Dr. Moyra Derby is an Associate Professor in Fine Art at the University of Leeds , School of Fine Art, History of Art and Cultural Studies. Her practice-based research combines studio work with theoretical inquiry, focusing on the intersections between painting , curatorial practices , and neuropsychology of attention . PhD in Fine Art (University of Kent, 2022) MA in Painting (Royal College of Art, London) Her studio practice explores mathematical systems and exponential sequences in painting, often engaging with art historical sources to challenge painting conventions. Collaborative projects like Interval [ ] and Working Spaces examine the spatial contingencies between painting and film, and the relationship between painting and architecture. Notable exhibitions include Diagramming (The Foundry Gallery, 2023) and In Correspondence (RaumX, 2022). She contributes to the editorial board of the Journal of Contemporary Painting and co-founded Crate Studio in Margate for supporting emerging artists.
Nathan B. Speirs is an Assistant Professor in the Mechanical Engineering Department at Brigham Young University's College of Engineering. He joined the faculty in 2023 and specializes in interfacial fluid dynamics using high-speed photography and theoretical modeling. His research explores water entry mechanics, cavitation bubble dynamics, and microphysical interactions of droplets with airborne particulates. PhD in Mechanical Engineering, Utah State University (2018) BS in Mechanical Engineering, Brigham Young University (2015) His research spans: High-speed imaging of water entry and cavitation phenomena Hydroelastic effects on cavity formation Droplet-particulate interactions in atmospheric flows Acceleration-induced fluid instabilities Recent publications focus on water entry dynamics, cavitation control, and fluid-structure interactions, with applications in naval engineering and environmental science. Trends include experimental validation of theoretical models and visualization of transient fluid behaviors. Scientific honors include the American Physical Society Division of Fluid Dynamics Gallery of Fluid Motion Award (2023). Teaching interests emphasize fluid dynamics and engineering measurement techniques. Professional roles include ad hoc peer review for the Journal of Fluids and Structures , Journal of Fluid Mechanics , National Science Foundation grant proposals, and American Physical Society conferences.
Jeremy Teitelbaum is a Professor in the Department of Mathematics at the University of Connecticut within the College of Liberal Arts and Sciences. He serves as Director of UConn's interdisciplinary Masters Program in Data Science, a one-year professional degree program. His academic career spans both pure mathematics and data science applications. Teitelbaum's research bridges classical algebraic number theory and modern machine learning. Initially focused on p-adic geometry, elliptic curves, modular forms, and p-adic L-functions , his work evolved significantly toward machine learning and data science . Current interests include bioinformatics, unsupervised learning (particularly clustering), and mathematical foundations of machine learning. He maintains active GitHub repositories documenting his computational work and lecture materials. His publication trends reveal a transition from pure number theory (2000s) toward machine learning applications (2020s), with consistent mathematical rigor throughout. Keywords across his work include algebraic geometry, representation theory, p-adic analysis, and statistical learning theory, reflecting both his foundational expertise and contemporary applications. Teitelbaum has held significant administrative roles including Dean of the College of Liberal Arts and Sciences (2008-2017) and interim Provost (2017-2018). He is a Certified Instructor for The Software Carpentry and develops extensive online educational materials, including complete video lecture series for Abstract Algebra and Transition to Higher Mathematics based on open-source textbooks. His teaching portfolio includes graduate courses like Fundamentals of Data Science (Grad 5100) and Mathematics of Machine Learning (Math 3094), alongside core mathematics courses such as Abstract Algebra and Linear Algebra. He maintains specialized interests in mathematical visualization tools, including Bokeh library applications and linear algebra pedagogy tools.
