systems and healthcare applications. Labs and Research Groups: Professor Gupta leads research in cyber-physical systems with applications in healthcare, sustainable computing, and mobile networks. His work involves interdisciplinary collaboration across engineering, computer science, and medical domains, particularly through ASU's Global Futures initiatives.

Mohit KumarView profile

Professor

• Trustworthy Artificial Intelligence
• Explainable AI
• Privacy-Preserving AI
• +10 more

Mohit Kumar is an außerplanmäßiger Professor of Computational Intelligence in Automation at the Institute of Automation Technology, University of Rostock. He concurrently serves as a Key Researcher in Data Science at the Software Competence Center Hagenberg, Austria, and as a Visiting Professor at the Georg-August-Universität Göttingen. His research centers on Trustworthy Artificial Intelligence frameworks, specifically developing Explainable AI, Privacy-Preserving AI, and Transferrable AI methodologies. He pioneers fuzzy logic applications in machine intelligence and creates AI-driven analytical systems for complex data, signals, and image processing. This work is rigorously grounded in probability theory, statistical modeling, estimation theory, and robust adaptive filtering techniques. At the Software Competence Center Hagenberg, he leads digitalization solution development through theoretically sound approaches and extensive real-world experimentation to solve critical industrial and societal challenges.

Rune SundsetView profile

Associate Professor

• Nuclear Medicine
• Radiation Biology
• Radiopharmaceutical Development
• +3 more

Rune Sundset is an Associate Professor at UiT The Arctic University of Norway, affiliated with the Nuclear Medicine and Radiation Biology research group. His work focuses on radiopharmaceutical development, molecular imaging, and liposomal drug delivery systems. Research themes: Radioligand design, PET/SPECT imaging optimization, and nanoparticle-biology interactions Key collaborations: European Journal of Nuclear Medicine and Molecular Imaging, BMC Research Notes, and Acta Physiologica Recent publications highlight his contributions to: Development of Cu-67 and Ga-68 radiotracers for tumor imaging Liposomal formulations for arginase inhibition in cancer therapy Machine learning applications in dynamic PET imaging Sphingolipid effects on liposome uptake by human phagocytes In the SECURE project, he investigates radioligand therapy and imaging biomarkers. His research group (NMRB) spans nuclear medicine, radiation biology, and biomedical engineering, with applications in glioblastoma, prostate cancer, and neurodegenerative diseases.

• Artificial Intelligence
• Machine Learning
• Computer Science
• +6 more

Fredrik Heintz is a professor at Linköping University's Department of Computer and Information Science within the Faculty of Science & Engineering. His research bridges artificial intelligence, education, and healthcare, focusing on AI literacy, synthetic data generation, and autonomous systems. Key affiliations: Linköping University (Faculty of Science & Engineering, Department of Computer and Information Science) Research Interests: Heintz's work spans multiple domains: Developing frameworks for AI literacy in K-12 education Creating fair synthetic healthcare data using GANs and bias-transforming techniques Advancing autonomous 3D exploration algorithms for dynamic environments Benchmarking tools for fairness, utility, and explainability in AI models Stream reasoning for real-time data analytics and knowledge extraction Evaluating ethical implications of AI in teacher education Scientific Contributions: His publications highlight collaborations with international researchers and significant grants from the Swedish Research Council, Knut and Alice Wallenberg Foundation, and VINNOVA. Notable projects include TransFusion for time-series generation, Bt-GAN for fair healthcare data, and DAEP for dynamic exploration planning. Funded by Wallenberg AI, Autonomous Systems and Software Program (WASP) ELLIIT Excellence Center at Linköping-Lund Mistra Geopolitics research program

Ellen RathjeView profile

Professor

• Geotechnical Engineering
• Earthquake Engineering
• Seismic Response Analysis
• +5 more

