Bohan Chen is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences. Their work focuses on advancing graph-based machine learning techniques and their applications in environmental science, remote sensing, and AI-driven analysis. Research interests include graph neural networks, active learning strategies, hyperspectral image analysis, and knowledge graph integration. Notable contributions include the development of the GLL layer for neural networks, the CUSP permafrost dataset, and hybrid models for multispectral image processing. Key research trends span theoretical advancements in graph-based learning and practical applications such as environmental monitoring, SAR data analysis, and pandemic modeling. Their work bridges computational methodologies with real-world environmental and societal challenges. Advising and grants: No student advisees listed. Contributions include foundational research without explicit grant mentions in provided text. Labs/teams: No specific lab or team affiliations mentioned in the text.
John Jones is an Associate Professor at The Ohio State University specializing in Digital Media Studies, with a focus on rhetoric, professional/technical communication, and digital literacies. His academic journey includes a PhD in English from the University of Texas – Austin and an MA from the University of Tennessee – Chattanooga. Previously, he served as an associate professor at West Virginia University and a visiting assistant professor at the University of Texas at Dallas. Research explores intersections of writing, computational processes, and digital culture Expertise spans digital media studies, visual rhetoric, and networked communication His work investigates the impact of wearable technologies, algorithmic systems, and network structures on knowledge creation and communication practices. Current projects examine computational ethics in digital writing and the role of digital networks in shaping rhetorical strategies. Analysis of John Jones’s publications reveals consistent engagement with: Digital media and computational rhetoric Networked communication frameworks Wearable technology implications Medical usability challenges Visual data representation Collective intelligence dynamics
Dr. Farzan Sasangohar is an Associate Professor in the Department of Industrial & Systems Engineering at Texas A&M University, holding the Mike and Sugar Barnes Faculty Fellowship. He also serves as an Assistant Professor at Houston Methodist Hospital's Center for Outcomes Research and Department of Surgery. His academic roles include affiliations with the Environmental and Occupational Health, Biomedical Engineering, and multiple centers focused on health technologies and systems design. Education: PhD in Industrial Engineering (Human Factors Engineering), University of Toronto (2015) Research Interests: Human factors in healthcare delivery and telehealth systems Wearable technology for stress/health monitoring Crisis management team cognition and decision-making Remote patient monitoring systems Mental health self-management interventions Awards: Jack A. Kraft Innovator Award (HFES, 2023) Dr. Hamed K. Eldin Early Career Award (2022) William C. Howell Young Investigator Award (2021) TEES Young Faculty Fellow (2021) Nominated for Ergonomics Journal Best Paper (2021) Advising & Grants: Mentored over 15 graduate students including Dr. Mahnoosh Sadeghi (PhD 2023) Recipient of NSF PATHS-UP Engineering Research Center funding Active grants in offshore worker fatigue management and telehealth integration Labs/Teams: Director of the Applied Cognitive Ergonomics Lab (ACE-lab) , focusing on human-system interactions in healthcare, aviation, and disaster management. Current projects include: - Wearable stress monitoring systems - Telehealth integration frameworks - Crisis team cognition analysis
Michael Krauthammer is a Professor of Medical Informatics and Chair of the Department of Quantitative Biomedicine at the University of Zurich, affiliated with the University Hospital of Zurich. His lab focuses on Clinical Data Science and Translational Bioinformatics, leveraging AI and machine learning to address healthcare challenges. Key areas include cancer genomics, federated learning, and automated medical imaging analysis. Education and affiliations: Krauthammer leads an interdisciplinary team supported by major funding agencies. His research spans bioinformatics, clinical decision support systems, and multimodal data integration. Notable projects include AI-assisted diagnosis in rheumatology and prime editing efficiency prediction. Recent work emphasizes longitudinal cfDNA analysis, drug interaction modeling, and personalized oncology. The lab collaborates across disciplines, with projects funded by Swiss and international grants. Students and postdocs work on topics like machine learning for radiology reports, longitudinal disease trajectories, and protein design. Key projects include the NTCIR-18 RadNLP challenge, prime editing prediction models (Nature Biotechnology 2024), and vision transformers for capillaroscopy analysis. The lab advocates for reproducible data science and ethical AI in healthcare.