Prof. Jeanette Hoffmann holds a full professorship in the Didactics of German Literature at the Faculty of Educational Sciences , Free University of Bozen-Bolzano. Her academic journey includes studies in German, mathematics, and educational science at the University of Münster, followed by research roles at the Free University of Berlin and Technical University Dresden. She earned her doctorate with an award-winning study on intercultural literary dialogues and completed teaching qualifications in Montessori education. Previously a professor in Dresden, she now leads educational initiatives focusing on multilingualism, graphic storytelling, and intercultural learning. Education & Professional Path: Studied at University of Münster (German, Mathematics, Protestant Theology, Educational Science) Doctoral research at Free University Berlin (awarded for study on literary conversations) Second State Examination at Montessori elementary school Berlin Professor positions at Technical University Dresden and University of Teacher Education Upper Austria Research Interests: Focuses on children's literature didactics , graphic storytelling , multilingual education , and intercultural learning . Her work bridges empirical research with practical pedagogy, emphasizing dialogue-based learning and innovative classroom methodologies. Awards & Recognition: Double award for her doctoral thesis (2011) 2024 Prize for Excellent Doctoral Supervision by the Symposium on German Didactics Lab & Teaching Activities: Directs the KinderLiteraturWerkstatt (ChiLiLab), a research lab exploring literary aesthetics through children's books. Engages in projects like the IMAGO initiative promoting multilingual storytelling in South Tyrol. Teaches courses on literary learning, media socialization, and early literacy pedagogy.
Dr Andrew Starkey is a Reader in the School of Engineering at the University of Aberdeen, where he also completed his PhD in 2001. He holds an Honours degree in Applied Mathematics from the University of St Andrews. He is actively involved in research and currently accepting PhD students in Engineering. His work bridges academia and industry, with a focus on AI applications in engineering, bioinformatics, and geosciences. University: University of Aberdeen School: School of Engineering Academic Rank: Reader Email: a.starkey@abdn.ac.uk Phone: +44 (0)1224 272801 Dr Starkey's research centers on Explainable AI (XAI) , Green AI , and Autonomous AI , with applications in robotics, econometrics, bioinformatics, seismic data analysis, and virtual reality. He has developed novel methods for feature selection, autonomous learning, and knowledge abstraction from agent-environment interactions. His work emphasizes low computational cost and transparency in AI systems. The most recent publications reflect a strong trend in applying AI to complex real-world problems, including digital rock technology, robotic grasping, real-time event detection, and medical data analysis. His interdisciplinary research combines machine learning with domain-specific knowledge in engineering and life sciences, often resulting in practical, industry-ready solutions. Millennium Product Award John Logie Baird Award for Innovation Enterprise Fellowship from Royal Society of Edinburgh and Scottish Enterprise Dr Starkey has supervised multiple research projects and secured funding from major bodies including EPSRC, BBSRC, and industry partners. His past work on the GRANIT project led to the development of AI-based condition monitoring for ground anchorages, resulting in commercialization through BlueFlow Ltd. He has collaborated with researchers across disciplines, including Dr Alasdair MacKenzie (bioinformatics), Dr Anne Schwab (seismic analysis), and Dr David Hazlerigg (genomics). He leads research in AI-driven engineering solutions and is the CEO of BlueFlow Ltd, a spinout company commercializing AI technologies developed at the University of Aberdeen. His lab focuses on developing autonomous, explainable, and environmentally sustainable AI systems for real-world deployment.
G. Caltais is an Assistant Professor in the Formal Methods and Tools (FMT) group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Previously, they worked as an independent junior scientist at the University of Konstanz (Chair for Software and Systems Engineering) and as a post-doctoral researcher at ETH Zürich (Chair of Software Engineering). They hold a PhD from Reykjavík University and Radboud University. Research Focus: Formal modeling of computer systems, emphasizing automata theory, concurrency, causal/knowledge models, and software-defined networks (SDNs). Current projects include DyNetKAT (formal SDN analysis), Zorro (zero downtime knowledge models), and exploring cyclic structures in software correctness. Committees: Served on program committees for CALCO 2023, ESOP 2023, FORTE 2022-2023, FSEN 2023-2025, IEEE NFV-SDN 2020-2022, SPIN 2022-2024, and others. Organized events like CREST@ETAPS 2019/2023 and EXPRESS/SOS 2023. Student Projects: Offers B.Sc./thesis proposals on DyNetKAT visualization, causal analysis of SDN safety violations, and learning formal network models from real datasets. Contact for custom project ideas.
