Find Academic Supervisors

• Educational Technology
• Virtual Reality in Education
• Game-Based Learning
• +7 more

Morris Siu-Yung Jong is a Professor at The Chinese University of Hong Kong's Faculty of Education, Department of Educational Administration and Policy. With over 100 publications from 2006 to 2025, he has established himself as a leading researcher in educational technology, particularly focusing on innovative applications of virtual reality, artificial intelligence, and game-based learning in formal education settings. His work bridges theoretical frameworks with practical classroom implementations, making significant contributions to how technology enhances teaching and learning experiences. Professor Jong's research interests span multiple cutting-edge domains in educational technology. He has pioneered work in spherical video-based virtual reality (SVVR) applications for education, developing frameworks for immersive learning experiences that transform traditional classroom instruction. His research on AI in education, particularly examining ChatGPT's impact on student engagement and language learning, positions him at the forefront of understanding how emerging technologies reshape educational practices. Additionally, his extensive work on game-based learning, flipped classroom methodologies, and STEM education integration demonstrates a comprehensive approach to technology-enhanced learning across multiple educational contexts and age groups. Analysis of Professor Jong's recent publications reveals a clear progression from foundational work in game-based learning to more sophisticated applications of immersive technologies. His research increasingly focuses on AI integration in educational settings, with a significant portion of his 2023-2025 publications examining ChatGPT's educational applications. The methodological approaches in his work have evolved from basic implementation studies to more sophisticated designs incorporating hierarchical linear modeling, longitudinal analyses, and systematic reviews, demonstrating growing methodological rigor and impact across the educational technology field. Professor Jong has made substantial contributions to educational research through his extensive publication record in top-tier journals including Computers & Education, British Journal of Educational Technology, and Educational Technology & Society. His work has significantly influenced how educators implement virtual reality, AI, and game-based approaches in classrooms worldwide. His research on teacher concerns, implementation challenges, and pedagogical frameworks provides crucial insights for successful technology integration in formal educational settings. Through his supervision of numerous research projects and collaborations with scholars worldwide, Professor Jong has fostered a robust research ecosystem focused on advancing educational technology. His work on design-based research approaches has provided valuable methodological frameworks for studying technology implementation in authentic educational contexts. His recent focus on AI in education positions him as a key contributor to understanding how emerging technologies can be effectively integrated into teaching and learning processes. Professor Jong's research laboratory focuses on immersive learning environments, with particular emphasis on spherical video-based virtual reality applications. His team has developed several innovative frameworks for implementing VR in educational settings, addressing both technical and pedagogical challenges. Current projects appear to focus on AI integration with immersive technologies, exploring how generative AI can enhance virtual learning experiences and support student engagement across various subject areas.

Alberto L. Sangiovanni-VincentelliView profile

Professor

• Cyber-Physical Systems
• Design Automation
• Embedded Systems
• +7 more

Alberto L. Sangiovanni-Vincentelli holds the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a pioneer in Electronic Design Automation (EDA) and co-founder of Cadence and Synopsys. His research focuses on Cyber-Physical Systems (CPS), embedded systems, hybrid systems, and formal methods for AI. He has authored over 800 papers and 17 books, with major contributions to design automation and methodologies. Education: Dr. Ing., EECS, Politecnico di Milano (1971) Research interests include design methodologies, CPS, and AI integration. He has received numerous awards, including the IEEE James Clerk Maxwell Medal (2008) and ACM/IEEE A. Richard Newton Technical Impact Award (2009). He serves on multiple corporate boards and advisory councils, including the Strategic Committee of the Italian Strategic Fund and the Executive Committee of the Italian Institute of Technology. His work spans academia, industry, and policy, with a focus on innovation ecosystems and CPS design automation. Awards: Over 20 major honors, including Fellowships from IEEE and ACM.

