Uğur Efe Uçar is a Researcher at the Department of Interior Architecture, Faculty of Architecture, Istanbul Technical University. He holds a PhD in Informatics in Architectural Design (2021) and has been a Research Assistant since 2019. His academic journey includes a Master's in Interior Architecture Design International (2018–2020) and a Bachelor's in Interior Architecture (2015–2018), all from Istanbul Technical University. His research focuses on integrating technology into architectural education and design practices, particularly through virtual reality (VR) applications, computational frameworks, and phenomenological studies of interior spaces. Key areas include generative design pedagogy, anthropometric measurements using VR, and earthquake-resistant interior design. He has explored topics like the implicit meanings of domestic elements (e.g., beds, water systems) and flexible shelter designs for emergency scenarios. Uçar has authored over a dozen peer-reviewed articles since 2021, consistently advancing interdisciplinary approaches at the intersection of technology, human behavior, and space. His work emphasizes both theoretical exploration and practical applications in sustainable and resilient design. No scientific awards are listed, but his contributions reflect a strong commitment to academic innovation and pedagogical development.
Giuseppe Agapito is a Professor at the Department of Law, Economics and Sociology (DiGES) at the University of Camerino, where he teaches courses such as Elements of Computer Science and Data Analysis. He specializes in computational biology, bioinformatics, and health informatics, focusing on genomic data analysis, machine learning applications in healthcare, and parallel computing methodologies. His research integrates multi-omics approaches, pathway enrichment analysis, and predictive modeling for drug response and disease mechanisms. Notable contributions include tools like BioPAX-Parser and cPEA, which enhance genomic data interpretation. He actively collaborates in international studies, such as the 4CE consortium analyzing SARS-CoV-2 impacts. His work addresses challenges in privacy-aware bioinformatics, high-performance computing for genomics, and AI-driven medical diagnostics. Education details are not explicitly provided in the texts, but his academic profile reflects extensive expertise in interdisciplinary fields bridging computer science and biomedical research. He maintains an active research agenda with over 50 publications since 2018, emphasizing scalable data analysis, drug biomarker discovery, and computational methods for clinical outcomes prediction. His teaching responsibilities include IT management and data analysis modules within social science curricula, reflecting a commitment to digital literacy across disciplines. Research interests span bioinformatics tool development, genomic data preprocessing, and AI applications in healthcare, with a focus on translational research. Recent articles highlight advancements in fMRI classification using graph neural networks, privacy-preserving genomic pipelines, and edge-based deep learning for medical signal analysis. Awards and grants are not explicitly listed, but his sustained contribution to international research consortia underscores his field influence. He advises students and researchers on computational methodologies and hosts weekly office hours for academic consultations.
Ansaf Salleb-Aouissi is a Senior Lecturer in the Department of Computer Science at Columbia University’s Fu Foundation School of Engineering and Applied Science. She holds affiliations with the Foundations of Data Science and Health Analytics centers. With a PhD from the University of Orleans, France (2003), she pursued postdoctoral training at INRIA Rennes before joining Columbia as an Associate Research Scientist in 2006. She transitioned to her current role in 2015 after serving as an adjunct professor in Computer Science and Data Science from 2014–2015. Her research focuses on machine learning applications in healthcare, education, and infrastructure systems. Key areas include medical informatics (e.g., preeclampsia prediction, genetic associations in pregnancy), educational data mining (intelligent tutoring systems, bootcamp design), and power grid reliability. Notable achievements include winning the NIH Maternal Morbidity Data Challenge and developing tools like LogicLearner for logic education. She has contributed to projects such as analyzing CDC pregnancy data and optimizing the New York City power grid. Her work bridges theoretical machine learning with real-world applications, emphasizing interpretability, bias mitigation, and collaboration across disciplines. She has published extensively in venues like JMLR, TPAMI, and ECML, addressing topics from counterfactual explanations to ensemble learning with missing data.
Frank Biocca is a Professor in the Department of Informatics at New Jersey Institute of Technology (NJIT). His research focuses on augmented reality, user experience, virtual environments, and human-computer interaction. He has led or co-led multiple federally funded projects, including studies on interactive deception analysis, wearable augmented reality interfaces, and the molecular mechanisms of RNA processing. Biocca’s work bridges technology design and psychological impacts, with applications in health communication, transportation, and media studies. He has authored over 100 publications and contributed to interdisciplinary collaborations across computer science, neuroscience, and social sciences. Key research projects include the NSF-funded 'Mobile Infospaces' initiative (2002-2006), exploring augmented reality interface design, and the ongoing 'POLYAMACHINES' project (2007-2022) investigating RNA polyadenylation mechanisms. His recent publications address algorithmic transparency in news systems, spatial presence in digital displays, and trust in mobility-as-a-service technologies. Biocca has advised numerous projects at NJIT, integrating cutting-edge technologies like spatial augmented reality and VR headsets. His contributions to HCI and media studies have been recognized through sustained academic engagement and collaborations with institutions worldwide. Current research trends emphasize ethical implications of AI-driven media and immersive technology’s role in health interventions.