Vladimir Filkov is a Professor in the Department of Computer Science at the University of California, Davis, College of Engineering. He leads two research labs: the DECAL Lab and the AI for Health Lab. He is actively engaged in research, teaching, and service, with a focus on open-source software sustainability, AI in healthcare, and data science. He has held leadership roles such as General Chair of ASE 2024 and inaugural Director of Translational Data Science at UCD DataLab. Professor, Department of Computer Science, UC Davis Director, DECAL Lab Director, AI for Health Lab General Chair, ASE 2024 Director of Translational Data Science, UCD DataLab (2020–2024) His research centers on the sustainability of open-source software, using socio-technical and governance data to forecast project success and evolution. He also investigates AI applications in health, particularly multimodal models for atrial fibrillation and NLP in medicine. His work bridges empirical software engineering, data science, and healthcare informatics, with strong community engagement through forums and podcasts. He has led major NSF, Google, and Sloan Foundation-funded projects on OSS sustainability and UC-wide OSPO initiatives. The recent publications highlight a strong trend in empirical software engineering, particularly around open-source governance, lifecycle analysis, and sustainability forecasting. There is also a growing emphasis on health-related AI, including multimodal models for cardiac conditions and natural language processing in clinical settings. The work combines data-driven modeling with real-world impact in both software ecosystems and healthcare. ACM Distinguished Member ACM SIGSOFT Distinguished Paper Award Vladimir Filkov has successfully secured competitive grants from the NSF (GCR, Phase I and II), Google, and the Sloan Foundation. He advises PhD students including Likang Yin, Raiyan Jahangir, and postdoc Stefan Stanciulescu. His mentoring spans topics in software engineering, AI, and computational biology. He has organized major research forums and collaborative initiatives across the UC system. He leads the DECAL Lab and the AI for Health Lab at UC Davis, fostering interdisciplinary research in software sustainability and healthcare AI. These labs support graduate students, postdocs, and collaborative projects with national and international partners.
Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.
Giles Reger is a Senior Lecturer in the Formal Methods Group of the School of Computer Science at the University of Manchester. He completed his BA in Computer Science at the University of Cambridge in 2009, followed by an MSc in Advanced Computer Science at the University of Manchester in 2010 (awarded Highest Achiever of the Year), and earned his PhD from the University of Manchester in 2014 with a thesis titled "Automata based monitoring and mining of execution traces". His research spans several key areas within computer science: Automated Theorem Proving (first-order) Saturation-based techniques Reasoning with theories and quantifiers Finite Model finding Collaborative and Concurrent proof attempts Runtime Monitoring/Verification Temporal specification languages Specification Mining/Inference Dr. Reger leads multiple EPSRC-funded research projects including SCorCH (Secure Code for Capability Hardware), CAPS (Collaborative Architectures for Proof Search), and QuTie (reasoning with Quantifiers and Theories). His work on the Vampire theorem prover and MarQ monitoring tool demonstrates his bridge between theoretical computer science and practical applications. Recent publications show strong focus on runtime verification, theorem proving, and program analysis with applications to security and performance monitoring. Notable awards: Highest Achiever of the Year Award for MSc studies Dr. Reger collaborates extensively with institutions including the University of Oxford, Arm, Amazon Web Services, and CERN (CMS Experiment). As Manchester lead on the SCorCH project, he develops formal analysis tools for security-aware hardware chips. His work on the VyPR framework enables developers to analyze Python program performance through temporal specification languages and monitoring algorithms.
Catherine Hurley is a Professor of Statistics at Maynooth University's Faculty of Science & Engineering, where she serves as Subject Head of Statistics in the Department of Mathematics and Statistics. She maintains strong affiliations with both the MU Hamilton Institute and the National Centre for Geocomputation, positioning her work at the intersection of statistics, data science, and computational methods. Dr. Hurley is a leading expert in data visualization with primary research interests in visualization techniques for data science and machine learning problems. Over her distinguished career, she has authored and contributed to numerous software packages, beginning with Data Viewer (1987), a predecessor to GGobi, Quail (1987-2000), and many R packages including condvis2, vivid (2021), and Bartvis (2022). Her work has significantly advanced the field of statistical graphics and model visualization. Her recent research has focused on conditional visualization for statistical models, with several publications on the condvis package and related tools that enable researchers to explore complex machine learning models through interactive visual interfaces. She has also made substantial contributions to dendrogram seriation, pairwise comparison visualization, and variable importance displays, creating numerous R packages that have become essential tools for statisticians and data scientists. Her 2023 publications include significant contributions to Bayesian additive regression trees and variable importance visualization for machine learning models. Dr. Hurley has held significant leadership roles in the statistical community, serving as Vice-President (2017-2019) and President (2019-2021) of the Irish Statistical Association. She also served as Editor-in-Chief and Editor of the R Journal from 2019 to 2023, playing a crucial role in advancing open-source statistical software development and dissemination. Her work demonstrates a consistent pattern of developing practical visualization tools that address real-world challenges in statistical analysis and machine learning interpretation. The progression from early work on statistical graphics infrastructure to recent innovations in model exploration reflects her sustained commitment to making complex statistical concepts accessible through visualization.
