Ryo Suzuki is an Assistant Professor at the ATLAS Institute within the University of Colorado Boulder's Computer Science department. His research focuses on innovative intersections of Human-Computer Interaction (HCI), Augmented Reality (AR), and robotics. He explores systems that blend AI, haptics, and shape-changing interfaces to create enriched user experiences. Key areas of investigation include embedding interactivity into static educational materials (e.g., textbooks), developing AI-driven AR tools for procedural instruction, and creating shape-changing robotics for tactile feedback. His work often emphasizes practical applications in education, remote collaboration, and creative industries. Recent projects include MapStory (LLM-driven map animation), RealityEffects (3D volumetric video augmentation), and HoloDevice (holographic cross-device collaboration).
Yingying (Jennifer) Chen is a Distinguished Professor and Department Chair in the Department of Electrical and Computer Engineering at Rutgers University, affiliated with the Wireless Information Network Laboratory (WINLAB) and the DAISY Lab. She holds a PhD in Computer Science from Rutgers University (2007). Her research focuses on Smart Healthcare, IoT, Cyber Security, Machine Learning, and AR/VR Security, with over 300 publications and multiple patents. Key roles include Associate Director of WINLAB, Fellow of ACM, IEEE, and AAIA, and recipient of the NSF CAREER Award (2010), Henry Morton Teaching Award (2017), and ACM Distinguished Scientist distinction. Awards also include the 2024 ACM Fellow and NAI Fellow (2022). Her work emphasizes interdisciplinary applications, such as AR/VR privacy attacks, adversarial machine learning defenses, and edge computing. Notable grants include NSF projects on AI on edge devices, NextG-enabled manufacturing, and healthcare system design. She advises Ph.D. students and collaborates with industry on testbeds like the Community-based Edge Sensing Testbed (NSF CCRI). Current research explores AI-driven sensing, privacy in immersive technologies, and robust multi-model analytics. Publications span top venues like ACM MobiCom, IEEE INFOCOM, and IEEE S&P. Labs include DAISY Lab (data analysis & security) and collaborations with WINLAB for wireless innovation. She serves on editorial boards of IEEE/ACM Transactions and organizes conferences like ACM MobiCom and IEEE ICDCS.
Cristina Torres-Machi is an Assistant Professor of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder, holding the Beavers Professorship of Construction Engineering. She earned her PhD in Civil Engineering from Universitat Politècnica de Valencia and Universidad Católica de Chile (2015), a MSc in Planning and Management in Civil Engineering (2012) and BSc in Civil Engineering (2008) from Universitat Politècnica de Valencia. Her research focuses on infrastructure asset management, sustainability/resilience in transportation, and decision-making optimization systems. She leads the Innovation for Resilient Infrastructure (IRI) research group, advancing methodologies for long-term infrastructure management. Recognized with awards including the Abertis International Award (2016) and excellence in PhD dissertation (2015/2017), she serves as Chair of ASCE’s Construction Research Council and Vice-Chair of TRB’s Transportation Asset Management committee. Her work integrates machine learning, predictive analytics, and data science to improve pavement performance and lifecycle cost analyses. Professional affiliations include the American Society of Civil Engineers (ASCE) and active roles in TRB committees. Research emphasizes pavement rehabilitation strategies, flood impact modeling, and equitable infrastructure adoption trends. Over 60 publications (60+ citations) span journal articles, conference proceedings, and book chapters, with projects funded by public/private agencies. Current projects explore satellite-based monitoring, EV infrastructure impacts, and disaster-resilient traffic systems. Her recent work addresses procurement fallacies in design-build projects and integrates social vulnerability assessments in natural hazard planning.
