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Nenad RadakovicView profile

Associate Professor

• STEM Education
• Transdisciplinary Learning
• Mathematics Education
• +5 more

Nenad Radakovic is an Associate Professor of STEM Education at Queen's University's Faculty of Education. His research focuses on transdisciplinary approaches in mathematics and STEM education, emphasizing how interdisciplinary integration can enhance learning across PK-12 and post-secondary contexts. He holds a PhD in Curriculum Studies and Teacher Development from the Ontario Institute for Studies in Education (OISE), University of Toronto, and has taught secondary mathematics in Croatia and Canada. Prior roles include Associate Professor at the College of Charleston and Sessional Lecturer at the University of Toronto, where he instructed courses on holistic mathematics pedagogy and curriculum design. Dr. Radakovic’s research interests span transdisciplinary curricula, risk education, mathematics teacher training, and the role of technology in education. He is a STaR Fellow with the Association of Mathematics Teacher Educators and contributes to professional organizations like NCTM and PMENA. His work explores how mathematics education can address societal challenges through critical pedagogy and inclusive practices. Key publications include edited volumes on transdisciplinarity in mathematics education and borders in teacher training. His recent articles address STEM curriculum innovation, inclusive pedagogical frameworks, and the application of feminist theory to educational research. Collaborations include transdisciplinary projects blending arts and mathematics, such as using 3D printing to explore cultural heritage through art.

Maren ElfertView profile

Senior Lecturer

• International and comparative education
• Global governance of education
• Influence of international organizations on educational policies
• +3 more

Dr. Maren Elfert is a Senior Lecturer in International Education at King’s College London, part of the School of Education, Communication & Society. She is affiliated with the Centre for Public Policy Research (CPPR). Previously, she worked at the UNESCO Institute for Lifelong Learning (2000s), earned her PhD in 2016 from the University of British Columbia, and completed a postdoctoral fellowship at the University of Alberta (2017-2018). Education: PhD in Education, University of British Columbia (2016) Professional History: UNESCO staff member (over a decade), Postdoctoral Fellow (University of Alberta), King’s College London faculty since 2019. Her research focuses on global governance of education, particularly the influence of international organizations like UNESCO, OECD, and the World Bank. She explores historical and contemporary dynamics shaping educational concepts and policies, including lifelong learning and the Sustainable Development Goals (SDGs). Her work emphasizes the interplay between international agendas and localized educational practices. Key research outputs include the book Global Governance of Education (2023) and articles examining SDG4’s role in global education policy. Her grants include SSHRC and Spencer Foundation fellowships, along with a British Academy/Leverhulme grant. Awards: SSHRC Postdoctoral Fellowship National Academy of Education/Spencer Foundation Fellowship British Academy/Leverhulme Small Grant Dr. Elfert advises PhD students on international education, global governance, and the historical role of international organizations. She leads the Diffusion of Global Education Policies project funded by the British Academy (2023–2025). Her affiliations include the King’s International Education Research Network, focusing on global education challenges.

• Computer Network Security
• Mobile Computing
• Cloud Computing
• +4 more

Dr. Dijiang Huang is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He joined ASU in 2005 after completing his Ph.D. in Telecommunications and Computer Networking from the University of Missouri-Kansas City (2004). His research focuses on cybersecurity, mobile computing, and cloud computing, supported by grants from the National Science Foundation (NSF), Office of Naval Research (ONR), and industry partners like HP. He has received prestigious awards, including the ONR Young Investigator Award and HP Innovative Research Award. Education: B.E. in Telecommunications, Beijing University of Posts and Telecommunications (1995) M.S. in Computer Science, University of Missouri-Kansas City (2001) Ph.D. in Telecommunications and Computer Networking, University of Missouri-Kansas City (2004) Research Interests: Huang’s work emphasizes secure communication protocols, privacy-preserving techniques, and resilient network architectures. He has pioneered frameworks like Secure Group Communication (SeGCom) and Attribute-Based Cryptography , addressing challenges in VANETs, SDN, and edge computing. His recent projects include developing Waterfall for SDN security and SmartDefense for DDoS mitigation. Grants & Awards: ONR Young Investigator Award (2008) HP Innovative Research Award (2008) NSF grants for secure mobile cloud frameworks and cyber-physical systems Professional Contributions: Huang has served as a reviewer for journals like IEEE Transactions on Wireless Communications and conferences such as ACM MobiArch. He co-developed the Open Human-Robotic Mobile Networking and Security Testbed (OHReST) and the Virtual Laboratory (VLab) for cybersecurity education.