Corey Brady is an Associate Professor at the Department of Teaching & Learning and Associate Dean for Research and Outreach at Southern Methodist University's Simmons School of Education and Human Development. He holds a Ph.D. in Mathematics Education from the University of Massachusetts, Dartmouth, alongside degrees in English Literature (MA) and Pure Mathematics (MS). Dr. Brady's research focuses on mathematical and computational modeling from a constructionist perspective, emphasizing collective learning and STEAM activity . His work includes design-based research on block-based programming environments and embodied participatory simulations. Recent publications (2023-2021) span journals like Frontiers in Education , Science Education , and Educational Studies in Mathematics , with sub-fields ranging from geometric transformations to disaster preparedness systems. He has received recognition including the Outstanding Paper Award at ICLS 2023 and Best Paper at ICLS 2020 . Previously, he held faculty roles at Vanderbilt and Northwestern Universities, led educational technology development at Texas Instruments, and taught at middle school to community college levels. His interdisciplinary approach bridges mathematics, computer science, and affective learning.
Prof. Dr. Annette Upmeier zu Belzen is a Professor at the Institute of Biology at Humboldt University of Berlin, where she has been serving since October 2005. She leads the working group on Biology Didactics and Teaching/Learning Research within the Department of Biology Didactics. Her academic work is situated in the Faculty of Mathematics and Natural Sciences at one of Germany's most prestigious research universities. Her educational background includes: Promotion Dr. paed. from the Institute for Biology Education at Westfälische Wilhelms-University Münster (1997) Master of Arts in School Management from Technical University of Kaiserslautern (2005) Studies in biology, education and psychology at the University of Münster, culminating in the First State Examination (1992) Prof. Upmeier zu Belzen's research primarily focuses on biology education, with special emphasis on models and modeling in science education. Her work explores how students develop scientific reasoning skills through model-based learning approaches. She investigates the cognitive processes involved in understanding biological phenomena through modeling activities and examines how these processes can be effectively supported in classroom settings. Her research has significant implications for science teacher education, as she develops frameworks for assessing and fostering model competence among pre-service teachers. Through her work, she bridges theoretical perspectives on scientific modeling with practical classroom applications, contributing to both educational theory and practice in science education. Her recent publications reveal a strong focus on model-based learning, scientific reasoning, and teacher education. She has been increasingly investigating the role of abductive reasoning in modeling biological phenomena as complex systems. Her work spans formal classroom settings, museum education environments, and teacher professional development contexts. A notable trend in her recent work is the integration of cognitive science perspectives with educational research to better understand how students and teachers engage with scientific models and practices. Prof. Upmeier zu Belzen holds several significant professional roles: Deputy spokesperson of the Interdisciplinary Center ProMINT-Kolleg Scientific Director of Humboldt Explorers Editor of Science Education Review Letters (SERL) Member of the Council of the Humboldt University of Berlin She is actively involved in educational policy and standards development, serving as a consultant for various national and international organizations including the Organisation for Economic Co-operation and Development (OECD) for the PISA Science Framework 2025. Her expertise is sought after for developing educational standards and frameworks across multiple German educational institutions. Prof. Upmeier zu Belzen leads and participates in several research teams and laboratories focused on science education. Her work with the Humboldt Explorers program creates innovative learning environments, while her involvement with the Interdisciplinary Center ProMINT-Kolleg supports STEM education initiatives across Berlin and Brandenburg.