Fabio Persia is a Professor at the University of Naples Federico II with an extensive publication record spanning 15 years (2009-2025), demonstrating continuous academic engagement. His research portfolio spans multiple institutions through collaborations with over 70 co-authors, most notably Daniela D'Auria (43 publications), Mouzhi Ge (17), and Giovanni Pilato (14). Dr. Persia's research interests focus on the intersection of semantic computing, event processing, and practical applications. His work evolved from foundational contributions to multimedia recommender systems (2013) to developing the ISEQL interval-based surveillance event query language (2016), and most recently to healthcare AI applications. He has pioneered complex event processing frameworks for video surveillance, created multi-agent systems for epilepsy detection (PredictMed-epilepsy), and explored social sensing for personalized routing during the pandemic. His recent work increasingly integrates large language models with healthcare monitoring systems, reflecting current AI trends. Analysis of his publication trends since 2020 reveals a strong healthcare focus (65% of recent work), particularly in patient monitoring architectures, clinical decision support, and medical AI integration. His publications demonstrate consistent methodological rigor across domains, often combining semantic computing with real-time event processing to address practical challenges in healthcare and social computing contexts. Dr. Persia has served as guest editor for six special issues in the International Journal of Semantic Computing (2023-2025) covering Robotic Computing, Transdisciplinary AI, and Multimedia Computing, confirming his leadership in these research communities. His editorial roles complement his extensive publication record, which includes 25 journal articles and 76 conference papers. His collaborative research includes significant projects with Stefania Costantini on patient monitoring systems, with Mouzhi Ge on multimedia recommenders, and with Sven Helmer on interval joins and event detection. While specific grant information isn't detailed in publications, his sustained output suggests successful funding from multiple sources. His current research trajectory indicates growing emphasis on LLM integration in medical contexts and context-aware systems for public health applications.
Marta Gwinn is an Adjunct Professor in the Department of Epidemiology at Emory University. She is retired from the U.S. Public Health Service and the Centers for Disease Control and Prevention (CDC), where she contributed to advancements in genomic epidemiology and public health surveillance. Her career has focused on leveraging genomic technologies to address infectious diseases like HIV and emerging pathogens, particularly during the HIV and COVID-19 pandemics. She co-founded the CDC's Office of Genomics and Public Health and later the Office of Advanced Molecular Detection, integrating pathogen genomics into public health strategies. Dr. Gwinn earned a BA from the University of Louisville, an MD from Vanderbilt University, and an MPH from the University of North Carolina. She teaches the course EPI 552: Human Genome Epidemiology. Her research emphasizes translational applications of genomics to improve health outcomes, including epigenetic studies related to HIV, diabetes, and cardiovascular diseases. She has contributed to frameworks for genomic epidemiology, including the GRIPS reporting guidelines for genetic risk prediction studies. Her work spans over four decades, with a focus on applying molecular tools to public health challenges. Notable contributions include analyzing host genetic factors in influenza mortality, evaluating HIV seroprevalence trends, and advancing genomic surveillance for pathogen outbreaks. She has published extensively on epigenetic epidemiology, genomic applications in public health, and methodological standards for genetic association studies.
Fudong Li is a Professor and Principal Academic in Cyber Security at Bournemouth University, leading the Cyber Security pathway for multiple undergraduate programs and serving as Programme Leader for BSc Forensic Computing and Security. His expertise spans over 15 years in teaching and research, focusing on digital forensics, biometric authentication, ethical hacking, and network security. He holds certifications such as EC-Council’s CHFI, CEH, and Cisco’s CCNA. Fudong has supervised 12 completed PhD students and authored over 60 publications in journals and conferences. His research interests include wearable computing for authentication, blockchain security, privacy-preserving technologies, and cloud storage verification. Notable work includes studies on Ethereum’s consensus mechanism transition and smartwatch-based continuous authentication systems. He has led the FORESIGHT grant (2019) for advanced cyber-security simulation platforms and serves as an external examiner for multiple UK universities. Fudong’s contributions align with UN SDGs for Quality Education and Gender Equality through cybersecurity education initiatives and inclusive technology design. His advisory roles include overseeing 13+ PhD candidates and collaborating on grants addressing cybersecurity challenges in aviation, power grids, and naval systems. He also leads external engagement activities, including visiting lectureships and international examiner roles.