Olli VarisView profile

Professor

• Water Resources Management
• Sustainable Development
• Climate Change Adaptation
• +5 more

Olli Varis is an Aalto Distinguished Professor at Aalto University's Department of Built Environment, specializing in Water and Environmental Engineering. He holds adjunct professorships at Asian Institute of Technology (Thailand) and has served as Vice Dean for Research & Innovation at Aalto School of Engineering (2013–2018). His expertise spans water resources management in developing economies, climate change impacts, and transboundary water governance. Education: Doctoral degree in Engineering (Helsinki University of Technology, 1991), Licentiate (1988), and dual Master's degrees in Agriculture (University of Helsinki, 1986) and Engineering (Helsinki University of Technology, 1984). Research focuses on sustainable development goals (SDGs), particularly water-energy-food nexus dynamics, urbanization impacts, and global river basin vulnerabilities. Key interests include flood early warning systems, migration-environment linkages, and Asian transboundary water challenges. Notable awards include Aalto Excellence Awards (2018, 2021), Best Research Paper 2023, and Environmental Research Letters' Best Paper 2010. Over 350 publications include works on China's water risks, Nepal's flood resilience, and global migration drivers. Active in UNESCO, WMO, and UNU/WIDER. Recent projects address urbanization's environmental pressures, interdisciplinary education challenges, and东南亚数字经济的崛起与挑战 (Vietnam's digital economy challenges). Leads the Water and Environmental Engineering research group, supervising 12 theses. Involved in public outreach,如芬兰赫尔辛基大学的媒体参与案例.

Ivon ArroyoView profile

Professor

• Learning Sciences
• Computer Science
• Data Science
• +9 more

Ivon Arroyo is a Professor in the Department of Teacher Education & Curriculum Studies (TECS) at the University of Massachusetts Amherst. Her research focuses on integrating novel technologies into math and computational thinking education, emphasizing affective and metacognitive states. She develops intelligent tutoring systems, such as COVES, which personalize learning in real-time and utilize facial expression recognition to enhance engagement. Her work on WearableLearning explores embodied, physically active multiplayer games for K-12 classrooms, leveraging mobile devices and wearable technologies to create immersive learning experiences. Dr. Arroyo holds an Ed.D. (2003) and M.S. (2000) from UMass Amherst and a B.S. from Universidad Blas Pascal in Argentina (1995). She has been recognized with multiple awards, including Best Paper Awards at the 2009 International Conference on Artificial Intelligence in Education and the 2010 Educational Data Mining Conference, a Fulbright Fellowship (1996), and a 1994 undergraduate prize for computer vision research. Her research interests span interdisciplinary areas such as Learning Sciences , Computer Science , Data Science , and Psychology . She prioritizes culturally responsive pedagogical agents and cross-cultural studies in educational technology, particularly in Argentina, India, and the U.S. Her projects often address challenges in developing countries, including localization of tutoring systems to Spanish. Advising and grants are central to her work, with grants like the NSF CAREER Award (2020) supporting embodied math classrooms. She collaborates on teacher dashboard frameworks and explores ethical AI integration in education. Her labs focus on creating tools that merge computational innovation with theoretical learning science principles, emphasizing real-world applications like the WearableLearning Cloud Platform.