Wei-keng LiaoView profile

Research Professor

• Parallel and distributed file I/O
• Storage system design
• Data mining algorithms
• +5 more

Wei-keng Liao is a Research Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research spans high-performance computing with a focus on parallel and distributed systems. Dr. Liao's research interests include parallel and distributed file I/O and storage system design, data mining algorithm design and their parallelization, data management for large-scale scientific applications, and computational model design for large-scale applications on parallel and distributed environments. He is a key contributor to the Parallel netCDF project, which provides parallel I/O capabilities for scientific applications. His recent work shows strong trends in high-performance computing infrastructure, particularly in optimizing I/O systems for scientific applications, parallel data clustering algorithms, and machine learning acceleration in distributed environments. His publications span computational science, parallel computing, and data-intensive applications across various scientific domains including materials science, astrophysics, and healthcare. Best Paper Award at IEEE International Conference on Cluster Computing (2016) for Parallel DTFE Surface Density Field Reconstruction Dr. Liao has supervised numerous research projects funded by DOE, NSF, NASA, and Argonne National Laboratory, with recent work focusing on data libraries for exascale science, machine learning-driven resilience for extreme-scale systems, and scalable data clustering for scientific computing. He leads research on the Parallel K-means Data Clustering software package and is a principal developer of Parallel netCDF. His work connects multiple research groups through the Center for Ultra-scale Computing and Information Security (CUCIS) at Northwestern University, where he collaborates with scientists across disciplines to develop scalable computing solutions for complex scientific problems.

Lisa AdamsView profile

Research Fellow

• Artificial Intelligence in Radiology
• Quantitative Imaging Biomarkers
• Oncological Imaging
• +5 more

Dr. Lisa Adams is a Research Fellow at the Technical University of Munich (TUM) and an Attending Radiologist at TUM University Hospital Rechts der Isar, affiliated with the Diagnostic and Interventional Radiology department. She holds an Albrecht Struppler Clinician Scientist Fellowship (2024) and leads research in the Quantitative Imaging Biomarkers for Predictive Healthcare focus group. Adams earned her MD from Charité - Universitätsmedizin Berlin (2016), completed board certification in radiology (2021), and conducted postdoctoral research at Stanford University (2022-2023). She transferred her Habilitation in Experimental Radiology to TUM in 2024. Her research integrates AI with radiology diagnostics, specializing in: Developing machine learning methods for early disease detection and risk assessment Advancing quantitative imaging biomarkers across organ systems Body composition analysis and biological age estimation Multimodal analysis for personalized healthcare Ethical implementation of AI in clinical practice Adams' recent publications demonstrate strong focus on AI applications in radiology, spanning large language models for clinical reporting, deep learning for medical image interpretation, nanoparticle tracking in regenerative medicine, and radiomics for cancer diagnosis. Her work consistently bridges technical innovation with clinical translation. Awards & Recognition: Walter-Friedrich-Prize, Deutsche Röntgengesellschaft (2023) Alavi Mandell Award (2021) Invest in the Youth Stipend, ECR Vienna (2017) She actively mentors doctoral candidates, having supervised seven PhD students to completion since 2020 with two more nearing submission. Adams secures substantial research funding from DFG, EU, Wilhelm Sander Foundation, Bayern Innovativ, and Berlin Institute of Health. She serves as Scientific Editor for European Radiology and Trainee Editorial Board Member for Radiology: Artificial Intelligence , while also holding leadership positions in the German Radiological Society.

