Wouter Beek is a post-doctoral researcher at VU University Amsterdam and co-founder of Triply . His work focuses on Semantic Web technologies and knowledge-intensive applications, particularly through large-scale knowledge bases. He has co-developed key tools like the LOD Laundromat , LOD Search , and sameAs.cc . Email: w.g.j.beek@vu.nl Email: wouter@triply.cc Research Interests : Beek specializes in Semantic Web standards, data quality, and empirical semantics. His work bridges Web semantics with pragmatics, emphasizing innovative reuse of linked data across domains like geospatial analysis and historic data. He explores formalization of meaning within knowledge graphs to enable automated interpretation. Key Projects : LOD Laundromat : Infrastructure for crawling and cleaning linked data. sameAs.cc : Service addressing entity resolution via owl:sameAs. 3D SPARQL Visualization : Enhances geo-spatial and statistical interpretation of SPARQL results. Scientific Awards : Best Resource Paper Award at ESWC 2018 for 'Near Sameness is Somewhat the Same As SameAs'. Applications : His work impacts policy-making (e.g., energy savings investment strategies), real-estate analysis, and citizen services through knowledge graph integration.
Dr. Katrina Schmid is an Associate Professor at Queensland University of Technology (QUT) , affiliated with the School of Clinical Sciences under the Faculty of Health . Her work bridges clinical optometry with cutting-edge research in refractive error development , myopia control , and ocular surface disorders , while also contributing to teaching pedagogy and authentic assessment in optometric education. PhD (Queensland University of Technology) BAppSc(Opt)(Hons) (Queensland University of Technology) Dr. Schmid’s research spans diverse areas including binocular vision , amblyopia , meibomian gland dysfunction , and the GABAergic system in eye growth control . She has coordinated units like Foundations of Optometric Practice , emphasizing professional identity and evidence-based competencies. Her scientific accolades include the Classic Paper Award (OPO, 2014) and the JLloyd Hewett Award (CXO, 2020) . Her 2021 rankings (#25 globally in refractive error research, #115 in optometric author impact) underscore her international influence. Classic Paper Award, OPO, 2014 JLloyd Hewett Award, CXO, 2020 QUT Medal recipient As an Editorial Board Member for journals like IOVS , TVST , and Nature Scientific Reports , she shapes academic discourse. Her teaching expertise includes ocular anatomy , pharmacology , and research methods , with a focus on student mentoring and examination validation.
Anthony C. Robinson is Professor of Geography and the E. Willard and Ruby S. Miller Professor at The Pennsylvania State University. He serves as Director of Online Geospatial Education Programs through the John A. Dutton e-Education Institute and Director of the GeoGraphics Lab within the Department of Geography in the College of Earth and Mineral Sciences. Robinson also holds leadership positions as Vice Chair of the International Cartographic Association Commission on Geovisualization and Chair of the AAG Cartography & Mapping Specialty Group. Dr. Robinson earned his B.S. in Applied Geography from East Carolina University (2002), followed by his M.S. (2005) and Ph.D. (2008) in Geography from Penn State. His academic career at Penn State has progressed from Research Assistant (2003-2008) to his current position as full Professor (2024-present), with previous appointments as Assistant Professor (2015-2019) and Associate Professor (2019-2024). Robinson's research focuses on designing and evaluating geovisualization tools to improve geographic information utility and usability across multiple domains including epidemiology, crisis management, national security, and higher education. His key contributions include characterizing how users assemble analytical results, studying visualization tools through eye-tracking methodologies, and exploring map symbol standardization for emergency management. Recent innovative work examines viral cartography in social media, techniques for visualizing the 'presence of absence' in big spatial data, and geographic dimensions of learner engagement in educational analytics. His publication record demonstrates a consistent trajectory toward increasingly complex geospatial challenges, with early work focused on foundational geovisual analytics methods evolving into current research addressing big data cartography, social media geovisualization, and educational applications of geographic information science. Robinson's scholarly output bridges theoretical cartographic principles with practical applications across diverse domains, particularly emphasizing user-centered design approaches. E. Willard and Ruby S. Miller Professor in Geography Past President of the North American Cartographic Information Society (NACIS) Co-Chair of the International Cartographic Association Commission on Visual Analytics Co-Director of GeoGraphics Lab Director of Online Geospatial Education Programs since 2014 As an advisor, Robinson has mentored doctoral students including Tim Prestby (recent PhD graduate) and currently supervises PhD candidate Lily Houtman. He actively seeks graduate students interested in cartography and geovisualization, user-centered design and evaluation, and health and ecological informatics. His research has been supported through institutional positions at Penn State including leadership of online geospatial education programs serving thousands of students globally. Robinson directs the GeoGraphics Lab, which serves as an incubator for innovative geospatial projects including community mapping initiatives and geospatial storytelling devices focused on community resilience and climate action. The lab recently hosted its inaugural Community Mapping Day at Penn State, engaging students, faculty, and community members in mapping pollinator pathways. Through the John A. Dutton e-Education Institute, he leads Penn State's GIS Certificate, Master of GIS, and Master of Spatial Data Science programs, which have educated thousands of professionals worldwide.