Mark ThouinView profile

Clinical Professor

• Information Systems Education
• Experiential Learning
• Postsecondary Leadership
• +3 more

Mark Thouin is a Clinical Professor of Information Systems and Associate Dean for Graduate Programs - Academic Operations at The University of Texas at Dallas (UT Dallas), Jindal School of Management. He holds an MBA from George Mason University and a PhD from Texas Tech University. His research focuses on IT business value, experiential learning, and postsecondary leadership, with notable contributions to information systems curriculum development and agile methodologies. He teaches courses like Business Analytics with SAS and Agile Project Management. His awards include the Outstanding Undergraduate Teaching Award from UT Dallas Jindal School of Management and AITP Region 3 Star Performer of the Year. His recent articles emphasize competency models in information systems education, experiential learning simulations for agile methodologies, and curriculum standards through collaborations like ACM/AIS IS2020 initiatives. Education: PhD, Texas Tech University, 2007 MBA, George Mason University, 2004 BS, Virginia Tech, 1992 Professional Organizations: Association for Information Systems (AIS) Society for Information Management Association for Computing Machinery His work bridges academic curriculum design with industry needs, particularly through competency-based frameworks and experiential learning approaches. He has contributed to global standards for information systems graduate programs (MSIS 2016) and undergraduate curriculum guidelines (IS2020).

• Energy Efficiency in Software
• Multicore Memory Systems
• Hardware/Software Co-Design
• +3 more

Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.

Thomas Michael SattichView profile

Associate Professor

• Geopolitics
• Sustainability Transition
• Energy Policy
• +4 more

Thomas Michael Sattich is an Associate Professor at the University of Stavanger , affiliated with the Faculty of Social Sciences and Department of Media and Social Studies . His research focuses on geopolitical implications of sustainability transitions, particularly energy policy interdependence and EU-China relations. PhD in Political Science and Economics Expertise in qualitative methods, mixed methods (cluster analysis), and data visualization Lead researcher on EU-funded digital climate negotiation simulation projects His recent publications examine renewable energy geopolitics, industrial policy competition, and sustainability education frameworks. Methodologically, he specializes in Qualitative Comparative Analysis (QCA) and network analysis, contributing to debates on energy security and regional cooperation.

Francesca GrisoniView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Drug Discovery
• +8 more

Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.

• Airport Operations
• Transportation Simulation
• Air Traffic Management
• +4 more

Miguel Mujica Mota is a Senior Lecturer at the Faculty of Technology, National Autonomous University of Mexico (UNAM), and a member of the Centre of Applied Research Technology. His research focuses on airport operations, multimodal transport systems, and simulation modeling. He has expertise in analyzing capacity challenges in multi-airport systems, particularly in Mexico City, and developing decision support systems for airport security and resource allocation. His work integrates sustainability and efficiency, addressing topics like environmental reporting in airlines and post-pandemic airport recovery strategies. Research Contributions: Dr. Mujica Mota has published extensively on airport capacity optimization, multimodal transport integration, and simulation-based methodologies. Key projects include the X-TEAM D2D initiative for door-to-door travel and the IMHOTEP project for smart passenger flow management. His work often involves collaboration with institutions like Schiphol Airport and the H2020 EU framework. Research Interests: Airport terminal design, air traffic management, simulation modeling, multimodal logistics, and sustainable aviation. Awards: A-BOOST Research Fund (2020) Beste paper award EMM2018 X-TEAM D2D Project Recognition (2020) Activities: Organized conferences like the 2023 EUROSIM Simulation Seminar and served on committees for events such as the 2024 Multilog Conference. Grants & Projects: Involved in EU-funded initiatives like H2020, focusing on multimodal integration and sustainable transport solutions. His research also explores climate change impacts on infrastructure and simulation-based validation approaches.

