Dr. Miriam Gieselmann was a researcher at the Media Technology Department of the University of Tübingen from November 2020 to June 2024. Her work focused on human-AI interaction dynamics, trust in AI systems, and methodological challenges in studying human-machine relationships. She contributed to interdisciplinary research on AI acceptance in business contexts and pedestrian behavior analysis in urban environments. Her research interests span: Multimodal interaction design Trust and disclosure behaviors toward AI Ethical implications of conversational AI Methodological innovations for human-machine interaction studies Applications of AI in education and traffic psychology Her publications analyze AI adoption barriers, disclosure decision-making mechanisms, and perceptual dynamics in human-AI relationships. She co-authored foundational work on pedestrian communication patterns at urban intersections, contributing to safer street design principles. Miriam collaborated with interdisciplinary teams across business, psychology, and urban studies domains. Her work emphasizes bridging theoretical insights with practical applications in technology design and public policy.
Charless Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI). His research focuses on computational vision, spanning human visual system understanding, machine vision systems, and applications in biomedical informatics and forensic science. He holds a Ph.D. from UC Berkeley (2005). His work integrates techniques from computer vision, AI, and applied mathematics to address challenges in automated biological data analysis, morphology, and spatial gene expression. Key research areas include forensic science (e.g., shoeprint matching via 3D reconstruction), biomedical applications (e.g., heart function mapping and pollen classification), and AI-driven systems for scene understanding. Recent projects include a $20M forensic science center funded by the National Institute of Justice. His publications emphasize geometric reasoning, 3D reconstruction, and adaptive learning algorithms. Notable contributions include developing algorithms for 3D human pose estimation with scene constraints, automated pollen identification via CNNs, and frameworks for cross-domain forensic analysis. His work bridges theoretical computer vision with real-world applications in forensics, healthcare, and environmental science.
Alvin NG Theng Haw is an Adjunct Associate Professor at the Division of Information Technology and Operations Management, College of Business (Nanyang Business School), Nanyang Technological University (NTU). He combines over 20 years of global leadership experience in Sales and Product Management with academic roles, including serving as a Senior Career Fellow and executive coach for NTU’s Global Executive MBA and Full-Time MBA programmes. His work focuses on integrating Digital Transformation, Internet of Things (IoT), and Artificial Intelligence for Business into strategic frameworks. Research Interests : Alvin specializes in leveraging Digital Transformation and Advanced Technologies (IoT, AI) to drive business innovation. His publications in materials science demonstrate interdisciplinary applications of these technologies to fields like Soft Robotics, Self-Healing Materials, and Wearable Electronics. His expertise extends to Smart Cities, Sustainability Technology, and Industry 4.0, where he applies business strategies to technological challenges. Contributions : As a founding member of the World Economic Forum’s Digital ASEAN Skills Task Force, he advocates for digital literacy and skills development. His industry experience includes roles in networking, software, cloud platforms, and biorenewable materials, reflecting a bridge between business and engineering.
Ken Forbus is the Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University. He earned his Ph.D. in Artificial Intelligence from MIT in 1984, along with S.M. and S.B. degrees in Computer Science from the same institution. Current research focuses on qualitative reasoning , analogical reasoning , spatial reasoning , sketch understanding , and the Companion cognitive architecture . He has made foundational contributions to qualitative physics , compositional modeling , and cognitive simulation through systems like CyclePad and Companions . His work spans AI, cognitive science, and education technology with applications in intelligent tutoring systems , educational software , and interactive entertainment . Awards and Fellowships: Humboldt Research Award AAAI Fellow Cognitive Science Society Fellow ACM Fellow AAAS Fellow Herbert A. Simon Prize recipient Research trends in recent publications include analogical reasoning frameworks, normative modeling, pretense simulation, qualitative spatial representations, and applications in education and cognitive systems. Articles frequently address intersections between AI, cognitive science, and human-computer interaction. Teaching activities include core courses like Cognitive Science 207 , Design of Problem Solvers , and Conversational AI . He co-developed the open-source Freeciv game framework for AI research in strategy games.
Christopher Storie is a Professor of Geography at the University of Winnipeg, specializing in GIS, Remote Sensing, and Urban Geography. He holds an office in Lockhart Hall (5L03) and teaches courses such as Introduction to GIS, Remote Sensing, and Urbanization in the Developing World. His research focuses on Deep Learning applications for automated land use/land cover mapping, informal settlement mapping, and urban-rural fringe detection. Key research interests include leveraging neural networks for geospatial analysis, urban dynamics in regions like Mexico City, and collaborative international fieldwork. He has contributed to over 20 peer-reviewed articles since 2000, emphasizing satellite imagery analysis and environmental monitoring. His work bridges technical geospatial methods with socio-environmental challenges in urban and developing regions.