Jeremy I. Borjon is an Assistant Professor in the Department of Psychology at the University of Houston, affiliated with the College of Liberal Arts and Social Sciences. He leads the Developing Systems Laboratory, focusing on infant cognitive, sensorimotor, and autonomic development. His research is supported by an NICHD R00 award and integrates multimodal technologies such as eye-tracking, motion capture, and wireless physiological sensors. Education: A.B. in Psychology and Neuroscience, Princeton University Ph.D. in Psychology and Neuroscience, Princeton University Dr. Borjon's research centers on how infants coordinate internal states with emerging cognitive and motor systems during the first two years of life. He investigates how visual, motor, and autonomic processes interact in real time, particularly during naturalistic caregiver interactions. His work emphasizes ecological validity by studying infants in dynamic, real-world contexts. He is particularly interested in sustained attention, language development, and how caregiver behaviors shape infant cognition. His recent publications reflect a strong trend in using dense, naturalistic behavioral sampling to understand developmental processes. The articles highlight interdisciplinary approaches combining developmental psychology, neuroscience, and engineering to study real-time cognitive and physiological dynamics in infants. Topics include physiological synchrony, attention regulation, and sensorimotor integration. Scientific Awards and Honors: R00 Pathway to Independence Award, NICHD K99 Pathway to Independence Award, NICHD NSF Postdoctoral Research Fellowship NICHD T32 Postdoctoral Fellowship 2019 Small Grant for Early Career Scholars, SRCD NSF Graduate Research Fellowship Princeton President’s Fellowship Simons Fellow in Computational Neuroscience Dr. Borjon has been actively involved in mentoring and is currently reviewing graduate applications for the Developmental, Cognitive, & Behavioral Neuroscience Program. His research is supported by federal grants, indicating active funding and research productivity. He previously held postdoctoral fellowships at Indiana University and positions at Yale and Emory. He directs the Developing Systems Laboratory, which employs cutting-edge technology to study infant behavior in naturalistic settings. The lab integrates head-mounted eye-tracking, wireless cardiorespiratory sensors, motion capture, and audiovisual recording to examine how cognitive achievements emerge within the context of a developing body and social environment.
Perpetua Kirby is an Assistant Professor in Childhood and Youth (Education) at the School of Education and Social Work, University of Sussex . She specializes in children’s rights, participation, and agency, utilizing ethnographic and multimodal methodologies to explore democratic approaches to education. Her work bridges research and practice, focusing on sustainability education and transformative models through collaborations with organizations like the Centre for Innovation and Research in Childhood and Youth (CIRCY) and the Centre for Teaching and Learning Research (CTLR) . Research Themes: Children’s rights and agency in educational settings Interdisciplinary sustainability education (Global North/South) Ethnographic and creative research methods Transformative pedagogies for climate uncertainty Key Projects: Nuffield-funded study on children’s voices and data in UK local authorities Collaboration with PASTRES Programme on art-based climate pedagogy Fellow of Sussex Sustainability Research Programme (SSRP) Scientific Awards: Fellow of the Sussex Sustainability Research Programme (SSRP) Teaching: Co-convenes Forest Food Garden elective at University of Sussex Supervises MA in Education and doctoral research training Media Impact: Featured in BBC Radio 4’s Women’s Hour, Financial Times, Huffington Post
Anthony Hornof is a Professor in the Department of Computer Science at the University of Oregon, part of the College of Arts and Sciences. He has been a faculty member since 1999 and was granted tenure in 2005. His research is centered on human-computer interaction, with strong emphases on cognitive modeling, eye tracking, and assistive technology. He leads an active research laboratory and has secured substantial funding from the National Science Foundation and the Office of Naval Research. University: University of Oregon School: College of Arts and Sciences Department: Department of Computer Science Position: Professor Email: hornof@uoregon.edu, hornof@cs.uoregon.edu Office: 356 Deschutes Hall Phone: (541) 346-1372 Education: B.A. in Computer Science, Columbia University, 1988 M.S. in Computer Science and Engineering, University of Michigan, 1996 Ph.D. in Computer Science and Engineering, University of Michigan, 1999 Research Interests: Dr. Hornof's research lies at the intersection of human cognition and computing. He is particularly interested in understanding and modeling the perceptual, cognitive, and motor processes involved in human-computer interaction. His work uses eye tracking both as an evaluation tool for cognitive models and as a real-time input method for creative expression and accessibility. A major focus is assistive technology, especially developing tools like EyeDraw that enable children with severe motor impairments to create art using only eye movements. He also explores eye-controlled musical compositions, bridging technology and artistic expression. His research is grounded in participatory design, involving end-users directly in the development process. Publication Trends: His recent publications demonstrate a consistent focus on modeling human behavior in complex interactive tasks. Key themes include visual search strategies, dual-task performance, cognitive modeling using eye-tracking data, and accessibility. His work spans top venues in HCI (CHI, TOCHI), cognitive science (CogSci, ICCM), and specialized conferences like ETRA and NIME. There is a strong methodological thread involving data calibration, model validation, and the development of predictive tools for interface design. Scientific Awards: Best Paper Award (Top 1%) at CHI 2014 (two papers) Honorable Mention Paper (Top 5%) at CHI 2010 Siegel-Wolf Award for Best Applied Paper at ICCM 2010 Advising and Grants: Dr. Hornof actively seeks to mentor exceptional undergraduate students, graduate students, and postdoctoral researchers in his lab. He emphasizes rigorous and creative scientific research. He has been awarded over $2.9 million in single-investigator research grants from prestigious agencies including the National Science Foundation (NSF) and the Office of Naval Research (ONR). Notably, he served as an NSF Program Director from 2012 to 2014, contributing to funding decisions for approximately $65 million in research. Labs and Teams: He leads the Human-Computer Interaction Laboratory at the University of Oregon, where interdisciplinary research is conducted on cognitive modeling, eye tracking, and assistive technologies. His team has developed software such as VizFix for visualizing eye-tracking data and has ported the Eyegaze system to Macintosh. The lab fosters collaborations with new media artists and musicians, and engages in participatory design with children who have disabilities.
Abhinav Dhall is an Associate Professor in the Department of Data Science & AI at Monash University. His research focuses on computer vision, affective computing, and human-centered AI, with a particular emphasis on deepfake detection, multimodal analysis, and ethical AI applications. He is actively involved in organizing workshops like the Multimodal and Responsible Affective Computing (MRAC) and chairs conferences such as ACCV. Dhall accepts PhD students and has contributed significantly to datasets like AV-Deepfake1M and EmotiW challenges. His work spans topics including HDR imaging, facial expression recognition, and AI ethics in multimedia systems.
Dr. Yen-Ting (Allen) Yeh is an Assistant Professor in the Department of Computer Science at the University of Saskatchewan, where he leads research in Human-Computer Interaction focusing on mobile interaction techniques, collaborative tools, and creative technologies. PhD, Cheriton School of Computer Science, University of Waterloo MS, Graduate Institute of Networking and Multimedia, National Taiwan University His research explores physical and cognitive human capabilities through: Innovative phone interaction methods (folding, dexterous gestures, side-touch expansion) Collaborative writing environments with privacy controls Creativity augmentation systems for 3D modeling Augmented reality and interactive fabrication tools Recent publications demonstrate strong focus on: Acoustic input techniques using finger snapping Motion-based creative reflection tools Dynamic gesture recognition systems Collaborative editing comfort optimization Scientific recognition includes: ACM Creativity and Cognition 2021 Honorable Mention The research group at the University of Saskatchewan's HCI Lab actively seeks students interested in phone interactions, human factors, AR/VR, collaborative tools, and creative arts applications.