Amanda Jensen-DossView profile

Professor

• Clinical Psychology
• Mental Health Implementation Science
• Measurement-Based Care
• +3 more

Amanda Jensen-Doss is a Professor and Director of Clinical Training in the Department of Psychology at the University of Miami's College of Arts and Sciences. Her work focuses on improving mental health care for children and adolescents through evidence-based practices, particularly in community settings. She leads the Child Implementation and Effectiveness Lab (CIELO Lab), which emphasizes translating research into clinical practice. Her research interests include measurement-based care (MBC), trauma-informed treatments, and implementation science. She has extensively studied clinician training, consultation strategies, and the role of data in optimizing youth psychotherapy outcomes. Key areas of focus include unaccompanied migrant children, adolescent treatment engagement, and therapist fidelity to evidence-based protocols. Dr. Jensen-Doss collaborates with community agencies to scale up evidence-based practices (EBPs), addressing barriers like funding and clinician self-efficacy. Her work bridges academic research with real-world clinical challenges, emphasizing pragmatic solutions to enhance mental health service delivery. She has contributed to national initiatives on MBC and EBP sustainability, and her lab provides resources for clinicians via platforms like shinyDLRs diagnostic tool. Notable grants and projects include the COMET study (Community Study of Outcome Monitoring for Emotional Disorders in Teens), which evaluates transdiagnostic treatments, and a focus on modular therapy approaches for anxiety, trauma, and conduct problems in schools. She advocates for clinician training models that balance expert consultation with cost-effectiveness. Her lab’s CIELO Lab website highlights ongoing projects on family support protocols for internalizing disorders and podcasts to improve health literacy. She is active in editorial roles, emphasizing methodological rigor and translational research in youth mental health.

• Database Technology
• Database Management Systems
• Distributed Systems
• +4 more

Michael Stonebraker is a renowned computer scientist and Adjunct Professor of Computer Science at MIT's CSAIL. He is a pioneer in database technology, having developed foundational systems like INGRES and POSTGRES at UC Berkeley. His work spans database management, distributed systems, and data integration. He has founded multiple startups to commercialize his research and holds numerous awards, including the ACM Turing Award (2014) and IEEE John von Neumann Medal (2005). He earned his Ph.D. from the University of Michigan and undergraduate degrees from Princeton and Michigan. His research focuses on advancing database systems, operating systems, and big data analytics. Recent work includes contributions to video data management, cloud computing optimization, and data discovery systems. Education: Ph.D., Computer, Information and Control Engineering, University of Michigan (1971) M.S.E., Electrical Engineering, University of Michigan (1966) B.S.E., Electrical Engineering, Princeton University (1965) Research Interests: Database Technology, Distributed Systems, Data Integration, and Big Data Analytics. His work bridges theory and practice, emphasizing scalable architectures and real-world applications. Awards & Recognition: ACM Turing Award (2014) ACM SIGMOD Systems Award (2015) MIT Tech Review TR7 (2016) C&C Prize (2020) Grants & Labs: His research is supported by grants from NSF, DARPA, and industry collaborations. He leads MIT's efforts in database systems and is affiliated with CSAIL labs focused on data management and high-performance computing.

Lorenzo CavallaroView profile

Professor

• Trustworthy AI for Systems Security
• Adversarial Machine Learning
• Malware Detection
• +6 more