Bo Luo is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas . He serves as Director of the High Assurance and Secure Systems (HASS) Research Center within the Institute for Information Sciences (I2S) , a National Center of Academic Excellence in Cyber Defense and Research by the National Security Agency. Education: Ph.D. in Information Sciences and Technology, Pennsylvania State University (2008) M.Phil. in Information Engineering, Chinese University of Hong Kong (2003) B.E. in Electronic and Information Engineering, University of Science and Technology of China (2001) His research focuses on security and privacy at the intersection of data science, AI/ML, IoT/CPS, and network security . Current projects include adversarial machine learning, privacy compliance in smart devices, and hardware-enabled security solutions. He leads the InfoSec Research Group , mentoring students in areas like IoT security, deep learning vulnerabilities, and cryptographic systems. Recent article trends highlight IoT device vulnerabilities (2025), privacy compliance in automotive apps (2024), adversarial AI-art detection (2024), and secure computation frameworks (2024). His work appears in top venues like ACM CCS , USENIX Security , and IEEE TDSC . Scientific Recognition: ACSAC 2021 Distinguished Paper Award ACSAC 2017 Best Paper Award CCS 2022 Best Paper Honorable Mention ICPC 2024 Distinguished Paper As Principal Investigator for the Jayhawk SFS CyberCorps Scholarship , he trains future cybersecurity professionals. His lab collaborates on cyber-physical security and AI safety , with alumni placed at institutions like Beloit College, Apple, and Amazon.
Matt Higham is an Assistant Professor at St. Lawrence University in the Math, Computer Science, and Statistics Department . His research focuses on Spatial Statistics with ecological applications, particularly Spatial Prediction Models that account for imperfect detection of animals in surveys. Education: PhD in Statistics from Oregon State University (2019) B.S. in Statistics and Botany from Miami University (2014) Research interests involve developing statistical methodologies for ecological applications, including spatial sampling , spatio-temporal modeling , and imperfect detection adjustment in wildlife surveys. He contributes to R software packages like spmodel and sptotal for spatial data analysis. Recent publications demonstrate trends in spatial statistics (4/7 articles), ecological modeling (5/7 articles), and statistical software development (2/7 articles). Key subfields include spatial prediction , block kriging , finite population estimation , and big spatial data handling . Teaching activities include courses in Introduction to Statistics , Applied Regression Modeling , Foundations of Data Science , and Data Visualization . Personal interests include racket sports, jogging, gaming, hiking, and backpacking.
William J. Turkel is a Professor of History at The University of Western Ontario, Canada, and a member of the Royal Society of Canada's College of New Scholars. His research focuses on computational history, science and technology studies, and disability studies. He holds a PhD from MIT (2004) and leads the History Department's Fab Lab, equipped with advanced fabrication tools. Turkel's work includes reverse-engineering historical technologies, digital exhibit design, and mentoring over 20 students. He has authored books like Spark from the Deep and The Archive of Place , and his open-source textbook Digital Research Methods with Mathematica is widely used. Awards include the Western Award for Technology-Enhanced Teaching (2021) and SSHRC funding for NiCHE (2004–14). Education: PhD, MIT (2004) Research Interests: Computational methods, big history, modular synthesis, disability studies, and astrobiology. His lab explores tangible computing, 3D printing, and historical experimentation. Articles Overview: Recent work spans digital humanities tool development, historical computing analysis, and music-pattern recognition via machine learning. Key themes include bridging computational methods with historical inquiry. Awards: Royal Society membership (2018), Western University award (2021), and SSHRC leadership (2004–14). Advising & Grants: Supervised over 20 students and postdocs, including Devon Elliott and Ian Milligan. Collaborates with Tim Hitchcock, Edward Jones-Imhotep, and others on projects like MK ULTRA analysis and exoskeleton patent studies. Labs/Teams: The History Department Fab Lab includes 3D printers, CNC tools, and electronics prototyping. His lab designs interactive exhibits and tangible artifacts to explore historical phenomena.
