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Dr. Kathryn Kaiser is an Assistant Professor in the Department of Health Behavior at the University of Alabama at Birmingham (UAB) School of Public Health. She holds concurrent appointments in multiple research centers including the Center for Clinical and Translational Science and the Nutrition Obesity Research Center (NORC). Her research focuses on meta-research methodologies, race/sex disparities in obesity, systematic reviews of nutrition interventions, and advancing FAIR data principles for scientific communication. Education: B.S. Microbiology (Texas A&M), B.S. Medical Technology (University of Texas Health Science Center), Ph.D. in Health Psychology (University of North Texas Health Science Center). Postdoctoral training in Vascular Biology/Hypertension at UAB. Extensive background in medical diagnostics instrumentation and laboratory science prior to academia. Research Interests: Systematic review methodologies, FAIR data standards, obesity disparities with a focus on neuroendocrine mechanisms, and translational research in bariatric surgery outcomes. Specializes in methodological rigor for clinical trials and evidence synthesis. Grants: NIH-funded projects on FAIR principles implementation, dairy intake research, obesity energetics, and lifespan studies. Recent grants include $2.1M for metadata education programs and $1.8M for knowledge mapping initiatives. Awards: Recognized as 2015 Top Reviewer for American Journal of Preventive Medicine. Serves as Associate Editor for Frontiers in Nutrition. Active member of Cochrane Collaboration and multiple professional societies. Teaching: Graduate courses in Health Program Evaluation, Psychophysiology, and Systematic Review Design. Supervises doctoral students in health behavior research through 30+ dissertation committees since 2011. Labs/Teams: Key contributor to UAB's Nutrition Obesity Research Center (NORC) and Center for Outcomes and Effectiveness Research (COERE). Collaborates with international metadata initiatives like Metadata 2020 and science dialogue mapping projects.

• Meta-Research
• Obesity Disparities
• Systematic Reviews
• +6 more

Anupam Joshi is the Acting Dean of the College of Information Technology and Engineering and Oros Family Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He also directs UMBC’s Center for Cybersecurity and leads the National Cybersecurity FFRDC for the University System of Maryland. His research focuses on networked computing, AI-driven cybersecurity, privacy-preserving technologies, and policy-driven security frameworks. He holds a Ph.D. in Computer Science from Purdue University (1993), an M.S. (1991), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1989). Dr. Joshi’s work spans over 400 publications with 32,650+ citations (h-index 92) and nine patents. His grants include funding from NSF, DARPA, NASA, NIST, and industry partners like IBM and Northrop Grumman. Key contributions include developing CAPD frameworks for IoT security, FABULA for automated threat intelligence, and KiNETGAN for intrusion detection through synthetic data. He is an IEEE Fellow and pioneer in applying AI to secure critical infrastructure, smart grids, and healthcare systems. His research trends emphasize AI-empowered cybersecurity, privacy compliance in data sharing (e.g., agriculture, healthcare), and mitigating attacks on smart systems. Notable projects include combating fake cybersecurity reports using provenance analysis, securing EV charging infrastructure, and enhancing smart farming resilience through policy-driven access control. Awards: IEEE Fellow Grants: Over $30M from NSF, DoD, NASA, and industry collaborations Labs/Teams: Director of UMBC Center for Cybersecurity, Cybersecurity Knowledge Graph initiatives Future work includes advancing neurosymbolic AI for cybersecurity, semantic data extraction from scientific literature, and AI ethics in healthcare applications.

• Networked Computing
• Intelligent Systems
• Cybersecurity
• +6 more

Dong Li is an Assistant Professor in the Department of Computer Science and Electrical Engineering (CSEE) at the University of Maryland, Baltimore County (UMBC). His research focuses on wireless sensing, mobile computing, wearable sensing, multi-modal sensing, and smart health, aiming to develop affordable and accessible technologies to address healthcare equity and environmental sustainability challenges. He holds a PhD from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, an M.Eng. in Software Engineering from Shanghai Jiao Tong University, and a B.S. in Computer Science from the University of Electronic Science and Technology of China. His work has been published in prestigious venues such as MobiCom, SenSys, IPSN, UbiComp, and HotNets. Key research themes include anomaly detection via knowledge graphs, privacy prediction models for social networks, and interactive recommendation systems. His interdisciplinary approach integrates machine learning, data mining, and cybersecurity to tackle real-world problems in health and environmental sustainability. Dr. Li’s contributions span theoretical advancements and practical system development, with a focus on bridging the gap between cutting-edge research and societal impact. His recent publications highlight innovations in data stream processing, weak supervision frameworks, and privacy behavior analysis in online platforms.