Tapan Mehta is a tenured Professor and Vice Chair for Research in the Department of Family and Community Medicine at the University of Alabama at Birmingham (UAB). He also holds appointments in the School of Health Professions - Health Services Administration and multiple research centers including the Comprehensive Arthritis, Musculoskeletal, Bone and Autoimmunity Center (CAMBAC), Center for Clinical and Translational Science (CCTS), and Center for Outcomes and Effectiveness Research and Education (COERE). His research spans health services, biostatistics, and data analytics with a focus on obesity, cardiometabolic conditions, disability, and rehabilitation. Dr. Mehta earned his PhD in Biostatistics and Masters in Electrical Engineering from the University of Alabama at Birmingham. His educational background combines engineering principles with statistical methodology, providing a unique foundation for his research in health services and outcomes. His research interests center on health services and outcomes research related to cardiometabolic conditions (diabetes and obesity), disability, and rehabilitation. He specializes in pragmatic study design application and development, population health initiatives, and analytics for large datasets. His work often involves developing and testing interventions for weight management, diabetes care, and physical activity promotion in diverse populations, including those with mobility disabilities. He has particular expertise in telehealth interventions, adaptive trial designs, and analyzing large existing datasets to answer critical health services questions. Analysis of Dr. Mehta's recent publications reveals a strong focus on adaptive intervention strategies for obesity and cardiometabolic conditions, telehealth delivery of care for people with disabilities, and innovative methods for improving healthcare quality. His work spans clinical trials, machine learning applications, and qualitative studies to understand patient experiences. A consistent theme across his research is the development of personalized, accessible interventions that can be implemented in real-world settings, particularly for underserved populations. Creativity is a Decision Faculty Contest Award (2016) Dr. Mehta serves as a PI and/or co-investigator on numerous research studies funded by NIH, NIDILRR, and PCORI. His grants portfolio includes the NIH-funded Nutrition Obesity Research Center Behavioral Science and Analytics Core and the CDC-funded Data Coordinating Center for the National Center on Health Physical Activity and Disability. He has mentored multiple PhD students, serving as committee chair for several dissertations in rehabilitation science and health services administration. His collaborative approach is evident in his numerous multi-institutional projects focused on improving health outcomes for people with disabilities and chronic conditions. Dr. Mehta leads several research initiatives including the Research Collaborative in the School of Health Professions and co-leads the NORC Behavioral Science and Analytics Core. His work often involves interdisciplinary teams spanning medicine, public health, engineering, and computer science. He has developed and tested numerous telehealth interventions including Movement-to-Music programs, digital coaching platforms, and AI-powered diabetes management tools designed specifically for rural and underserved populations.
Denis Lalanne is a full Professor in the Department of Informatics at the University of Fribourg, leading the 'human-building interaction' group at the Smart Living Lab and directing the Human-IST institute . He specializes in Human-Computer Interaction (HCI), focusing on multimodal interaction, visual analytics, and Human-Building Interaction (HBI). His work bridges computer science with architecture and urban design to improve building occupants' comfort and accessibility. His research spans topics such as assistive technologies for visually impaired individuals, energy-efficient lighting systems, and predictive models for indoor environmental quality. He has led projects like the Human-IST Lab and SWICE (Sustainable Well-being in Energy Transition), emphasizing interdisciplinary collaboration between academia, industry, and public institutions. Key contributions include the development of frameworks for human-centric lighting, wearable sensors for urban comfort analysis, and empathic systems to enhance HCI. His work is published in top venues like ACM Transactions on Accessible Computing , IEEE , and CHI . Education and career highlights include a PhD from EPFL (Swiss Federal Institute of Technology), postdoctoral research at IBM’s USER Group, and prior teaching roles at the University of Avignon. He is a Swiss representative at IFIP TC13 and has organized major conferences like IHM 2016.
Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .
Stephen Voida is an Assistant Professor and Founding Faculty member in the Department of Information Science at the University of Colorado Boulder, part of the College of Communication, Media, Design, and Information. He holds a PhD in Computer Science and an MS in Human-Computer Interaction from Georgia Institute of Technology. His research focuses on mental health informatics, personal informatics, and HCI, particularly exploring how technology influences mental health and supports individuals in managing conditions like bipolar disorder and diabetes. His work has been recognized with best paper awards and funded by NSF and Google Research. Voida's research spans designing tools for personal data reflection, studying multitasking and interruptions, and developing interventions for mental health challenges. He has held academic positions at institutions including Indiana University, Cornell, and the University of Calgary, alongside industry roles at Microsoft and Boeing. His current projects include technology for bipolar disorder management and infrastructure for diabetes self-care. Key awards include a CRA Computing Innovation Fellowship and a Best Paper Honorable Mention at CHI 2024. His teaching includes courses on programming, ubiquitous computing, and research methods. He remains active in HCI communities, advocating for technology that balances innovation with human well-being.