Christina Youngmi Choi is a Professor in the School of Design at the Royal College of Art (RCA), specializing in emerging technologies, healthcare, and inclusive design. She holds a BFA and MA in Industrial Design from South Korea, followed by a MSc and PhD from the Georgia Institute of Technology (Georgia Tech), where she also served as faculty, earning tenure and holding leadership roles such as Associate Chair and Director of the Graduate Program. Her research focuses on leveraging technology for assistive and inclusive design, emphasizing evidence-based and human-centered approaches. Key areas include usability assessment, assistive product design, and healthcare innovation. She has over 60 publications in journals, conferences, and books, with notable works exploring augmented reality in design and accessibility. Dr. Choi has led or contributed to significant grants, including the Rehabilitation Engineering Research Center (RERC) for Wireless Inclusive Technologies (2016–22), and the NSF-funded Feasibility and Usability Assessment of an Intraoral Inconspicuous Control Surface (2013–16). Her work spans collaborations with industry partners like LG Electronics and Jeju Airlines, addressing challenges in healthcare, education, and consumer technology. Awards: Best Paper Award at AHFE 2022 Georgia Tech Teaching Excellence Recognition (multiple years) National Science Foundation Women of Excellence Award (2016) Her advising and grants highlight interdisciplinary projects, such as integrating traditional wood joinery into CNC manufacturing and developing user-friendly medical devices. She is an editorial board member of the Journal of User Experience and peer reviewer for multiple journals and conferences. Her work also addresses privacy in health technologies and medication adherence systems, reflecting a commitment to societal impact through design.
Dr. Joyoung Lee is an Associate Professor in the Department of Civil and Environmental Engineering at New Jersey Institute of Technology (NJIT). He previously served as Laboratory Manager at the Federal Highway Administration's Saxton Transportation Operations Laboratory. His research focuses on Connected Vehicle (CV) systems, including applications in traffic management, signal control optimization, and autonomous vehicle infrastructure integration. Dr. Lee holds a Ph.D. (2010) and M.S. (2007) in Transportation Engineering from the University of Virginia, and a B.S. (2000) in Transportation Engineering from Hanyang University. His work emphasizes CV-based solutions for real-time traffic systems, cooperative vehicle-infrastructure systems (CVIS), and autonomous vehicle integration. Notable achievements include the 2019 IEEE CAVS Best Paper Award and multiple best paper recognitions from PTV User Group Meetings. His research also addresses traffic safety through innovations like the Virtual Guide Dog system for visually impaired pedestrians and advanced traffic monitoring frameworks using LiDAR and computer vision. Education: Ph.D., Transportation Engineering, University of Virginia (2010) M.S., Transportation Engineering, University of Virginia (2007) B.S., Transportation Engineering, Hanyang University (2000) Dr. Lee's research interests span smart city infrastructure, edge computing for traffic systems, and sustainable transportation solutions. He has pioneered algorithms for cooperative intersection management, automated platooning systems, and federated learning-based traffic optimization. His work bridges theoretical models with real-world implementation through partnerships with FHWA and industry stakeholders. Key contributions include development of the Cumulative Travel-Time Responsive (CTR) traffic signal control system, smart arrival notification systems for paratransit services, and advanced microsimulation calibration techniques. His lab focuses on translating CV data into actionable strategies for safer, more efficient transportation networks. Awards: IEEE CAVS Best Paper Award (2019) ASCE Grand Challenge Innovation Contest Honorable Mention (2017) PTV VISSIM Best Paper Awards (2012, 2008) Excellence in Research Award (University of Virginia, 2011) Ongoing projects include semi-decentralized graph neural networks for traffic forecasting and low-cost LiDAR-based traffic monitoring systems. His work addresses critical challenges in autonomous vehicle integration, incident management, and infrastructure resilience through interdisciplinary collaborations.
Dr. Dongyun Nie is an Assistant Professor at Dublin City University's School of Computing. She holds a PhD in Computer Science with a specialization in Customer Relationship Management. Her core research explores customer lifetime value, forecasting, data mining, and record linkage. Her recent publications demonstrate interdisciplinary work spanning health informatics, sports analytics, and environmental data engineering. Research predominantly focuses on machine learning applications for real-world data challenges including eye-tracking systems, lifelog analytics, and public health data infrastructure. Teaching responsibilities include modules on Machine Learning (CA4109), Enterprise Systems Configuration (CA2049), and Web Design (CA106), integrating research expertise into computing education.
