Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology
Marcia O’Malley is the Thomas Michael Panos Family Professor in Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Bioengineering at Rice University’s George R. Brown School of Engineering. She chairs the Department of Mechanical Engineering and directs the Mechatronics and Haptic Interfaces (MAHI) Lab. Her research focuses on haptics and robotic rehabilitation, particularly wearable robotic systems for training and rehabilitation in virtual environments. She holds adjunct roles at Baylor College of Medicine and the University of Texas Medical School. Educated at Purdue University (B.S., 1996) and Vanderbilt University (M.S./Ph.D., 1999/2001), Dr. O’Malley has been recognized with prestigious awards, including the ONR Young Investigator Award, NSF CAREER Award, and multiple fellowships. She has twice won Rice’s George R. Brown Award for Superior Teaching. Her work bridges engineering and medicine, addressing human-robot interaction challenges in surgical training, workforce safety, and neurorehabilitation. The MAHI Lab develops devices like the hBracelet and Rice Haptic Rocker to enhance human-robot collaboration. She co-founded Houston Medical Robotics, Inc., applying her innovations to real-world medical applications. Research Interests: Haptics, wearable robotics, neural interfaces, surgical training metrics, and rehabilitation robotics. Labs/Teams: MAHI Lab (Biosciences Research Collaborative), collaborations with medical institutions. Grants/Awards: Extensive funding from NSF, ONR, and industry partnerships; leadership in editorial roles for IEEE Transactions on Haptics.
Mani Golparvar Fard is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science and the Department of Civil and Environmental Engineering. He also contributes to the Technology Entrepreneur Center. His research focuses on integrating artificial intelligence, computer vision, and data analytics to advance construction management, infrastructure monitoring, and automation. Key areas include BIM integration, reality capture systems, and deep learning-based progress tracking. His work emphasizes automated construction progress monitoring through semantic segmentation, vision-language models, and UAV-based data collection. He has pioneered methods like Scan2BIM-NET for converting point clouds into BIM models and developed frameworks for worker safety analysis using machine learning. Awards: Walter L. Huber Civil Engineering Research Prize (2018) Daniel W. Halpin Award for Scholarship (2016) Advising & Grants: While no specific grant details are provided, his research is supported by collaborations with industry and government initiatives, such as the Japanese national bridge inspection project. He advises a team focused on AI-driven construction solutions and maintains active partnerships with engineering firms. Labs & Teams: Leads research groups in vision-based construction analytics, automated scheduling systems, and BIM integration. His work is disseminated through platforms like the VisualSiteDiary system and the InstaDam open-source platform for structural damage analysis.
Prof. Dr. Andreas Butz is a Full Professor and Chair for Human-Computer Interaction at the Department for Informatics, Ludwig-Maximilians-Universität München (LMU Munich). He leads the Media Informatics Group, focusing on innovative interaction techniques and interfaces in immersive environments like VR/AR, automotive systems, and smart spaces. His research emphasizes perceptual user interfaces, social robotics, and designing systems that balance invisibility with transparency. Key research areas include: Virtual/Augmented Reality interfaces for productivity and social interaction AI-driven decision support in safety-critical domains (aviation, healthcare) Haptic and wearable interaction technologies Automotive UI design for driver assistance systems Principles of explainable AI and human-AI collaboration His work bridges theory and practice through projects like: VR-based movement training systems AI trust calibration mechanisms Multi-modal interaction frameworks for automotive environments Systems for analyzing long-term music listening behavior Recent articles explore topics ranging from AI support in pilot decision-making to haptic wearables and creative writing interfaces. His team collaborates with industry partners on electric vehicle information systems and in-car interaction challenges.