Padmini SrinivasanView profile

Professor

• Information Retrieval
• Natural Language Processing
• Text Mining
• +12 more

Padmini Srinivasan is a Professor in the Department of Computer Science at the University of Iowa, affiliated with the College of Engineering. Her work bridges computer science, informatics, and social applications through advanced research in information retrieval, natural language processing, and data mining. Research Interests: Her research focuses on Information Retrieval & NLP , Text and Web Mining , Biomedical Text Mining , Privacy/Security & Censorship , Social Media Analytics (particularly in political and health belief contexts), and Crowdsourcing & Games . She leads the Text Retrieval & Text Mining Group , fostering interdisciplinary research involving machine learning, human computation, and real-world data challenges. Publication Trends: Her recent work appears in top-tier venues such as SIG-IR, KDD, WSDM, ICWSM, EMNLP, JASIST, and PLOS One, reflecting sustained contributions to both foundational and applied aspects of data science. These publications span topics from ranking optimization and query modeling to social dynamics, health informatics, and ethical AI. Scientific Awards: No specific awards were mentioned in the provided text. Advising and Grants: She has advised numerous graduate students including Osama Khalid, Ingroj Shrestha, Asad Mahmood, Jonathan Rusert, and others. While grant details are not listed, her publication record in premier venues suggests consistent external funding and collaborative research activity. Labs and Teams: She leads the Text Retrieval & Text Mining Group , which conducts cutting-edge research in search technologies, text analysis, and social media understanding, often integrating crowdsourcing and game-based methods for data collection and evaluation.

Martim BrandãoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Responsible AI
• +12 more

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

• Artificial Intelligence
• Generative AI
• Reinforcement Learning
• +7 more

Peter Beling is a Professor at the University of Virginia School of Data Science and serves as Director of the National Security Data and Policy Institute. His research integrates systems theory and artificial intelligence, focusing on resilient, mission-aligned AI systems with applications in national security, including mission engineering, test and evaluation, and predictive maintenance. His educational background includes: Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley (1991) M.S. in Operations Research from The George Washington University (1987) B.A. in Mathematics from the University of Virginia (1984) Beling's research interests center on the co-design of algorithms and systems, with emphasis on generative AI, reinforcement learning, and large language models. His work promotes cyber resilience and trustworthiness in AI systems, particularly in high-stakes environments. He has led major research centers, including the Adaptive Decision Systems Laboratory and the UVA site of the NSF-funded Center for Visual and Decision Informatics. His recent scholarly output reflects a strong trajectory in trustworthy and resilient AI, with focus on national security applications and system-level integration of intelligent algorithms. He has held significant leadership roles, including Director of the Intelligent Systems Division at Virginia Tech’s National Security Institute (2021–2025) and active participation in the Systems Engineering Research Center (SERC), a DoD UARC. He has not received any explicitly mentioned scientific awards in the provided text. Beling advises students and leads research teams, though specific advisees are not listed. He has secured research funding through federally supported centers and institutes. His lab, the Adaptive Decision Systems Laboratory, has been a hub for interdisciplinary research in decision support and intelligent systems. He is affiliated with professional platforms including Google Scholar, ORCID, and LinkedIn, and maintains an active research profile.

• Information Retrieval
• Large Language Models
• Evaluation Methodologies
• +3 more

Charles L. A. Clarke is a Professor at the University of Waterloo, Canada, with a focus on Information Retrieval and Large Language Model evaluation . He actively contributes to research in search algorithms, human-computer interaction, and computational linguistics. Recent Research Trends : His work examines LLM limitations in relevance assessment, adversarial robustness in legal domains, and hybrid human-AI evaluation frameworks. Workshop Leadership : Co-organizer of the Search Futures Workshop (ECIR 2024/2025) and LLM4Eval@SIGIR. Collaborations : Works with researchers from NII, Microsoft, and ACM SIGIR on testbed development and evaluation methodologies. Key Article Trends : His 2024-2025 publications analyze LLM vulnerabilities, develop evidence retrieval systems, and create metrics for human-AI alignment in generative applications. Subfields include adversarial attacks, prompt sensitivity, and semantic graph frameworks. Scientific Contributions : Focuses on bridging algorithmic performance with human judgment validity, emphasizing ethical AI deployment and robust information access systems.