Sorin Draghici is a Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research specializes in computational biology, integrating multi-omics data to discover disease subtypes and repurpose drugs. He develops bioinformatics tools like ROntoTools and CPA for pathway analysis and single-cell data interpretation. Research Focus: Machine learning applications in oncology and infectious diseases, including COVID-19 proteomics and ovarian cancer recurrence prediction. Recent innovations include AI-driven data interrogation (LmRaC) and upstream regulator prediction (PURE). Publications emphasize scalable algorithms for big data challenges.
Megan Monroe is an Associate Teaching Professor in the Department of Computer Science at Tufts University . Her academic journey includes a Ph.D. and M.S. from the University of Maryland and a B.S. from Carnegie Mellon University . Research Interests : Monroe specializes in Visual Analytics , focusing on Temporal Data Analysis and Computational Thinking . She developed the EventFlow visualization tool used in healthcare and defense sectors. Her work bridges Artificial Intelligence , Computation Theory , and Human-Computer Interaction . Publications highlight her contributions to Interactive Visualization , Temporal Querying , and Multi-user Environments , with applications in healthcare data and machine learning debugging. Scientific Awards : Information is Beautiful Awards (2016) HCIL-Yahoo! Research Award (2013) VAST 2013 Honorable Mention Audience Choice Award at Pitch Dingman Competition (2011) Monroe’s teaching includes Computation Theory , Machine Structures & Programming , and Teaching Computer Science courses. She previously worked at IBM Research , designing tools for Watson technologies.
Carolyn Seaman is a Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC), and Director of the Center for Women in Technology. Her research focuses on empirical studies of software engineering, particularly technical debt, software maintenance, and qualitative research methods. She also explores topics like communication in software teams and knowledge management. She holds affiliations with the Empirical and Applied Software Engineering Lab (EASEL) at UMBC, the International Software Engineering Research Network (ISERN), and serves on the editorial board of the Information and Software Technology Journal (ISTJ). Her work bridges theoretical and practical aspects of software development, emphasizing practitioner insights and organizational challenges. Dr. Seaman has mentored numerous PhD students, including graduates focused on software process improvement, open-source adoption, and technical debt management. Her current advisees include Brian Frey and Abdullah Aldaeej. Her publications span journals like IEEE Transactions on Software Engineering and conferences such as the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. Her research trends highlight technical debt management, qualitative methods in software engineering, and the intersection of software practices with organizational structure. Awards are not explicitly mentioned, but her contributions to software engineering education and diversity initiatives, such as the T-SITE program for transfer students, reflect her broader academic impact.
Adam Sales is an Assistant Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), with affiliations in Learning Sciences & Technologies and Data Science. He holds a BS in Physics and Mathematics from Johns Hopkins University and a PhD in Statistics from the University of Michigan. His research focuses on causal inference using large administrative datasets, integrating machine learning with design-based analysis of randomized trials and observational studies. Key methodological interests include principal stratification, mediation analysis, and regression discontinuity designs applied to educational and social science problems. Recent work involves analyzing log data from intelligent tutoring systems, refining regression discontinuity approaches, and applying high-dimensional covariates to improve matching estimators. He emphasizes statistical rigor in empirical research and collaborates across disciplines to strengthen educational data analysis. Articles highlight applications in education technology, health-risk behaviors, and policy evaluation, reflecting interdisciplinary engagement with learning sciences, data science, and social sciences.