Maureen A. Sartor is a Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan Medical School, with a joint appointment in Biostatistics at the School of Public Health. She serves as Co-Director of the Bioinformatics Graduate Program and leads an active research laboratory focused on computational biology. Education: PhD in Biostatistics, University of Cincinnati (2007) MS in Biomathematics, North Carolina State University (2000) BS in Mathematics with minors in Biology and Computer Science, Xavier University (1998) Research Focus: Dr. Sartor develops bioinformatics methods for analyzing high-throughput regulatory and epigenomic data. Her primary research examines cancer epigenomics and biomarker discovery in oral squamous cell carcinomas, with additional projects on ALS pathogenesis and computational methods for predicting chemical exposure-gene interactions. Her lab specializes in multi-omics analyses and tool development for genomic data interpretation. Publication Trends: Her recent work (2016-2022) demonstrates strong emphasis on HPV-related head/neck cancer biology, epigenetic regulation mechanisms (DNA methylation/hydroxymethylation), and development of bioinformatics tools for genomic region annotation and enrichment analysis. Publications consistently integrate computational method development with translational cancer research applications. Student Advising: Actively mentors graduate students across bioinformatics, biostatistics, and cancer biology programs. Current advisees include 8 PhD candidates and 1 MS student. Previously advised 6 PhD graduates now working in academia and industry. Laboratory Leadership: Directs the Sartor Lab emphasizing collaborative science and Michigan Medicine's core values (Caring, Integrity, Teamwork, Innovation). Research activities focus on cancer epigenomics, HPV oncology, and bioinformatics tool development for the research community.
Daphna Harel (she/her) serves as Associate Professor of Applied Statistics and Director of the A3SR MS Program within the Department of Applied Statistics, Social Science, and Humanities at New York University's Steinhardt School of Culture, Education, and Human Development. She holds leadership roles including PI of the NYU QUEER data lab and membership on the steering committee for the DEPRESSD project. Education: PhD in Mathematics and Statistics from McGill University Harel's research focuses on measurement challenges in survey methodology, particularly for self-reported questionnaires in health and social sciences. Her work bridges theoretical statistics with practical applications in LGBTQIA+ data collection, differential item functioning, and patient-reported outcomes. She develops methodological frameworks for polytomous Item Response Theory and creates guidelines for statistical analysis of complex survey data. Her recent work emphasizes improving statistical practice for LGBTQIA+ populations and advancing queer data collection methods. Her publication portfolio demonstrates consistent focus on measurement validity across diverse health contexts, with recent work expanding into LGBTQIA+ data science. Her research shows increasing emphasis on methodological innovations for gender and sexuality measurement, with multiple publications presented at major conferences including AAPOR 2024 and LGBTQ+ Health Conference 2024. Research Leadership: PI of NYU QUEER data lab (founded Fall 2022) Steering committee member for DEPRESSD project Collaborator with Scleroderma Patient-centered Intervention Network Recipient of NIH R21 funding and NYU intramural grants Harel mentors a diverse team of graduate students through the Applied Statistics for Social Science Research program, focusing on inclusive research methods for marginalized populations. Her grant portfolio includes investigations into transgender voice training, LGBTQIA+ survey methodology, and healthcare access for gender-affirming services. Laboratory Leadership: As founder and PI of the NYU QUEER data lab, Harel leads research projects examining survey question design for gender/sexuality measurement, content moderation effects on hate speech, and accessibility of transgender voice training. The lab operates through collaboration between NYU Steinhardt and the NYU BITS lab, with research assistants drawn from the Applied Statistics MS program.
Darius Plikynas is a Senior Researcher at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University . His research integrates computational intelligence methods with agent-based simulation to model cognitive and social processes. Position: Senior Researcher, Chief Researcher in the Project Address: Akademijos St. 4, room 224, Vilnius Contact: +370 5 210 9333, +370 620 95101 Email: darius.plikynas@mif.vu.lt Personal page: http://www.dariusplikynas.eu Dr. Plikynas' research spans interdisciplinary domains including neuroscience, physics methods, complexity theory, and distributed cognitive systems. He led the 2017–2019 project "Development of a metric, conceptual and simulation model of the social impact of cultural processes" under the LMT Research Group Funding Program. His recent publications (2016–2025) reflect trends in combining machine learning with social science questions (fake news analysis, propaganda detection) and agent-based modeling of cultural/social capital dynamics. Key collaborations include Leonidas Sakalauskas, Rimvydas Laužikas, and Arunas Miliauskas. Scientific supervision includes doctoral students: Andrius Budrionis (University of Tromsø, Norway) Ieva Rizgelienė (PhD topic: "Propaganda detection and classification in social media using hybrid deep learning") He also serves as an expert at Vilnius University and has contributed to projects involving: 2D financial market visualization Neural oscillation-based cognitive modeling Indoor navigation for blind individuals Cultural participation impact on social capital