Marko Lovric is an Assistant Professor at Wageningen University & Research's Department of Forest and Nature Conservation Policy. He holds a PhD in forest policy from the University of Freiburg, Germany. His expertise spans forest policy, environmental governance, data analysis, and AI applications in forestry. He has 17 years of research experience, focusing on European ecosystem services, bioeconomy transitions, forest certification, and international forest policy formulation. Education: PhD in Forest Policy (University of Freiburg), prior roles include senior researcher at the European Forest Institute and assistant at the University of Zagreb's Faculty of Forestry. Research Interests: Marko’s work addresses forest policy implementation, ecosystem service valuation, bioeconomy modeling, and international trade dynamics. He employs quantitative methods, big data, and network analysis to explore governance innovations and sustainable forest management strategies. Projects: He coordinated the H2020 SINCERE project on forest ecosystem services and the Horizon Europe project on forest research ecosystems. Current projects include INTERCEDE, focusing on future forest incomes and ecosystem services. Teaching: Involves MSc courses in forest policy, including internships and thesis supervision in forest conservation policy. Labs/Teams: Active in Wageningen’s Forest and Nature Conservation Policy group, contributing to interdisciplinary projects on governance and bioeconomy transitions.
Dr. Muhammad Najib is an Assistant Professor (Lecturer in the UK system) at Heriot-Watt University's School of Mathematical and Computer Sciences in Edinburgh. He is also an Associate Member of the University of Oxford's Department of Computer Science. His research focuses on ensuring AI safety through formal verification methods, particularly in multi-agent systems. Najib holds a DPhil/PhD from the University of Oxford, supervised by Julian Gutierrez and Mike Wooldridge, and has industry experience at Samsung Electronics. Education: BSc from Sepuluh Nopember Institute of Technology, MSc from the University of Liverpool, DPhil/PhD from the University of Oxford. He previously worked as a postdoctoral researcher at TU Kaiserslautern under Anthony Lin. Research Interests: Logic and game theory in AI foundations, equilibrium verification in multi-agent systems, temporal logics, and formal verification techniques. He developed the EVE tool for rational verification. Najib actively supervises PhD students and collaborates on projects like the UKRI AI CDT-D2AIR. Key Contributions: Published 17+ research outputs since 2018. Areas include equilibrium design, probabilistic multi-agent systems, and computational complexity analysis. His work bridges formal methods with AI safety, emphasizing automated synthesis and model checking.
Jonathan Roberts is a researcher at Bangor University, UK, with a focus on data visualization, visual analytics, and educational technology. His work bridges computer science and creative design, particularly in data art exhibitions and authentic learning. Key research areas: Data Visualization, Visual Analytics, Educational Technology, Digital Art Recent publications explore generative AI in visualization design, multiple-view patterns for time series data, and frameworks for creative learning. His collaborations span institutions like QUT, University of Manchester, and University of Cambridge. Notable awards include VAST 2012 Honorable Mention and VAST 2010 Analytic Process Recognition. He contributes to visualization pedagogy and has co-authored works on haptic interfaces, immersive analytics, and coastal data modeling.
Erisa Karafili is an Associate Professor in Cybersecurity at the University of Southampton. She leads Teaching Methods Innovation at the GCHQ/EPSRC Academic Centre of Excellence for Cyber Security Education (ACE-CSE) and is a Champion in Security by Design at ACE-CSR. A Fellow of the Higher Education Academy, she joined the University in 2020 after roles including a Marie Curie Fellowship at Imperial College London, where she investigated cyber-attack attribution techniques. Her research focuses on formal methods applied to security, IoT threat models, and secure data sharing frameworks. Education: PhD in non-classical logics applied to multi-agent systems security from the University of Verona. Previous positions include PostDoc at Technical University of Denmark and Researcher at Imperial College London. Research Interests: Cyber-attack attribution, IoT security, formal methods in cybersecurity, data privacy, and argumentation-based reasoning for security. Awards: Higher Education Academy Fellowship. Current PhD Students: Betul Gokkaya, Mohammed Homaid Alquliti, Peter Geoffrey Williams, Steve Johnson. Active Projects: Heterogeneous Material Integrated MEMS/NEMS-Photonics Platform for Secure Communication (collaborative with Jize Yan and others).