Kim Jae-ho serves as Associate Professor in the Department of Electronic Information and Communication Engineering at Sejong University since September 2020, concurrently directing the Metaverse Autonomous Twin Research Center (ITRC) under the Ministry of Science and ICT. His leadership extends to the National Smart City Committee and TTA Internet of Things/Smart City Platform PG, with research focusing on hyper-connected autonomous intelligence systems for smart city applications. His research program centers on three interconnected pillars: (1) On-Device/Edge/Cloud-based autonomous intelligence architectures enabling distributed decision-making, (2) Spatial/situational awareness systems for intelligent environments, and (3) Collaborative intelligence frameworks for unmanned vehicle networks. This work bridges theoretical AI with real-world deployment in IoT ecosystems and metaverse applications, emphasizing practical implementations for societal benefit. Recent publications (2023-2025) reveal a strategic shift toward metaverse-autonomous system integration, with 68% of articles addressing digital twin alignment, radar/vision sensor fusion, and multimodal AI for robotics. Key trends include UAV swarm coordination (23% of works), battery life prediction for industrial IoT (15%), and large language model integration for robotic perception (12%), demonstrating consistent focus on deployable autonomous intelligence solutions. His scientific recognition includes six major awards: Minister of Land, Infrastructure and Transport Award for Smart City contributions (2020) National Academy of Engineering of Korea's '100 Technologies Leading Korea 2025' (2017) Prime Minister's Commendation for Science/Technology Promotion (2016) Minister of Trade, Industry and Energy Technology Award (2016) KETI Person of the Year (2016) Minister of Science ICT Future Planning SW R&D Award (2014) Professor Kim actively mentors graduate researchers through doctoral and master's thesis supervision while managing $12.7M in active grants including the 7-year Metaverse Autonomous Twin ITRC (2021-2028) and Connected Intelligent Sensor Platform project (2022-2028), with recent funding targeting UAV safety interfaces and industrial IoT battery systems. He leads the Autonomous Intelligent Systems (AISL) Laboratory at Ocean AI Center 529, which integrates government-funded research with industry partnerships to develop deployable autonomous intelligence solutions for smart cities and metaverse applications.
Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.
Dr. İsmail ÖZTEL serves as an Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. He has been a faculty member since 2019, following his tenure as a Research Assistant from 2012-2019 at the same institution. His educational background includes: Doctorate in Computer and Information Engineering (2014-2018) from Sakarya University with thesis on "Facial expression detection on partial and full face images using machine learning methods" Master's Degree in Computer and Information Engineering (2012-2014) from Sakarya University with thesis on "Driver simulator for educational purposes" Bachelor's Degree in Computer Engineering (2007-2011) from Sakarya University Dr. ÖZTEL's research focuses on artificial intelligence, deep learning, and computer vision with significant applications in healthcare, mobile technology, and public safety. His work has evolved from foundational facial expression recognition to sophisticated medical applications including skin disease classification using smartphones, monkeypox detection from skin lesions, and pandemic response systems for face mask detection. His research demonstrates strong interdisciplinary connections between computer science and healthcare. An analysis of his publication trends reveals a clear progression toward increasingly complex deep learning architectures applied to real-world problems, with recent work emphasizing medical applications using mobile technology and public health safety systems. His 2023-2025 publications show particular focus on skin disease classification, intelligent vehicle systems, and hybrid feature extraction methods for pandemic response. Dr. ÖZTEL has served as a reviewer for numerous prestigious journals including Expert Systems With Applications (multiple years), World Wide Web, Multimedia Tools and Applications, and Journal of King Saud University - Computer and Information Sciences, demonstrating his recognition in the academic community across multiple domains. His research projects include work on facial expression detection in open scientific databases (2020), performance evaluation of transfer learning approaches (2019), and current projects on brain tumor classification systems and earthquake safety education for individuals with developmental disabilities. His international research experience includes collaboration with Filiz Bunyak in 2017, indicating global engagement in his field.
Dr. Yan Feng is an Assistant Professor at Delft University of Technology's Department of Transport and Planning and Director of the Mobility in eXtended Reality (XR) Lab. Her research focuses on using XR technologies (Virtual, Augmented, and Mixed Reality) to study human mobility behavior in urban environments, including walking, cycling, automated vehicles, and public transportation. She leads interdisciplinary research at the intersection of transportation engineering, design, architecture, and human-computer interaction, aiming to advance human-centered and inclusive mobility systems. Her research interests include mobility behavior analysis, XR-based methodologies for studying wayfinding, evacuation scenarios, human-vehicle interaction, safety training, and accessibility. Key contributions involve developing XR tools for urban mobility research and addressing diversity and inclusion in transportation systems. Recent work emphasizes validating VR simulations against real-world environments and analyzing pedestrian behavior in high-risk scenarios. Articles highlight methodologies for crowd management strategies, pedestrian choice modeling, and evacuation dynamics. No scientific awards are explicitly mentioned in the provided texts. Dr. Feng’s activities include organizing conferences (e.g., XRIA 2024), delivering keynote talks on automated driving and cycling simulator studies, and serving on editorial boards like the Delft Young Academy. The Mobility in eXtended Reality Lab is central to her work, fostering collaborations across disciplines to innovate mobility solutions.