• Wireless Sensing
• Mobile Computing
• Wearable Sensing
• +4 more

Yingying (Jennifer) Chen is a Distinguished Professor and Department Chair in the Department of Electrical and Computer Engineering at Rutgers University, affiliated with the Wireless Information Network Laboratory (WINLAB) and the DAISY Lab. She holds a PhD in Computer Science from Rutgers University (2007). Her research focuses on Smart Healthcare, IoT, Cyber Security, Machine Learning, and AR/VR Security, with over 300 publications and multiple patents. Key roles include Associate Director of WINLAB, Fellow of ACM, IEEE, and AAIA, and recipient of the NSF CAREER Award (2010), Henry Morton Teaching Award (2017), and ACM Distinguished Scientist distinction. Awards also include the 2024 ACM Fellow and NAI Fellow (2022). Her work emphasizes interdisciplinary applications, such as AR/VR privacy attacks, adversarial machine learning defenses, and edge computing. Notable grants include NSF projects on AI on edge devices, NextG-enabled manufacturing, and healthcare system design. She advises Ph.D. students and collaborates with industry on testbeds like the Community-based Edge Sensing Testbed (NSF CCRI). Current research explores AI-driven sensing, privacy in immersive technologies, and robust multi-model analytics. Publications span top venues like ACM MobiCom, IEEE INFOCOM, and IEEE S&P. Labs include DAISY Lab (data analysis & security) and collaborations with WINLAB for wireless innovation. She serves on editorial boards of IEEE/ACM Transactions and organizes conferences like ACM MobiCom and IEEE ICDCS.

• Smart Healthcare
• Internet of Things (IoT)
• Cyber Security
• +7 more

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +3 more

Prof. Enkelejda Miho is a Professor of Digital Life Sciences at the School of Life Sciences, FHNW, leading the aiHealthLab. Her work bridges computer science/AI with life sciences, focusing on drug discovery, personalized medicine, and immunology. She holds roles as Team Leader at aiHealthLab and Group Leader at the Swiss Bioinformatics Institute. Research Interests : She applies machine learning to analyze immune repertoires, antibody engineering, and autoimmunity diagnostics. Her lab develops computational tools like the RWD-Cockpit for real-world data analysis and synthetic antibody-antigen models (Absolut!) to advance biotherapeutics. Her work on dengue immunity and monoclonal gammopathies highlights translational applications. Key Projects : The aiHealthLab focuses on AI-driven diagnostics and therapeutics. Her contributions include AI frameworks for antibody specificity prediction, age-related immune repertoire changes, and large-scale network analysis of antibody repertoires. Labs/Teams : Leads aiHealthLab and collaborates with the Swiss Bioinformatics Institute, integrating computational and experimental immunology.

• Digital Life Sciences
• Medical Informatics
• Artificial Intelligence in Healthcare
• +4 more

Cristina Torres-Machi is an Assistant Professor of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder, holding the Beavers Professorship of Construction Engineering. She earned her PhD in Civil Engineering from Universitat Politècnica de Valencia and Universidad Católica de Chile (2015), a MSc in Planning and Management in Civil Engineering (2012) and BSc in Civil Engineering (2008) from Universitat Politècnica de Valencia. Her research focuses on infrastructure asset management, sustainability/resilience in transportation, and decision-making optimization systems. She leads the Innovation for Resilient Infrastructure (IRI) research group, advancing methodologies for long-term infrastructure management. Recognized with awards including the Abertis International Award (2016) and excellence in PhD dissertation (2015/2017), she serves as Chair of ASCE’s Construction Research Council and Vice-Chair of TRB’s Transportation Asset Management committee. Her work integrates machine learning, predictive analytics, and data science to improve pavement performance and lifecycle cost analyses. Professional affiliations include the American Society of Civil Engineers (ASCE) and active roles in TRB committees. Research emphasizes pavement rehabilitation strategies, flood impact modeling, and equitable infrastructure adoption trends. Over 60 publications (60+ citations) span journal articles, conference proceedings, and book chapters, with projects funded by public/private agencies. Current projects explore satellite-based monitoring, EV infrastructure impacts, and disaster-resilient traffic systems. Her recent work addresses procurement fallacies in design-build projects and integrates social vulnerability assessments in natural hazard planning.