Vahid Yazdanpanah is an Assistant Professor (Lecturer) of Computer Science at the University of Southampton and a Visiting Lecturer at the University of Twente. He holds a PhD from the University of Twente (2019), an MSc in Artificial Intelligence from Utrecht University (2015), and an MBA from the University of Greenwich (2012). His research focuses on multiagent systems, AI responsibility, and circular economy applications. He leads the Agents, Interaction and Complexity (AIC) research group and is an RRI Champion for the UKRI CDT in AI for Sustainability (SustAI). Research interests include agent-based computing, formal logics for multiagent decision-making, and socio-technical systems. Notable awards include the Best Paper Award at PRIMA 2016 and recognition at AAMAS 2021. He teaches courses such as COMP2211 (Software Engineering) and supervises PhD students in AI and sustainability. Active projects include the ARGOS (AI Resilience Governance) and AutoTrust (Internet of Vehicles) initiatives. He has served on program committees for AAAI, IJCAI, and AAMAS, and reviews for journals like AI & Society and Annals of Operations Research. His work bridges technical AI advancements with ethical, legal, and societal challenges in responsible AI deployment.
Christine ABDALLA MIKHAEIL is an Assistant Professor in the Department of Management of Information Systems at IÉSEG School of Management in France. She holds dual Ph.D. degrees in Business Administration with a focus on Information Technology from the University of Paris Dauphine (France) and Georgia State University (USA), alongside advanced degrees in Business Consulting and Administration from Paris Dauphine. Her research focuses on collective action dynamics in social media, cybersecurity and privacy challenges, artificial intelligence applications, and disinformation propagation. Recent work explores the adoption of privacy-enhancing technologies (PETs), paradoxes in hybrid work visibility, and data adequacy in qualitative IS research. She has published in leading journals such as Information Systems Journal and Information and Organization . Her articles reflect a strong emphasis on understanding socio-technical systems through interdisciplinary lenses, combining behavioral theories with digital technology analysis. No specific awards or grant details are mentioned in her profile. She currently advises no formally listed students.
Lawrence Kim is an Assistant Professor at the School of Computing Science, Simon Fraser University. His research focuses on Human-Computer Interaction, Tangible User Interfaces, and Human-Robot Interaction, with teaching interests in Physical Computing and Human-Centered Computing. Education: PhD in Mechanical Engineering (Stanford University, 2020), MS in Mechanical Engineering (Stanford, 2015), and BS in Mechanical Engineering (University of Illinois at Urbana-Champaign, 2013). Research interests emphasize tangible interaction design, swarm robotics, and assistive technologies for special needs populations. His work includes developing interfaces like Woogu for child education and DiminishAR for cognitive enhancement. He directs the Tangent Lab (https://tangent.cs.sfu.ca/), exploring embodied and robotic interaction. Teaching includes courses such as CMPT 263 (Introduction to Human-Centered Computing) and CMPT 415/416 (Special Research Projects). His recent articles address topics like head posture correction in VR, programmable fidgeting with swarm robots, and stress prediction via mouse movements.
David Hästbacka is an Associate Professor (tenure track) at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on software engineering, industrial automation, and energy systems, emphasizing system architecture, interoperability frameworks, and dependable IoT solutions. He leads a research group exploring edge and cloud computing, semantic integration, and smart energy systems. Education & Professional Background : While specific educational details are not provided, his academic career includes roles such as Postdoctoral Researcher in the SEMIS project (2017-2020) and extensive involvement in EU-funded initiatives like COCOP (EU H2020) and Horizon Europe projects. Research Projects : Active in high-impact projects like Hedge-IoT (Horizon Europe, 2024-2027), TwinfFlow (Business Finland), and TRINEFLEX (Horizon Europe), with a focus on industrial automation, distributed systems, and energy grids. Past projects include FEMMa (Business Finland), DisMa (Academy of Finland), and Arrowhead (ECSEL). Teaching & Supervision : Specializes in Web/Cloud architectures, IoT systems, and dependable automation technologies. Supervises students in topics like edge computing frameworks and MLOps pipelines. Technical Contributions : Develops frameworks for industrial interoperability (e.g., OPC UA PubSub integration), edge-cloud toolchains, and MLOps methodologies. His work addresses challenges in microservices, Kubernetes distributions, and semantic data integration. Labs & Teams : Leads a research group advancing automation technologies through interdisciplinary collaboration, with partnerships in industry and academia to bridge theory and practice in smart systems.