Lorenzo Cavallaro is a Full Professor of Computer Science at University College London (UCL), specializing in Trustworthy AI for Systems Security. His research focuses on developing learning-based methods that are robust against adversaries by understanding the interplay between program analysis, representations, and machine learning models. His research interests span multiple critical areas in cybersecurity, including adversarial machine learning, malware detection, program analysis, and security evaluation. Cavallaro's work particularly emphasizes the challenges of concept drift in security systems and the development of robust defenses against evolving threats. His research has significant implications for Android security, binary analysis, and memory safety in embedded systems. Analysis of his recent publications (2024-2025) reveals a strong focus on addressing fundamental challenges in ML-based security systems. His work spans malware detection systems that maintain reliability under distribution shifts, adversarial attacks in the problem space, context-driven approaches using LLMs for security applications, and temporal invariance in malware detection. A recurring theme is the critical examination of whether ML-based security systems are truly robust and reliable in real-world scenarios. Cavallaro serves in significant editorial and advisory roles including the NDSS Steering Group (2023-2026), Associate Editor for Computer & Security and ACM TOPS, and Scientific Advisory Board for SERICS. He has been actively involved in program committees for top security conferences including IEEE S&P, USENIX Security, CCS, and NDSS from 2021-2025. He teaches Malware (COMP0060; 2022—ongoing), Research in Information Security (COMP0057; 2021—23), and Computer Security 2 (COMP0055; 2021—ongoing) at UCL, contributing to the next generation of security researchers and practitioners.

Patrick BrownView profile

Associate Professor

• Spatial Statistics
• Cancer Statistics
• Statistical Software
• +3 more

Patrick Brown is an Associate Professor at the University of Toronto , affiliated with the Department of Statistical Sciences and cross-appointed to the Centre for Global Health Research and St. Michael's Hospital . His research focuses on spatio-temporal data modeling , Bayesian inference , and non-parametric methods for spatial epidemiology and environmental sciences. Fields of Interest : Spatial Statistics, Cancer Statistics, Statistical Software Education : PhD from University of Lancaster His methodological work encompasses Bayesian inference for non-Gaussian spatial data, Gaussian Markov random fields, and computational techniques like INLA and MRA. Applied research themes include disease mapping, environmental risk assessment, and public health surveillance using real-world data sources such as electronic health records and wastewater monitoring . He has developed key R packages (mapmisc, geostatsp, diseasemapping) supporting spatial statistical applications. Current collaborative projects span diverse fields: Ultra-diffuse galaxy detection with astrophysical applications Multi-pollutant mortality studies in Canadian cities SARS-CoV-2 seropositivity tracking Homelessness population estimation using EHR Geospatial cancer risk tools for Nova Scotia His work bridges statistical innovation with global health challenges , emphasizing computationally efficient solutions for large-scale spatiotemporal datasets.

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

Yong-Bin Kang is a Senior Data Science Research Fellow at the ARC Centre of Excellence for Automated Decision Making and Society (ADM+S) at Swinburne University of Technology, affiliated with the School of Social Sciences, Media, Film and Education. He holds a PhD in AI from Monash University and leads numerous transdisciplinary research projects applying artificial intelligence to address complex societal challenges. Education: PhD in Faculty of IT, Monash University, Australia Dr. Kang's research focuses on Responsible AI and Society, with specific interests in developing Societal-AI platforms that integrate social data with ethical principles. His work spans healthcare, humanitech, education, financial planning, environmental health, and justice domains. He investigates how AI can enhance decision-making processes while promoting societal well-being, with particular attention to ethical implementation and human-centered approaches. His expertise encompasses AI, natural language processing, machine learning, and decision-making optimization. Analysis of Dr. Kang's recent publications reveals a strong trajectory toward socially responsible AI applications across diverse domains. His work consistently bridges technical AI capabilities with social implications, particularly focusing on ethical frameworks, community-centered design, and addressing societal inequalities through technology. The publications demonstrate increasing collaboration across disciplines including criminology, environmental science, mental health, and education. Dr. Kang is actively involved in significant research funding initiatives, with multiple ongoing projects that address critical societal challenges through AI. His supervision availability includes Doctorate (PhD) candidates, indicating his commitment to mentoring the next generation of researchers in AI and data science fields. Current Flagship Areas: Digital Capability Innovative Society Manufacturing Futures Sustainable Development Goals: Good Health and Well Being (SDG 3) Industry, Innovation and Infrastructure (SDG 9) Affordable and Clean Energy (SDG 7)