Laura Devendorf is an Associate Professor at the ATLAS Institute and Department of Information Science at the University of Colorado Boulder. As director of the Unstable Design Lab, she bridges human-computer interaction (HCI), computational design, and craft practices through smart textiles and collaborative innovation. BFA in Studio Art and BS in Computer Science from University of California Santa Barbara PhD in Information Science from UC Berkeley Research Interests focus on smart textiles as a medium to challenge human-machine relationships, with projects exploring: Computational design tools for weaving Gendered labor in technology Biodegradable materials for wearables Interdisciplinary collaboration with craftspeople Speculative design practices Human-fungi relationships Recent Research Trends demonstrate her leadership in: AdaCAD software for parametric weaving Desktop biofiber spinning systems Interactive hygromorphic textiles Material-led HCI frameworks Scientific Recognition : Best Pictorial Award (TEI '23) Best Paper Honorable Mention (DIS ’22) Honorable Mention (CHI EA ’20) Best Pictorial Honorable Mention (DIS ’22) Collaborations & Grants : NSF CAREER Grant (2020) for smart textiles innovation Extensive partnerships with Mirela Alistar, Kristina Andersen, and others Advancing open-source tools like AdaCAD and Desktop Bio-spinning
Dr. Chutima Boonthum-Denecke is a Professor in the Department of Computer Science at Hampton University's School of Science. She joined Hampton University in 2006 as an Assistant Professor and now serves as Director of the Information Assurance and Cyber Security Center (IAC@HU). She leads the NSF CyberCorps Scholarship for Service program and has contributed to NSF initiatives like ARTSI and STARS Alliances. Her educational background includes a Ph.D. in Computer Science from Old Dominion University (2007), an MS in Applied Computer Science from Illinois State University (2000), and a BS in Computer Science from Srinakharinwirot University (1997). Dr. Boonthum-Denecke's research integrates artificial intelligence, natural language processing, and cybersecurity. Key interests include: Developing intelligent tutoring systems and educational games Secure coding practices for software engineering NLP applications in information retrieval and assessment tools Cyber-physical security for IoT and robotics Her recent publications (2016-2021) focus on machine learning applications in cybersecurity, including sentiment analysis for threat detection, blockchain-enhanced IoT security, and vulnerability assessments of emerging technologies. Collaborative work with students frequently addresses privacy ethics in AI assistants, RFID implants, and cloud systems. She mentors students through the IAC@HU lab, resulting in award-winning conference presentations on cybersecurity topics. As Principal Investigator of NSF CyberCorps, she oversees scholarship programs that bridge academic research with national security needs.
Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Faculty of Arts and Science. She holds a PhD in Information Systems from Singapore Management University (2017) and a B.Sc. in Computer Science from Zhejiang University (2012). Her research focuses on integrating heterogeneous data sources to enhance software engineering practices, including data mining, recommender systems, and social network analysis. Prior to Queen's, she was a data scientist at Living Analytics Research Centre (LARC), SMU. She has held visiting positions at Carnegie Mellon University, INRIA Paris, and SAIL Canada. Research Interests: Data Mining Software Engineering Social Network Analysis Information Retrieval Recommender Systems Computer Security Recent Research Trends: Her work emphasizes AI-driven solutions for software bug management, code translation, vulnerability detection, and developer behavior analysis. Notable contributions include leveraging LLMs for technical debt repayment and enhancing code vulnerability detection via Graph Neural Networks. Awards: SMU Presidential Doctoral Fellowship (2015-2016) Best Paper Award at SANER 2017 Grants & Advising: No formal advisees listed, but active in collaborative projects with industry and academic partners. Labs/Teams: Previously associated with SOAR Group at SMU and currently leads research in Queen's School of Computing.
Yuanzhu Chen is a Professor in the School of Computing at Queen’s University, affiliated with the Faculty of Arts and Science. He previously served as Professor and Department Head at Memorial University of Newfoundland (2005–2021). His research focuses on computer networking, mobile computing, complex networks, and applied machine learning, emphasizing wireless innovation beyond traditional wired systems. He holds a PhD from Simon Fraser University (2004) and a B.Sc. from Peking University (1999). Education: PhD in Computing Science (Simon Fraser University, 2004); B.Sc. in Computer Science (Peking University, 1999). Earlier roles include Post-doctoral Researcher at Simon Fraser University (2004–2005) and leadership positions at Memorial University, including Department Head (2019–2021). Research Interests: Network Coding and Opportunistic Routing Mobile and Wireless Network Protocols Complex Network Analysis Machine Learning Applications Indoor Positioning Systems Social Network Dynamics Selected Awards: Recipient of Queen’s University President's Award for Distinguished Teaching. Lab Affiliation: Director of the Wireless Networking and Mobile Computing Lab (WineMocol). Active in collaborative projects involving smartphone sensors, community-based environmental monitoring, and stock market prediction using web data.
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.
Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.
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.