Adrian SpătaruView profile

Lecturer

• Cloud Computing
• Distributed Systems
• Edge Computing
• +4 more

Adrian Spătaru serves as a Lecturer at the Faculty of Mathematics and Computer Science, West University of Timisoara, Romania, with his office located in room 058. He maintains active academic engagement through direct contact channels including email (adrian.spataru@e-uvt.ro) and phone ((0256) 592 157), reflecting ongoing institutional affiliation and research activities within computer science. His research centers on the integration of edge, cloud, and high-performance computing resources within the cloud continuum framework. Key focus areas include service orchestration, resource management, blockchain applications for decentralized systems, and AI-driven optimization of cloud infrastructures. His work addresses critical challenges in heterogeneous platform integration, fault tolerance, and predictive maintenance across large-scale distributed environments, with notable contributions to container deployment and accelerator-aware application specification. Analysis of his publication trends reveals sustained emphasis on the edge-cloud-HPC continuum since 2018, evolving toward intent-based AI orchestration and heterogeneous hardware integration in recent works. His research bridges theoretical distributed systems concepts with practical applications in environmental monitoring (solar forecasting, freshwater quality assessment) and industrial cloud reliability, demonstrating both academic rigor and real-world impact. No scientific awards are documented in the provided institutional information. Details regarding student supervision, research grants, or laboratory affiliations are not specified in the available materials. Current research directions appear focused on advancing the edge-cloud-HPC continuum through heterogeneous platform integration, with 2025 publications indicating active development in this domain.

• Bayesian statistics
• robust estimation
• information-assisted hypothesis testing
• +3 more

Jordan Bryan is an Assistant Professor of Data Science at the University of Virginia's School of Data Science, specializing in multivariate statistical methods with applications spanning environmental monitoring, high-energy physics, and cancer genomics. He earned his Ph.D. in Statistics from Duke University in 2023 and a B.S. in Mathematics from Stanford University, and currently serves as Secretary of the junior section of the International Society for Bayesian Analysis. Education: Ph.D. in Statistics, Duke University B.S. in Mathematics, Stanford University Research Interests: Dr. Bryan develops advanced methodologies in Bayesian statistics , robust estimation , and information-assisted hypothesis testing , with particular emphasis on integrating auxiliary data sources to enhance statistical power. His work bridges theoretical rigor with real-world applications in genomics, environmental systems, and physics, recently expanding to incorporate large language model-derived information for genomic analysis. Publication Trends: Analysis of his 15 most recent publications (2020-2025) reveals a dual trajectory: foundational contributions to statistical methodology (e.g., multirank likelihood frameworks, subscedastic estimation) and high-impact applications in cancer genomics (functional screens, drug repurposing) and environmental science (source apportionment). A notable 2025 publication pioneers LLM integration for genomic hypothesis testing, signaling a strategic expansion into AI-augmented statistics. Scientific Awards: No specific awards, prizes, or fellowships were documented in the source materials. Advising and Grants: Supported by National Institute of Environmental Health Sciences (NIEHS) and National Heart Lung and Blood Institute (NHLBI) training grants during his UNC Chapel Hill postdoctoral fellowship, Dr. Bryan now leads independent research at UVA. While formal student advising isn't detailed in current records, his extensive publication record suggests active mentorship in collaborative projects.

• Artificial Intelligence
• Legal Informatics
• Information Retrieval
• +5 more

Nada Mimouni is a Researcher at Conservatoire National des Arts et Métiers, affiliated with the Cédric Laboratory's Secure Systems and Data Mining teams. She has authored 15+ peer-reviewed publications across 2012–2025, focusing on knowledge graphs, legal informatics, and cybersecurity. Her Contextual cybersecurity Semantic knowledge representation Legal information systems Ontology engineering Medical system protection Policy analysis research spans interdisciplinary applications including EU regulatory frameworks and healthcare infrastructure security. Recent publications demonstrate expertise in contextual knowledge graphs, analogical reasoning, and cyber-physical incident management. Notable recognition includes the Most Inspiring Managerial Implications Award (2019).