Ellen Rathje is a Professor and Janet S. Cockrell Centennial Chair in Engineering at the University of Texas at Austin's Department of Civil, Architectural, and Environmental Engineering within the Cockrell School of Engineering. Her expertise spans geotechnical engineering with a focus on earthquake engineering, seismic response of earth structures, and liquefaction evaluation. She leads research initiatives such as DesignSafe cyberinfrastructure and TexNet Seismological Network, advancing geohazard risk assessment and computational modeling. Education: Ph.D. (1997), M.S. (1994), and B.S. (1993) in Civil Engineering from University of California, Berkeley and Cornell University. Research Interests: Dr. Rathje investigates earthquake-induced ground failures, including lateral spreading, liquefaction, and slope instabilities. Her work integrates machine learning, finite element analysis, and geospatial data to enhance predictive models for infrastructure resilience. Recent projects address induced seismicity in energy-producing regions, site amplification in Central and Eastern North America, and tailings dam failure mechanisms. Awards: Recipient of the 2022 Ralph B. Peck Award, 2018 William B. Joyner Lecture Award, and 2016 ASCE Fellow distinction. Her work has advanced open science through DesignSafe's cyberinfrastructure, supporting natural hazards research collaboration. Technical Contributions: Developed hybrid finite-element/material point methods for granular collapse modeling, probabilistic frameworks for regional landslide assessments, and neural network-based site amplification models. Active in post-earthquake reconnaissance through GEER (Geotechnical Extreme Events Reconnaissance) and TexNet operations.

Mustafa AbdallahView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence
• Game Theory
• +7 more

Mustafa Abdallah is an Assistant Professor at the Computer and Information Technology (CIT) department of Purdue University in Indianapolis, with a courtesy appointment at the Purdue Polytechnic Institute. He holds a PhD in Electrical and Computer Engineering from Purdue University (2022) and prior degrees from Cairo University (MS: 2016, BS: 2012). His research focuses on game theory, behavioral decision-making, explainable AI, and deep learning, applied to cybersecurity, autonomous systems, and IoT anomaly detection. His work has been recognized by the prestigious Bilsland Fellowship and grants from IEEE and IUPUI. Industrial collaborations include Adobe Research (meta-learning for time-series forecasting), Principal Financial Group (financial risk prediction using Kalman filters), and RDI Company (deep learning for pronunciation systems, resulting in a US patent). He has published extensively in top venues like IEEE S&P, IEEE TCNS, and ACM AsiaCCS.

• Scientific Machine Learning
• Climate Science
• Earthquake Seismology
• +3 more

Karianne Bergen is an Assistant Professor of Data Science and Earth, Environmental & Planetary Sciences at Brown University, with a courtesy appointment in Computer Science. She leads the Scientific Machine Learning (SciML) Research Group, affiliated with the Data Science Institute (DSI), DEEPS, and the SciAI Center. Her research focuses on scientific machine learning (SciML), including surrogate models for climate science, explainable AI (XAI), and foundation models. She holds a Ph.D. and M.Sc. in Computational and Mathematical Engineering from Stanford University and a B.Sc. in Applied Mathematics from Brown University. Her postdoctoral training included a Harvard University HDSI fellowship in Computer Science. Education: B.Sc. Applied Mathematics, Brown University (2009) M.Sc. Computational and Mathematical Engineering, Stanford University (2015) Ph.D. Computational and Mathematical Engineering, Stanford University (2018) Research Interests: Dr. Bergen’s work bridges machine learning and Earth sciences, emphasizing scalable methods for climate modeling and geophysical data analysis. Her group develops emulators for Antarctic ice sheet dynamics, XAI frameworks for climate data interpretation, and scientific foundation models for geoscience applications. Recent projects include GAN-based sea ice resolution enhancement and flow-based neural networks for sea level projections. Awards: Harvard Data Science Initiative Postdoctoral Fellowship (2018–2020) Stanford Graduate Fellowship in Science and Engineering (2011–2015) Outstanding Student Paper Award, American Geophysical Union (2015) Advising & Collaborations: She mentors PhD students in Earth, Environmental, and Planetary Sciences and collaborates with institutions like MIT-Lincoln Laboratory, SciAI Center, and international geoscience groups. Her lab includes postdocs (e.g., Hilarie Sit) and alumni from data science practicum programs. Labs/Teams: The SciML group at Brown University focuses on multidisciplinary projects at the intersection of AI and Earth sciences, with recent presentations at AGU Fall Meetings and the AI for Science Workshop at ICML.

• Cybersecurity
• Machine Learning Applications in Security
• Data Provenance Analysis
• +7 more

Kangkook Jee is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His work focuses on cybersecurity, machine learning applications in security, data provenance analysis, and graph neural networks. He has developed systems like ProvIoT for IoT security and UTrack for enterprise user tracking. His research addresses challenges in adversarial machine learning, malware detection, and robust graph classification under adversarial conditions. He also explores federated learning, confidential computing, and blockchain-based secure data sharing. Key contributions include techniques for detecting stealthy attacks in IoT, improving graph neural network robustness against adversarial node modifications, and enhancing intrusion detection through provenance-based analysis. His work bridges theoretical advancements in machine learning with practical enterprise security solutions. Notable systems include AIQL for efficient attack investigation and SEAL for storage-efficient causality analysis in enterprise logs. Research trends across his publications emphasize combining provenance tracking with modern ML techniques to address evolving cybersecurity threats. Work in 2024-2025 focuses on decompilation challenges, federated edge-cloud security, and graph abstraction methods for robust classification. No scientific awards are explicitly listed in the provided texts. His grants and advising activities are not detailed here, though his extensive publication record suggests active collaborative research. His lab works on tools like Nodoze for automated threat triage and APTrace for agile causality analysis in enterprise systems.