Joseph Alejandro Gallego Mejia is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He holds a PhD with meritorious distinction in Systems and Computing Engineering from the National University of Colombia, along with a Master’s and dual Bachelor’s degrees in Systems and Computing Engineering and Industrial Engineering. PhD in Systems and Computing Engineering, National University of Colombia (Meritorious Distinction) Master of Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Industrial Engineering, National University of Colombia His research focuses on artificial intelligence, machine learning, computer vision, quantum machine learning, natural language processing, and cybersecurity. He explores robustness estimation, anomaly detection, incremental learning, and scalable software architectures for AI systems. His work bridges theoretical foundations and practical applications in health, remote sensing, and edge computing. The recent publications reflect a strong trend in interdisciplinary AI research, combining machine learning with quantum computing, cybersecurity, and natural language understanding. His work spans domains such as satellite imagery analysis, medical diagnostics, IoT security, and conversational AI, demonstrating a commitment to scalable and robust intelligent systems. Keywords across publications include Computer Science, Machine Learning, Quantum Computing, and Cybersecurity, with subfields ranging from adversarial robustness to hybrid quantum-classical models. Scientific distinctions include: PhD with meritorious distinction, National University of Colombia Postdoctoral fellow, Frontier Development Lab (Trillium), supported by NASA and ESA He has served as a reviewer for top-tier journals and conferences including Neurocomputing, IEEE Access, Radioscience, NeurIPS, and NLDL. Though no formal grants are listed, his postdoc was funded by NASA and ESA, indicating significant external support. He teaches courses in programming, data science, machine learning, deep learning, NLP, and software engineering. He founded the tech company Sammu and mentors students through instruction and research supervision. He is actively involved in research and teaching, contributing to innovative programs in AI and computing education. His lab and team affiliations are not explicitly stated, but his work suggests collaboration with AI, quantum computing, and cybersecurity research groups.
Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.
Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.
S. Mohadeseh Taheri-Mousavi is an Assistant Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She joined CMU in September 2022 after postdoctoral appointments at MIT and Brown University. Her research is supported by major grants from NASA STRI, DARPA, the Army Research Laboratory, and the Naval Nuclear Laboratory, and she is affiliated with the NextManufacturing Center and the Wilton E. Scott Institute for Energy Innovation. Her educational background includes a Ph.D. from EPFL, Switzerland, and M.Sc. and B.Sc. degrees from Sharif University of Technology, Iran. She was awarded both early and advanced Swiss National Science Foundation fellowships during her postdoctoral studies. Taheri-Mousavi’s research focuses on the intersection of materials science, mechanical engineering, and computer science. She develops multi-scale computational models and AI-driven frameworks—such as AlloyGPT and generative AI agents—to design next-generation structural alloys, particularly for additive manufacturing and extreme environments. Her work emphasizes materials sustainability, industrial decarbonization, and uncertainty quantification in alloy design. The integration of machine learning with Integrated Computational Materials Engineering (ICME) and CALPHAD methods enables rapid exploration of high-dimensional composition and processing spaces. Her recent publications (2023–2025) show a strong trend toward AI/ML applications in alloy discovery, hydrogen embrittlement modeling, and high-temperature aluminum and tungsten alloys. These works reflect a deep commitment to accelerating materials innovation through human-AI collaboration and smart experimental validation. Her scientific honors include prestigious Swiss National Science Foundation fellowships. She has also received seed funding from the Scott Institute for Energy Innovation to study hydrogen embrittlement. She advises a dynamic team of doctoral students and a postdoctoral researcher, working on topics including hydrogen embrittlement, generative AI for welding, and gradient alloys. Her research is funded by high-impact grants from NASA, DARPA, the Army, and the Naval Nuclear Laboratory, supporting transformative projects in structural alloy design. She leads the Taheri-Mousavi Group, which operates within CMU’s Materials Characterization Facility and the NextManufacturing Center. The group focuses on developing novel AI-integrated computational frameworks to guide efficient and intelligent experimentation in alloy development.