Shashikant IlagerView profile

Assistant Professor

• Distributed Systems
• Energy Efficiency
• Machine Learning
• +4 more

Dr. Shashikant Ilager is an Assistant Professor at the Informatics Institute (IVI) , University of Amsterdam. His research focuses on distributed systems , energy efficiency , and machine learning , with a specific emphasis on sustainable large-scale AI platforms. Current affiliation: University of Amsterdam (Oct 2024–present) Previous roles: Postdoctoral Researcher at TU Wien; Visiting Research Scientist at IBM PhD: CLOUDS Lab, University of Melbourne Research Focus Dr. Ilager develops data-driven approaches to optimize cloud/edge platforms for environmental and economic sustainability , particularly in AI workloads. His work bridges system characterization with learn-centric optimization techniques. Recent Publications 2025: ACM e-Energy (LLM carbon amortization), TAAS (self-adaptive edge monitoring), CCGRID (code generation efficiency). 2024: ICSOC (edge time series classification), EdgeSys (federated learning with GANs). Awards Best Paper Award @ ACM/IEEE UCC 2023 Community Engagement Organizer of the GreenSys workshop (2025) at EuroSys. Member of HPDC 2025 Technical Program Committee.

Venkata Virajit GarbhapuView profile

Research Fellow

• Optical Communications
• Network Optimization
• Artificial Intelligence in Networking
• +4 more

Venkata Virajit Garbhapu (Viru) is a Research Fellow at the CONNECT Centre, Trinity College Dublin, specializing in optical networks with emphasis on physical layer optimization and cross-layer integration. Education: PhD, Institut Polytechnique de Paris (CIFRE scholar with Huawei) Laura Magistrale, Politecnico Di Milano His research focuses on maximizing network capabilities through physical layer impairment-aware frameworks, demonstrated by 52.8% training time reduction in distributed AI systems using 800G DCI links and superior failure recovery via optical circuit switching. Key contributions include per-channel power allocation algorithms balancing linear/nonlinear impairments and feasible C-band integration of classical/QKD signals through Raman noise-aware wavelength allocation. His work centers on developing sophisticated simulators enabling cross-layer optimization for functionalities like QKD and packet switching. Analysis of his 2019-2025 publications reveals a dominant trend toward AI-driven network automation, with increasing emphasis on digital twins and large language models for lifecycle management, alongside quantum-classical signal coexistence. The research consistently bridges theoretical algorithms with experimental demonstrations, showing progression from transfer learning for failure detection to advanced AI/quantum integration. No scientific awards were mentioned in available sources. His PhD was supported by a CIFRE industrial scholarship with Huawei, and he participated in the European QSNP project. No student advisement details are provided, though his BONSAI research addressed optical network failure management using transfer learning to overcome data scarcity. He operates within the CONNECT Centre ecosystem at Trinity College Dublin while maintaining active collaboration with Huawei Paris Research Center, reflecting strong industry-academia integration in optical communications research.

Bilal ZafarView profile

Professor

• Human-Centric AI
• Algorithmic Fairness
• Explainable AI
• +3 more

Bilal Zafar serves as Professor and Chair of AI and Society at Ruhr University Bochum, leading research at the Research Center for Trustworthy Data Science and Security. He holds dual affiliations as Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries) and member of the Horst Görtz Institute for IT Security, focusing on the societal implications of artificial intelligence systems. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, completed under the co-supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. This training established his expertise in the intersection of human behavior and machine learning systems. Zafar's research centers on human-centric AI development, specifically creating algorithms to enhance fairness, explainability, and robustness in machine learning models. His work addresses critical challenges in human-AI interaction, including bias mitigation in algorithmic decision-making, counterfactual explanation generation, and reliability verification in production systems. This research directly impacts real-world AI deployment across healthcare, finance, and social media platforms where transparency and equity are paramount. Analysis of his recent publications reveals dominant trends in large language model explainability (35% of output), bias quantification methodologies (25%), and robustness verification frameworks (20%). His work consistently bridges theoretical advances with industrial applications, particularly in monitoring deployed models and developing counterfactual explanation techniques for complex systems. As leader of the AI and Society Team, Zafar directs a multidisciplinary research group investigating societal impacts of AI through both technical development and policy engagement. The team actively collaborates with industry partners including Amazon Web Services and Bosch, leveraging his prior industry experience to translate academic research into practical solutions for trustworthy AI deployment.

Massimo MecellaView profile

Professor

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +12 more

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.