Norman Johnson is a Bauer Professor of Business Analytics and Chair of the Decision and Information Sciences Department at the University of Houston's C. T. Bauer College of Business. He holds a joint appointment as a Professor in the Hobby School of Public Affairs. His research focuses on decision-making processes, psychometric analysis, data mining, and predictive analytics, with applications in computer-mediated negotiations, virtual worlds, and healthcare information systems. He has over two decades of academic experience, including prior roles as an Assistant Actuary and current advisory roles in corporate data analytics. Johnson earned his Ph.D. from the City University of New York. His research appears in top-tier journals such as MIS Quarterly, Journal of Management Information Systems, and European Journal of Information Systems. Key themes include the impact of communication media on negotiation dynamics, affective computing in virtual environments, and the integration of structured/unstructured data analytics in public and private sectors. His applied work bridges academia and industry, with projects on predictive modeling for pension funds, healthcare IT adoption, and user engagement in virtual worlds. While no formal awards are listed, his prolific publication record reflects sustained scholarly impact. Johnson advises students in the Bauer Ph.D. programs and collaborates across disciplines, including the Hobby School of Public Affairs. His current research emphasizes big data applications in governance and healthcare, reflecting his dual role in business analytics and public policy.
Russ Cucina, MD, MS, is a Professor of Medicine in the Division of Hospital Medicine at the University of California, San Francisco (UCSF). He holds dual roles as Vice President and Chief Health Information Officer for UCSF Health System, overseeing analytics, software infrastructure, and genetic/genomic services. His academic and professional focus integrates clinical informatics, healthcare technology, and genomic medicine to advance patient care and institutional missions. Dr. Cucina's education includes an MD from UC Davis, an MS in Biomedical Informatics from Stanford, and a BA in Molecular and Cell Biology from UC Berkeley. He is board-certified in Internal Medicine. His research interests span electronic health record (EHR) interventions, clinical decision support systems, pharmacogenomics implementation, and healthcare operations optimization. Notable achievements include the 2010 Distinguished Paper Award from the American Medical Informatics Association and the AMDIS Award for clinical informatics excellence. His work emphasizes translating data-driven solutions into actionable clinical practices, such as reducing telemetry overuse and improving discharge processes. Dr. Cucina leads UCSF's genetics/genomics laboratories and strategic partnerships in health IT. He is a vocal advocate for leveraging technology to enhance healthcare quality and accessibility, with a focus on personalized medicine and system-wide informatics advancements.
John Betts is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He serves as Course Director for the Bachelor of Information Technology and previously held roles as Chief Examiner and Lecturer for multiple IT units. His research focuses on computational modelling, optimization, simulation, and data science, with applications in societal polarization, healthcare, and retail inventory systems. Education: Doctor of Philosophy (Operations Research), Monash University (2003) Graduate Diploma in Statistics/Operations Research, RMIT University (1996) Postgraduate Diploma in Mathematics and Mathematics Education, University of Melbourne (1992) Diploma in Education, Monash University (1984) Bachelor of Arts, Monash University (1983) Research Interests: Dr. Betts explores computational methods to address variability in complex systems, including agent-based modeling for societal polarization, optimization in healthcare (e.g., prostate brachytherapy), and simulation-driven decision-making in retail and transportation. His work contributes to UN SDGs related to education and sustainable cities. Projects: Leading the Optimising multi-item retail inventories project (2021–2026) focusing on inventory optimization algorithms. Contributing to the Biofocussed Prostate Cancer RadioTherapy (BiRT) project (2017–2021), developing personalized radiation therapy plans. Investigating Societal Polarization dynamics via agent-based models and hate crime measurement frameworks. Advising & Grants: Dr. Betts has secured $2.5M+ in research funding, including ARC grants for retail optimization and prostate cancer treatment planning. He advises on interdisciplinary projects spanning computer science, healthcare, and social sciences. Labs/Teams: Collaborates with Monash’s Data Science Institute and the Australian Research Data Commons, contributing to the Temporal Networks Security group and Health Informatics initiatives.
Prof. Dr. Catherine Jutzeler is an Assistant Professor at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich. Her research focuses on spinal cord injury, pain management, and the application of machine learning in medical diagnostics and rehabilitation. She specializes in developing advanced computational methods for analyzing spinal MRI scans, predicting clinical outcomes, and optimizing therapeutic interventions. Her work bridges biomedical engineering and clinical neuroscience, with a strong emphasis on translational research. Key areas include neuroimaging analysis, serological biomarker discovery, and the development of predictive models for neurological recovery. Recent studies have explored the impact of pharmacological treatments, racial disparities in biomarker responses, and biomechanical thresholds in cervical myelopathy. Prof. Jutzeler collaborates extensively on interdisciplinary projects, integrating clinical data with machine learning frameworks. She is particularly known for pioneering diffusion-based models for lumbar spine segmentation and advancing data standards in spinal cord injury research. Her contributions aim to improve diagnostic accuracy and personalize patient care in neurotrauma and chronic pain management.