Eakta Jain is an Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida's College of Engineering. Her research centers on human-computer interaction with a specialized focus on eye-tracking technologies, virtual reality, and privacy-preserving techniques in immersive environments. With over 15 years of sustained academic contributions, she has established herself as a leading researcher in gaze analysis and its applications across multiple domains. Dr. Jain's research interests span eye-tracking, virtual reality, extended reality (XR), privacy in immersive technologies, human-computer interaction, computer vision, and animation. Her work demonstrates a consistent trajectory from fundamental gaze analysis techniques to practical applications addressing critical privacy concerns in emerging technologies. She has made significant contributions to understanding how gaze data can be used to enhance user experience while simultaneously developing methods to protect user privacy in these systems. Analysis of her recent publications reveals a strong focus on privacy challenges in XR environments, with particular attention to gaze data protection, face-swapping technologies, and the psychological impacts of continuous monitoring. Her research bridges theoretical insights with practical implementations, often resulting in novel algorithms and frameworks that address real-world problems in immersive technologies. The interdisciplinary nature of her work connects computer science with cognitive psychology and human factors research. Dr. Jain has received recognition through publications in top-tier venues including IEEE Transactions on Visualization and Computer Graphics, ACM Transactions on Applied Perception, and the Symposium on Eye Tracking Research and Applications. Her work has been influential in shaping the discourse around privacy in immersive environments and has practical implications for the development of ethical XR systems. She actively mentors students and collaborators, with several junior researchers appearing as co-authors on her publications. Her research group appears to focus on the intersection of computer vision, graphics, and human-centered computing, with projects spanning from fundamental gaze analysis to applied privacy-preserving techniques in commercial VR systems. Current projects suggest strong industry connections and potential grant funding supporting her privacy-focused XR research.
Dr. Richard Segall is a Professor in the Department of Information Systems and Business Analytics at Arkansas State University , affiliated with the Beck College of Sciences & Mathematics . He is also affiliated faculty in the Master of Engineering Management (MEM) Program , the Environmental Sciences Program , and serves on thesis committees at the University of Arkansas at Little Rock (UALR) . Education: Ph.D. in Operations Research, University of Massachusetts at Amherst (1984) M.S. in Operations Research and Statistics, Rensselaer Polytechnic Institute (1975) M.S. in Mathematics, Rensselaer Polytechnic Institute (1973) B.S. in Mathematics, Rensselaer Polytechnic Institute (1971) Dr. Segall's research spans data mining, text mining, web mining, big data analytics, bioinformatics, supercomputing applications, and mathematical modeling . His work bridges business analytics and computational biology , with a focus on transdisciplinary applications in agriculture, healthcare, and space systems. His recent publications emphasize genomic data analysis , plant disease diagnostics , AI-driven healthcare solutions , and space technology forecasting . The integration of machine learning , data visualization , and open-source tools is a recurring theme across domains. Scientific Awards & Grants: Three research awards from the National Research Council (NRC) Software grants from Oracle Corporation and SAS Institute, Inc. Dr. Segall has served on the editorial boards of the International Journal of Data Science , International Journal of Data Mining, Modelling and Management , and International Journal of Fog Computing . He previously contributed to the Arkansas Center for Plant-Powered Production (P3) and currently participates in the Center for No-Boundary Thinking (CNBT) .
Bo An is a President's Chair Professor and Head of the Division of Artificial Intelligence at the College of Computing and Data Science , Nanyang Technological University, Singapore . He also holds a courtesy appointment as Professor at the School of Physical & Mathematical Sciences and serves as Director of the Centre of AI-for-X. Previously, he was a Nanyang Assistant Professor (2014-2018), Associate Professor at the Chinese Academy of Sciences (2012-2013), and Postdoctoral Researcher at the University of Southern California (2010-2012). His academic journey began with B.Sc. and M.Sc. degrees from Chongqing University, followed by a Ph.D. in Computer Science from the University of Massachusetts, Amherst (advised by Victor Lesser). Research Interests : Artificial Intelligence Multiagent Systems Computational Game Theory Reinforcement Learning Automated Negotiation Optimization Research Impact : Applications in infrastructure security (deployed by US Coast Guard and Federal Air Marshals), e-commerce, sensor networks, and financial technology. Over 150 publications in top venues like AAMAS, IJCAI, AAAI, ICML, NeurIPS, KDD, and ACM/IEEE Transactions. Scientific Recognition : 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award 2012 INFORMS Wagner Prize 2018 & 2022 Nanyang Research Awards 2017 Microsoft Collaborative AI Challenge IEEE Intelligent Systems 'AI's 10 to Watch' (2018) Leadership Roles : Editor-in-Chief of IEEE Intelligent Systems, Associate Editor for AIJ, JAAMAS, and ACM Transactions. Served as General Co-Chair for AAMAS'23 and Program Chair for IJCAI'27.
Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)
Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.
Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.