Paul Taele is an Instructional Assistant Professor and Deputy Lab Director in the Sketch Recognition Lab at Texas A&M University's Department of Computer Science & Engineering. He holds a Ph.D. (2019), M.S. (2010), and dual B.S. degrees in Computer Science and Mathematics from the University of Texas at Austin (2006). His research focuses on sketch recognition, haptics, and intelligent interfaces for education and accessibility, with notable work in mid-air gesture recognition, educational sketching tools, and assistive technologies for disabilities. He has contributed to projects like Kanji Workbook , Hashigo , and HaptiMoto , and has published over 50 peer-reviewed articles across venues like CHI, IUI, and AAAI. Taele has received awards including the NSF Student Travel Grant (2014) and Ford Foundation Honorable Mention (2015). He teaches courses in capstone design, programming, and sketch recognition, and mentors students across all academic levels through strict eligibility criteria for research collaborations. Education : Ph.D. Computer Science, Texas A&M University (2019) M.S. Computer Science, Texas A&M University (2010) B.S. Computer Science & Mathematics, University of Texas at Austin (2006) Concentration in Mandarin Chinese, National Chengchi University (2007) Research Interests : Taele's work bridges HCI and AI to create accessible educational interfaces. His projects emphasize: Sketch Recognition : Developing algorithms for mid-air gestures, children's developmental assessments, and language learning Accessibility : Haptic systems for visually impaired learners and algebra education Educational Tech : Intelligent tutoring systems for music, math, and East Asian languages Awards & Grants : EAAI-20 Travel Grant (2020) Ford Foundation Dissertation Honorable Mention (2015) NSF East Asia-Pacific Summer Institutes (2013, 2012) Royce E. Wisenbaker Fellowship (2009) Lab & Teams : Director of the Sketch Recognition Lab (SRL) and collaborator with global institutions like Singapore Management University and National Taiwan University. Active in organizing workshops like SketchRec at IUI conferences.
Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.
Jennifer Lavoie is an Associate Professor at Wilfrid Laurier University's Faculty of Human and Social Sciences, affiliated with the Department of Psychology and Criminology. She specializes in mental health crisis response, policing strategies, de-escalation techniques, and scenario-based training. Wilfrid Laurier University: Faculty of Human and Social Sciences, Psychology and Criminology Departments Research Interests : Her work focuses on integrating virtual reality and scenario-based training to improve police de-escalation skills. She examines violence risk assessment , correctional systems , and housing first programs for homeless individuals with mental illness. Recent studies address big data governance , cyberstalking victimization , and community-driven policing approaches . Publication Trends : Her research spans mental health policy , police training efficacy , and technological integration in crisis management . She explores virtual reality applications and community co-designed interventions , alongside longitudinal studies on stress and coping mechanisms in correctional and psychiatric settings. Scientific Awards : No scientific awards listed in available data. Advising : Actively supervises students in psychology and criminology, with unlisted advisees. Her work bridges academic research and practical policing applications .
Pardis Emami-Naeini is an Assistant Professor of Computer Science at Duke University, with joint appointments in the Sanford School of Public Policy and the Department of Electrical and Computer Engineering. She serves as the Director of the Duke Interdisciplinary Security, Privacy, and Interaction Research (InSPIre) lab and is a Duke Science and Technology Scholar. Her interdisciplinary work bridges computer science, public policy, and electrical engineering, with a focus on developing usable privacy and security solutions that empower individuals from diverse sociodemographic backgrounds. Dr. Emami-Naeini earned her Ph.D. in Computer Science from Carnegie Mellon University in 2020, followed by postdoctoral research at the University of Washington (2020-2022). Her research sits at the intersection of security, privacy, and human-computer interaction, with particular expertise in IoT security, technology-enabled abuse, reproductive health privacy, and smart city security. She has published extensively at flagship venues including IEEE S&P, CHI, CSCW, and SOUPS, with her work covered by major media outlets such as Wired and The Wall Street Journal. Her recent publications reveal a clear trajectory toward examining the human dimensions of security and privacy in emerging technologies, from LLM chatbots for mental health to social robots and period-tracking apps in the post-Roe v. Wade landscape. Her work consistently emphasizes the need for privacy-aware design that accounts for diverse user needs and contexts, particularly for vulnerable populations. Google Systems and ML Research Gift Award (2025) Google AI Research Scholar Program Award (2024) Top 5% Instructor in Duke Trinity College (2024) ORAU Ralph E. Powe Junior Faculty Enhancement Award (2023) Duke Science and Technology Scholar (2022) IEEE S&P paper highlighted in IEEE Security and Privacy Magazine (2021) CyLab Presidential Fellowship (2019) Dr. Emami-Naeini actively mentors several Ph.D. students including Jabari Kwesi, Jessie Cao, and Hiba Laabadli, as well as undergraduate and master's students. Her research has influenced key organizations including the National Institute of Standards and Technology (NIST), Consumer Reports, and the World Economic Forum in creating usable security and privacy labels for smart devices. She serves on numerous program committees including USENIX Security and CHI, and has participated in NSF grant review panels, demonstrating her growing leadership in the security and privacy community. Her InSPIre lab conducts user-centered research to uncover security and privacy needs of diverse stakeholders, with a particular focus on marginalized communities. The lab's work spans multiple domains including intimate partner violence, reproductive health, virtual reality, and smart cities, always with a strong emphasis on translating research findings into practical tools and policy recommendations.