• Infrastructure Asset Management
• Sustainability and Resilience in Transportation
• Decision-Making and Optimization Systems
• +3 more

John Byabazaire is a Research Fellow at the School of Computer Science, University College Dublin (UCD). He holds a PhD in Computer Science from UCD (2024), following a BSc (Gulu University, 2013) and MSc (Waterford Institute of Technology, 2018). His research focuses on IoT systems for data collection, remote sensing, AI-driven end-to-end system management, and fog analytics. He has held academic roles including Assistant Lecturer at Gulu University (2018–2019) and teaching roles at UCD since 2019, including Occasional Lecturer and Senior Teaching Assistant. His research spans smart agriculture, data quality in IoT, and education technology. Notable contributions include frameworks for yield mapping in precision agriculture, trust-based data validation in IoT, and machine learning approaches for livestock health monitoring. He has secured grants like the National ICT Initiatives Support Program (Uganda Government, 2019–2020). Teaching includes courses on cloud computing, web development, and distributed systems. His articles emphasize IoT data quality, agricultural analytics, and educational technology innovation. He actively promotes technology adoption in African education and agriculture sectors through collaborative projects.

• IoT Systems
• Data Quality
• Fog Analytics
• +7 more

Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.

• Internet of Things
• Cyber-physical Systems
• Wireless and Mobile Sensing
• +7 more

Ningchuan Xiao is a Professor of Geography at The Ohio State University's Department of Geography. His work bridges Geographic Information Science (GIScience) with computational methods, emphasizing spatial optimization, cartography, and machine learning integration. Education: Ph.D. in Geography from The University of Iowa (2003). Courses taught include GIS fundamentals, cartography, and Python-based spatial analysis. Research Interests: Spatial Optimization: Developing algorithms for land acquisition, redistricting, and resource allocation. Machine Learning & Cartography: Exploring AI-driven map interpretation and ethical visualization of complex data. Census Data: Innovating privacy-preserving techniques while maintaining data utility, including temporal/spatial modeling. Open Source Tools: Authored GIS Algorithms (2016) and maintains GitHub repository 'gisalgs' for accessible code. Publications: Recent work (2023-2025) highlights advancements in synthetic microdata generation, privacy-utility tradeoffs in census aggregation, and AI-driven cartographic recognition. His 2022 studies include traffic camera analytics and choropleth map QA systems. Awards: Not explicitly listed in the provided texts. Advising & Grants: Collaborated with researchers like Y. Lin, J. Li, and S. Bao. Projects include the Sustainable Columbus Observatory (SCO) for urban sustainability metrics. Research is supported through academic partnerships and computational initiatives.

• Machine Learning and Spatial Data
• Census Data
• Spatial Optimization
• +3 more

Prof. Dr. Pablo Valdivia Martin is a Full Professor of European Culture and Literature at the University of Groningen, leading the Research Centre for the Study of Democratic Cultures and Politics (DemCP) . He holds affiliations including Research Associate at Harvard University's Paulson School and Academic Director of the Netherlands Research School for Literary Studies (OSL). His expertise spans cultural narratives, digital humanities, AI, and applied physics. He has supervised 9 PhD candidates and completed 7 PhD and 4 postdoc projects. Awards include the 2018 Lecturer of the Year and 2022 PIEoneer Award. Current research focuses on AI-enabled cultural analysis and crisis narratives. Education: BA (Hispanic Studies, Granada), BA (Comparative Literature, Valladolid), MA (UNED), PhD (University of Nottingham, 2007). Research interests: Cultural Analytics, Technology & Society, Computational Literary Studies, and University Innovation. Key projects include the INNOVALAB Erasmus+ initiative and leadership in the 'Humour in Court' NWO VIDI grant. Awards: Recognized for contributions to teaching and innovation, with international impact through advisory roles at NIAS-KNAW and Chilean research networks. Advising: Overseeing 9 active PhD projects on topics like AI systems for metaphor analysis, sensorial perception in former detainees, and legal narratives in media.