Francesco GrecoView profile

Research Fellow

• Human-Computer Interaction
• Usable Security
• End-User Development
• +4 more

Francesco Greco is a Research Fellow and Ph.D. student at the University of Bari Aldo Moro's Computer Science Department, actively contributing to the Interaction, Visualization, Usability & UX (IVU) Laboratory under Prof. Maria Francesca Costabile. He completed a visiting research position at King's College London's Cybersecurity (CYS) group from October 2023 to March 2024 under Prof. Luca Viganò's supervision. His academic qualifications include: Master's degree in Computer Science (2022, University of Bari, full marks with honors) Bachelor's degree in Computer Science and Digital Communication (2020, University of Bari - Taranto, full marks with honors) Greco's research centers on Human-Computer Interaction and Usable Security , with specialized expertise in End-User Development , Internet of Things security , Computer Vision , and eXplainable AI for cybersecurity . His work develops human-centered security tools that translate technical findings into actionable user protections, particularly through phishing detection systems that generate intuitive explanations. Analysis of his 15 recent publications (2023-2025) reveals a cohesive research trajectory focused on XAI-driven security interventions . Key themes include timing optimization for phishing warnings, human factors in cybersecurity incidents, and LLM-based educational tools. His work consistently bridges theoretical HCI principles with practical security applications, as demonstrated by tools like APOLLO for phishing email analysis. Greco actively collaborates within the IVU Lab ecosystem and maintains international partnerships, including his recent work at King's College London. His research output demonstrates significant contributions to usable security frameworks and cybersecurity education methodologies.

Cheryl SealsView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Educational Technology
• +4 more

Cheryl Seals is the Charles W. Barkley Endowed Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. She holds a Ph.D. in Computer Science from Virginia Tech, with additional degrees from Virginia Tech, North Carolina A&T State University, and Grambling State University. Her research focuses on AI ethics, natural language processing (NLP), virtual reality (VR) applications in healthcare and education, and promoting diversity in STEM. She leads initiatives like the NSF-funded AI ethics education project and the Institute for African Americans in Computing Sciences (IAAMCS). Notable awards include the Charles W. Barkley Professorship (2020) and participation in Drexel’s ELATES fellowship program. Her work bridges technology and social impact, including VR-based empathy training for medical students and interventions to mitigate pandemic-induced learning loss. She collaborates on interdisciplinary projects, such as automated grading tools for speech therapy and pavement crack recognition systems. Education: Ph.D. Computer Science, Virginia Tech M.S. Computer Science, Virginia Tech M.S. Software Engineering, North Carolina A&T State University B.S. Computer Science, Grambling State University Research Interests: AI Ethics and STEM Education Reform NLP for Sentiment Analysis and Hate Speech Detection VR Applications in Healthcare Training and Empathy Development Computing Equity and Underrepresented Groups Interdisciplinary Tools for Education and Accessibility Awards and Recognition: Charles W. Barkley Endowed Professor (2020–2025) ELATES Fellowship Participant (2024) Grants and Collaborations: NSF Grant: Integrating AI Ethics into STEM Curricula Collaborative Research: Evolution of IAAMCS National Science Foundation (NSF) AI Ethics Initiative Labs and Affiliations: Center for Artificial Intelligence and Cybersecurity Engineering McCrary Institute for Cyber and Critical Infrastructure Security

• Biomedical Informatics
• Healthcare Informatics
• Machine Learning
• +4 more

Syed Ahmad Chan Bukhari is an Associate Professor in the Department of Computer Science, Mathematics, and Science at The Lesley H. and William L. Collins College of Professional Studies, St. John's University, New York. He also serves as the Director of Research and Director of Healthcare Informatics at CCPS. Bukhari holds a Ph.D. in Computer Science from the University of New Brunswick and a postdoctoral fellowship from Yale University, with additional work at Stanford University's CEDAR Metadata Center. His research focuses on biomedical informatics, trustworthy AI, machine learning, and healthcare data standards. He teaches graduate and undergraduate courses in healthcare informatics and computer science. Bukhari leads an NSF-funded lab (bukharilab.org) and has developed protocols for biomedical data standardization with NCBI, including MiAIRR and CEDAR-to-NCBI pipelines. Notable awards include ACM Distinguished Speaker, Senior Member IEEE, and an NIH-NCBI Fellowship (2016). His work emphasizes reproducibility in biomedical data and FAIR principles. Recent projects include federated knowledge graphs, AI-driven tutoring systems, and sustainable AI in finance.