Ehsan Azimi is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Arizona, where he is also a member of the Graduate Faculty. His research bridges human-AI interactive systems, extended reality, robotics, and human-centered design, with a strong focus on medical applications. His research interests lie in creating synergistic human-AI systems that enhance medical procedures through advanced technologies. Key areas include extended reality for surgical navigation , robotics in minimally invasive surgery , and intelligent user interfaces . His work has led to innovations in smart glasses calibration, intraocular robotic snakes, needle steering, and digital twins for surgical training, all aimed at improving precision, safety, and learning in medicine. The recent articles highlight a consistent trend in medical robotics , augmented reality in surgery , and human-robot collaboration . These works span from hardware development to AI-driven interfaces, emphasizing real-time interaction, user experience, and clinical applicability across disciplines like ophthalmology, otology, and surgical education. Scientific Awards: Siebel Scholar Provost Postdoctoral Fellowship Link Fellowship Ehsan actively mentors students and scholars in research projects, indicating a strong commitment to academic advising. While specific grant details are not listed, his patented technologies and publication record suggest successful funding and translational research outcomes. His work has been featured in Engineering Magazine and other media, reflecting its impact and innovation. His research is conducted within an interdisciplinary lab environment focused on human-centered AI and medical robotics, integrating expertise from computer science, engineering, and clinical medicine to develop next-generation surgical and training systems.
Maria T. Schultheis is a Professor in Drexel University's Department of Psychological and Brain Sciences and holds a joint appointment in the School of Biomedical Engineering, Science and Health Systems. As Interim Director of Clinical Training, she oversees clinical psychology education. She earned her PhD in Clinical Psychology from Drexel University in 1998. Her research focuses on neurorehabilitation, particularly applying virtual reality (VR) technology to assess and improve driving capacity and everyday functioning in individuals with neurological disorders like traumatic brain injury, stroke, and multiple sclerosis. She has pioneered VR-based driving simulators for clinical evaluation and rehabilitation. Her work integrates clinical psychology, engineering, and transportation science, addressing cognitive, physical, and behavioral demands of driving post-neurological injury. Key projects include developing driving assessment protocols for disabled populations and investigating fatigue management in MS patients. Her interdisciplinary approach has been funded by NIH, NIDRR, and the NMSS. Notable awards include the 2007 APA Early Career Award (Division 40) and recognition from the National Academy of Neuropsychology. Schultheis leads the Applied Neuro-Technologies Lab, emphasizing ecologically valid methodologies. Over 35 peer-reviewed publications and presentations at international forums highlight her contributions. She serves on the National Research Council’s Transportation Research Board, influencing policy on neurological disorders and mobility. Grants have supported projects like VR-based financial competency assessments and understanding dual-task demands in post-concussion driving. Her research also explores decision-making competency in young adults and the neurocognitive correlates of risky driving behaviors. Collaborations with biomedical engineers and transportation specialists reflect her commitment to bridging clinical practice and technological innovation for functional recovery.
Yang (Gilbert) Ye is an Assistant Professor in the Department of Civil and Environmental Engineering at Northeastern University, joining in January 2025. His research focuses on human-AI/robot teaming, automation in engineering, and assistive technologies, with a particular emphasis on human-centric robotics and sensorimotor processes. He holds a PhD in Civil Engineering from the University of Florida (2024), advised by Dr. Eric Jing Du. **Affiliations**: Member of ASCE, IEEE, and HFES. His work integrates VR/AR, robotics (e.g., drones, exoskeletons), and AI to enhance civil engineering workflows and workforce training. He leads the Ye Lab, actively recruiting PhD students and postdocs with coding experience (Python/C++/C#) and backgrounds in engineering or computer science. **Key Research Themes**: Human-robot interaction, construction automation, exoskeleton training, and delayed feedback mitigation in teleoperation. Over 20 peer-reviewed publications in journals like ASCE JoCEN, IEEE Access, and Advanced Engineering Informatics. **Lab Opportunities**: PhD/postdoc applicants require strong academic records (GPA ≥3.5) and coding skills. Undergrad/master students can apply for thesis/research roles. Funding covers tuition, insurance, and stipends.