Dr. Olena Syrotkina is a Professor at the University of Windsor's School of Computer Science. She holds a Ph.D. in Mathematical Simulation from Ukrainian State University of Science and Technologies and an M.Sc. in Computer Science from Dnipro University of Technology. Her research focuses on SCADA diagnostics, big data analytics, discrete mathematics, and machine learning algorithms. Research Interests: Her work spans computational methods for large-scale data analysis, including machine learning applications in industrial systems and theoretical frameworks for data optimization. Primary areas include: SCADA system reliability and diagnostics Big data processing and resource optimization Machine learning-driven predictive modeling Research Trends: Her recent publications emphasize mathematical optimization in big data contexts, with themes like quantum computing applications, fault detection in complex systems, and e-commerce analytics. Over 80% of her 2020-2024 publications involve scalable algorithms for industrial data. Honors & Advising: No awards are listed. She currently mentors graduate students but no named advisees were specified. Laboratory & Teams: Leads research in computational methods at UWindsor, collaborating with industrial partners on SCADA and data infrastructure projects.
Dr. Samiya Khan is a Lecturer in Computer Science at the University of Greenwich, UK, affiliated with the School of Computing and Mathematical Sciences within the Faculty of Engineering and Science. She holds a PhD in Computer Science from Jamia Millia Islamia, India (2020), and is an alumna of the University of Delhi, India. Previously, she served as a postdoctoral research fellow at the University of Wolverhampton, UK. Her expertise spans data science, artificial intelligence, edge computing, IoT, and digital health. Her research contributions include over 25 publications in high-impact journals and conferences, as well as authored/co-edited books such as 'Big Data and Analytics' and 'Internet of Things (IoT): Concepts and Applications' (Springer Nature). She has also contributed as an Associate Editor for Scientific India Magazine and a reviewer for multiple journals and conferences. Samiya’s work emphasizes interdisciplinary applications, including cybersecurity for industrial systems, smart healthcare solutions, and sustainable computing. Her recent research explores XR (Extended Reality) for clinical training, IoT in urban sustainability, and AI-driven telehealth services. Her publications highlight advancements in anomaly detection for critical infrastructure, blockchain-enabled IoT security, and AI frameworks for medical imaging. She has also authored works on big data preprocessing and cloud computing challenges in academia and industry. Key contributions include a book on 'Extended Reality for Healthcare Systems' (Elsevier, 2024) and frameworks for low-cost green computing services. Her research bridges theoretical innovation with practical applications in healthcare, education, and environmental sustainability.
Chris Herdman is a Professor in the Department of Psychology at Carleton University, Canada, and Director of the Advanced Cognitive Engineering (ACE) Lab within the Visualization and Simulation Centre (VSIM). His research focuses on human perception, cognition, and their application to advanced human-machine systems, particularly in aviation and automotive contexts. He holds a Ph.D. from the University of Alberta. Research interests include cognitive health assessment for pilots, driver safety, and the integration of neurophysiological measures (e.g., EEG) to evaluate human performance in high-stakes environments. His lab collaborates across disciplines, involving students from Psychology, Cognitive Science, Biology, and Engineering. Recent work emphasizes developing tools like CANFLY for pilot risk assessment and studying driver engagement using machine learning. His studies often combine experimental methods with real-world applications, addressing challenges like age-related cognitive decline and automation in transportation. Key projects include investigating the effects of workload, sleep deprivation, and sensory integration on task performance. The ACE Lab also explores autonomous vehicle disengagement protocols and the impact of pandemic-related changes on driving behavior. Labs/Teams: Advanced Cognitive Engineering (ACE) Lab, Visualization and Simulation Centre (VSIM). Collaborations span academic, governmental, and industry partners.
Dr. Dong-Han Ham was part of the academic staff at Middlesex University's Computer Science department. His research focuses on human factors, cognitive ergonomics, and safety-critical systems, particularly in nuclear power plant operations and decision support systems. He has contributed to frameworks for usability evaluation, human error analysis, and visual analytics. Key research interests include human-system interaction, task complexity analysis, and applying systems theory to improve safety and efficiency in complex environments. He has developed methodologies like the MaRMI-III framework for component-based software development and the TACOM extension for nuclear plant task analysis. His work often intersects with big data applications, such as extracting human reliability data from incident reports using Safety-II concepts. He has also explored usability in mobile interfaces and the design of physical controls for process systems. Ham has organized conferences like ECCE 2007 and contributed to editorial roles in journals. His research emphasizes practical applications of theoretical models, aiming to bridge gaps between cognitive science and engineering systems.
Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.