John GuttagView profile

Professor

• AI for Healthcare and Life Sciences
• Biological and Medical Devices and Systems
• Graphics and Vision
• +3 more

John Guttag is the Dugald C. Jackson Professor in Electrical Engineering and Computer Science at MIT. His work focuses on AI-driven healthcare solutions, biomedical systems, and advanced computer vision applications. He leads research in medical image analysis, machine learning reliability, and healthcare equity. Guttag's contributions include innovative frameworks like MultiMorph and Scale-Space Hypernetworks, addressing challenges in medical imaging and clinical decision-making. Affiliations: MIT Electrical Engineering & Computer Science Department (EECS) Research emphasizes AI for healthcare, particularly in segmentation, predictive analytics, and ethical algorithm design. Notable projects include real-time fraud detection systems and studies on racial disparities in clinical risk scores. His work bridges computer science with clinical practice through tools like Voxelmorph for medical image registration and ScribblePrompt for interactive biomedical segmentation. Recent publications highlight advancements in uncertainty-aware AI, contrastive learning, and scalable medical data processing. Guttag’s methodologies prioritize practical clinical applications, aiming to improve diagnostics and healthcare workflows. His lab develops open-source tools and frameworks that enhance accessibility to advanced medical imaging technologies.

Anita Williams WoolleyView profile

Professor

• Collective Intelligence
• Human-AI Collaboration
• Organizational Behavior
• +5 more

Anita Williams Woolley is a Professor of Organizational Behavior and Theory at Carnegie Mellon University’s Tepper School of Business. She holds a PhD in Organizational Behavior from Harvard Business School, alongside a BA and MA in Psychology and Social Psychology from Harvard University. Her research focuses on collective intelligence, particularly in human-AI collaboration, team coordination, and the cognitive foundations of group performance. She is a Senior Editor at Organization Science and a founding Associate Editor of Collective Intelligence . Woolley’s work has been funded by DARPA, NSF, and the U.S. Army Research Office, including a $3M DARPA grant to advance artificial social intelligence. Her research has been published in top journals like Science , PNAS , and Nature Human Behavior . She explores how transactive memory systems, collective attention, and team composition enhance group performance, with recent studies on vocal and facial synchrony in teams and the impact of gender diversity on intelligence. Educations: PhD in Organizational Behavior, Harvard Business School MA in Social Psychology, Harvard University BA in Psychology, Harvard University Her research has practical implications for distributed teams and AI integration, including studies on hybrid intelligence and the design of collaborative tools. She co-leads the CI@CMU Lab and has advised organizations on optimizing team dynamics. Personal interests include fostering gender equality in science, as highlighted in her work on team diversity and the establishment of the Joe and Connie Williams Scholarship in her hometown. Woolley has held roles such as Associate Dean for Research at Tepper School (2022–2024) and has taught courses on managing teams and global collaboration. Her contributions extend to policy and public discourse on AI ethics and human-AI collaboration.

Kieron O'HaraView profile

Associate Professor

• Privacy
• Trust
• Digital Modernity
• +4 more

Kieron O'Hara is an Associate Professor in Electronics and Computer Science at the University of Southampton. He is a founding contributor to Web Science, focusing on digital modernity's societal, economic, and political impacts. His work explores privacy preservation in AI/ML contexts, trust dynamics in networked societies, and internet governance. He co-authored seminal works like A Framework for Web Science (2006) and Four Internets (2021), and chairs UKAN's anonymisation network. He has held roles at the Ministry of Justice's transparency panel and serves as editor of Foundations and Trends in Web Science . Research Groups: Web and Internet Science, Centre for Democratic Futures External Roles: Speaker for 'Four Internets: The Ethics and Geopolitics of the Internet' (2019) His research interests include anonymisation techniques, data geopolitics, and the ethical implications of emerging technologies. Notable awards include Best Paper prizes at ACM Web Science (2016) and Best Oral Presentation at Sensecam (2012). Current projects focus on provenance/anonymization frameworks and national identity system analysis, funded by the Alan Turing Institute.