Dr. Stephen Rice is a researcher at Newcastle University specializing in health economics and systematic reviews , with a focus on clinical cost-effectiveness and medical interventions. His collaborative work frequently engages with the National Institute for Health and Care Excellence (NICE) appraisals, evaluating therapies for conditions like non-small cell lung cancer , health-care-associated infections , and diabetic retinopathy screening . Research Interests center on cost-effectiveness analysis , public health economics , and systematic review methodologies . His publications often address rare diseases, chronic conditions like Primary Biliary Cirrhosis , and health policy frameworks. Key Contributions include co-authoring high-impact evaluations for NICE Single Technology Appraisals, analyzing nutritional interventions for elderly populations, and exploring outcomes in malignant biliary obstruction treatments. His work emphasizes evidence-based policy and resource optimization in healthcare settings.
Jaakko Hollmen serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, affiliated with the Helsinki Institute for Information Technology (HIIT) and the Computer Science Lecturers research group. His interdisciplinary work bridges machine learning with critical applications in healthcare, transportation systems, and environmental science. His research focuses on advanced machine learning methodologies including Bayesian optimization, random forests, and principal component analysis. Key application areas span neonatal healthcare (mortality prediction, brain injury analysis), transportation modeling (activity-based model calibration), and environmental data science (weather-crop relationships, drug-environment interactions). His approach emphasizes practical implementations of complex algorithms for real-world problems. Hollmen's recent publications (2018-2023) reveal a consistent trajectory in developing machine learning solutions for high-dimensional data challenges, with increasing emphasis on medical applications. His work demonstrates strong cross-domain collaboration, particularly with medical researchers at Helsinki University Hospital. His notable recognition includes: Best paper finalist and runner-up award (Computer Track) at the first IEEE Life Sciences Conference (LSC) for research on predicting complications in very low birth weight infants (2017) As a core member of HIIT, Hollmen contributes to Finland's national information technology research infrastructure while maintaining active collaborations with medical and agricultural research groups. His current projects focus on optimizing transportation models and advancing clinical prediction systems through novel machine learning techniques.
Lonneke Boels is a PhD candidate at Utrecht University's Freudenthal Institute, specializing in Mathematics Education and Statistics Education . While primarily affiliated with the Faculty of Science, she teaches mathematics at the Christelijk Lyceum Delft secondary school. Her research focuses on improving statistical literacy through graphical interpretation, particularly histograms, using eye-tracking methodology as part of the Dynamics of Youth research theme. Education : MSc in Mathematics (Educational focus) Methodology : Eye-tracking analysis, didactic design Key Publications : 2021: Polygon learning with gamified software 2019: Teacher strategies with histograms 2018: Machine learning analysis of student gaze patterns Scientific Recognition : Awarded a prestigious NWO doctoral grant for teachers in 2016. Active in academic discourse through invited presentations at ICOTS, PME, and CERME conferences. 2021: International Congress on Mathematical Education 2020: EARLI SIG 27 conference 2019: PME and CERME conferences 2018: ICOTS10 keynote Professional Contributions : Regular contributor to Euclides journal since 2011, covering digital math games, educational websites, and practical didactic problems in secondary school mathematics.
Dr. Cherie Lucas is a Nexus Fellow at the University of New South Wales (UNSW) , affiliated with the Faculty of Medicine and Health . With over 24 years of academic teaching experience across University of Sydney (USYD) , University of Technology Sydney (UTS) , and UNSW, she specializes in pharmacy education , interprofessional collaboration , and artificial intelligence in health education . As a registered pharmacist with 34 years of clinical experience, her work bridges practical healthcare and pedagogical innovation. PhD in Pharmacy Education , University of Sydney Graduate Certificate in Educational Studies , University of Sydney Bachelor of Pharmacy , University of Sydney Her research focuses on reflective practice , curriculum design , and AI-driven educational tools , exemplified by her work on the RIPE-N model for interprofessional education and the WRAP Toolkit for Aboriginal cultural competence. She has published over 90 manuscripts, with a strong emphasis on formative feedback systems like AcaWriter and immersive virtual reality applications. 2024 Fellow of the Pharmaceutical Society of Australia (FPS) 2024 Senior Fellow in Higher Education (SFHEA, UK) 2019 Australian Awards for University Teaching (AAUT) Citation 2017 Best Full Paper Award (LAK17 Conference, Canada) Dr. Lucas co-supervises PhD candidates, such as Ms. Carley Jans in health education in the Metaverse . She holds editorial roles with journals like Exploratory Research in Clinical and Social Pharmacy and serves on the Pharmacy Council of NSW as Deputy President. Her work is supported by grants, accessible via her ORCID profile.
Dabao Zhang is a Professor at the University of California, Irvine School of Public Health in the Department of Epidemiology & Biostatistics. His research focuses on statistical methodology for high-dimensional data, causal inference, and computational biology applications. Ph.D. in Statistics (Cornell University, 2003) M.Sc. in Probability & Statistics (Peking University, 1993) B.Sc. in Mathematical Statistics (Nankai University, 1990) His research interests include: Construction of large causal systems and big data visualization Statistical genetics and omics data integration Gene-environment interactions and survival analysis AI explainability and generative models for text analysis Recent research projects involve: Developing exploratory tools for big data relational structures Computational algorithms for biological causality inference Novel measures for AI model explainability Scientific awards : National Science Foundation CAREER Award (2009) Purdue University Seed for Success Awards (2011, 2020) Purdue College of Science Outstanding Service Award (2023) His methodological contributions span generalized linear models, gene network analysis, and metabolomic profiling, with applications in cancer research, plant genetics, and neurodegenerative diseases.