• Humanities
• Cultural Studies
• Literary Theory & Criticism
• +7 more

Professor Donald Wlodkowic is a faculty member at RMIT University's School of Science (Biosciences), leading the Neurotox Laboratory. His research focuses on aquatic ecotoxicology, behavioral ecotoxicology, and eco-neurotoxicology, with expertise in neurotoxins, industrial pollutants, and neuroactive drugs' effects on central nervous systems. He innovates biomicrofluidic technologies and digital video-based analysis for high-throughput behavioral studies in toxicology. Research Interests: Aquatic toxicology, neurotoxicology, water quality, and eco-neurotoxicology. His lab develops real-time biomonitoring systems and early-warning tools for water quality assessment. Current projects include neurotoxicity studies of pollutants, high-throughput behavioral assays, and embryo-based risk assessment. Teaching: Coordinates courses in cell biology, biochemistry (BIOL2146/2333/2420, PROC2048) and supervises honors projects (ONPS2313). He integrates digital tools into bioscience curricula for innovative teaching. Advising & Leadership: Supervises Masters and PhD students in projects such as 'Emerging Pollutants on Aquatic Animal Behavior' and 'Nanotoxicology.' He has led international academic-industry collaborations in Australia, New Zealand, and Europe, fostering motivated research teams. Labs & Teams: NeuroTox Lab pioneers interdisciplinary research in ecotoxicology and neurotoxicology, combining microfluidics, digital analytics, and behavioral assays to address environmental and health challenges.

• Aquatic Ecotoxicology
• Behavioral Ecotoxicology
• Eco-Neurotoxicology
• +7 more

Kerryn Butler-Henderson is an Adjunct Professor in the Department of Health and Biomedical Sciences at RMIT University. Her research focuses on digital health transformation, healthcare informatics, and workforce education. She actively contributes to understanding the integration of artificial intelligence, predictive analytics, and electronic health records (EHR) into healthcare systems. Her work emphasizes improving clinical outcomes through data-driven approaches and addressing challenges in workforce digital literacy. She holds an ORCID identifier (0000-0002-6082-2108) and is open to supervising PhD and Masters students. Research interests include: Learning health systems and AI applications Prediction modeling for musculoskeletal disorders Digital health workforce development Value-based healthcare frameworks Educational innovations in nursing and healthcare Key publications from 2024-2025 explore topics like EHR generalizability, digital health literacy measures, and global digital health workforce roles. Her interdisciplinary collaborations span occupational health, gerontology, and healthcare accounting. Dr. Butler-Henderson is engaged in curriculum development for digital health competencies and has contributed to national surveys on nursing education trends. She maintains strong industry connections through RMIT's Health and Biomedical Sciences research networks.

• Digital Health
• Health Informatics
• Healthcare Transformation
• +4 more

Associate Professor Bruno Schivinski is affiliated with RMIT University's School of Media & Communication. He specializes in online consumer behavior, quantitative research methods, and multivariate data analysis. His work bridges digital media impact, consumer psychology, and health behavior, with a focus on gaming disorder, social media engagement, and brand equity. Education and professional background include roles at Gdansk University of Technology and consulting for institutions like the Polish Ministry of Science. He serves as Associate Editor for the Journal of Management and Business Administration–Central Europe . Research interests span digital phenotyping, behavioral addictions, and sustainable consumption. Notable contributions include studies on gaming disorder measurement, food waste reduction, and influencer marketing effectiveness. His work is published in top-tier journals like Journal of Business Research and Journal of Clinical Medicine . Recognition includes the Vice-Chancellor’s Award for Research Impact (2020), Emerald Literati Outstanding Reviewer (2022), and multiple best paper awards. He supervises research projects on digital behavior, food waste, and social media's role in health. Professional memberships include the Royal Statistical Society, Higher Education Academy, and American Marketing Association. His interdisciplinary approach addresses real-world challenges in digital health, consumer behavior, and sustainability.

• Online Consumer Behavior
• Quantitative Research Methods
• Multivariate Data Analysis
• +5 more

Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.

• Artificial Intelligence in Medicine
• Biomedical Natural Language Processing
• Health Informatics
• +3 more