Heike Hofmann is a Professor at the University of Nebraska-Lincoln. Her work focuses on data visualization, statistical computing, and forensic statistics, with a strong emphasis on developing open-source software tools for data exploration in R. She actively contributes to academic research and software development. Current Institution: University of Nebraska-Lincoln Email: hofmann@iastate.edu Her research spans topics such as forensic toolmark analysis , machine learning explainability , and interactive visualization frameworks . She designs algorithms for bullet comparison and error rate assessment in forensic science, leveraging 3D imaging and statistical diagnostics . Recent publications highlight her work on human factors in visual inference , automated forensic matching , and reproducible pipelines for functional data. Notably, she creates tools like the cmcR package for bullet land scan comparison and nullabor for statistical significance calculations. Heike Hofmann contributes to software development with R packages including ggparallel (parallel coordinate plots), gglogo (sequence logos), and rotations (3D rotation analysis). Her GitHub activity shows consistent commits to statistical computing projects.
Florina Piroi is a Senior Scientist (Research Fellow) at the Center for Research Data Management (E058-06) within the Faculty of Informatics at TU Wien. Her research integrates information retrieval, knowledge graphs, and semantic technologies to advance exploratory search systems and research data infrastructure. Her core research interests include: Information Retrieval evaluation and validation methodologies Knowledge graph applications for domain-specific search Patent text mining and semantic technologies Medical concept normalization and entity linking Argumentative zoning in scientific documents Table extraction and classification systems Analysis of her recent publications reveals strong trends in applying machine learning to specialized IR tasks, particularly in patent analysis and medical text processing. Her work emphasizes robust evaluation frameworks and cross-lingual capabilities, with consistent contributions to the CLEF campaign and PatentSemTech workshops. Dr. Piroi actively collaborates with international researchers through the Network Lab at TU Wien and contributes to the university's Center for Research Data Management, focusing on semantic technologies for research data infrastructure and scholarly communication systems.
Bao Michael Uyen is an Adjunct Professor at the University of Ottawa Faculty of Engineering (Department of Mechanical Engineering) and a Senior Scientist at Defence Research and Development Canada (DRDC) , a role he has held since 1993. He is also co-chair of NATO Modelling Simulation Group 186, leading research on multidimensional data farming. His affiliations span NORAD, US Space Command, NATO Centre for Maritime Research & Experimentation, and DRDC Atlantic. Education: PhD in Theoretical Particle Physics, McGill University (1993) BSc in Physics (summa cum laude), University of Ottawa (1988) BSc in Mathematics (summa cum laude), University of Ottawa (1988) Undergraduate studies at Caltech (2 years) Research Interests: Dr. Uyen's research lies at the intersection of complex systems, autonomous agents, and defense applications. He investigates how autonomous vehicles, human operators, and AI systems interact in high-stakes environments. His work spans robotics, AI, cybersecurity, machine learning, combinatorial optimization, stochastic processes, and discrete mathematics. These disciplines are applied to mine countermeasures, ballistic missile defense, multi-domain operations, and strategic deterrence. Research Trends: His recent publications reflect a strong focus on multi-domain defense modeling , AI-enhanced simulation , and autonomous systems . Themes include quantum game theory in deterrence, probabilistic modeling for mine detection, and data farming for strategic decision support. These works are often collaborative and NATO-linked, emphasizing real-world defense applications. Scientific Awards & Honors: Koopman Prize 2014 – Outstanding Military Operations Research (INFORMS) Third Prize, CORS Practice Competition 2006 – AUV Mine Countermeasures Deputy Minister Commendation Award – NORAD Sustainability Special Merit, CORS Practice Competition 2000 – Search & Rescue Modeling Gold Medal, University of Ottawa 1988 NSERC Postdoctoral Fellowship 1993–1995 Multiple NSERC and Ontario Graduate Scholarships Advising & Grants: Dr. Uyen has mentored numerous graduate students who now hold positions at institutions like Yale, IBM, and the University of Manitoba. He has secured over $6.7 million CAD in defense research funding, including a $5.6M collaborative project with uOttawa, UNB, and NRC focused on autonomous agent coordination. Labs & Teams: He leads research under the NATO Modelling Simulation Group 186 and collaborates with DRDC teams across Canada and internationally. His work integrates defense simulation, robotics, and AI, often involving interdisciplinary teams from academia, military, and government agencies.
Daniel Amyot is a Professor at the School of Electrical Engineering and Computer Science , University of Ottawa , with research spanning Requirements Engineering, Business Process Modeling, Regulatory Compliance, Healthcare Informatics, and Smart Contracts. He is affiliated with the LIFE Research Institute , Institut du Savoir Montfort , and the IBM/Telfer Centre for Business Analytics and Performance . Degrees: Ph.D., Computer Science, University of Ottawa (2001) M.Sc., Computer Science, University of Ottawa (1994) B.Sc., Informatique de génie, Université Laval (1992) Research Interests focus on modeling methodologies like the User Requirements Notation (URN) standard and its jUCMNav tool, integrating BPM with AI for Process Mining, and applying these in healthcare and FinTech contexts. Recent work explores Smart Contracts for transactive energy systems and regulatory intelligence using LLMs. Recent Trends across 15 articles highlight LLM applications in requirements classification, Smart Contract formalization for compliance, Process Mining in governmental services, and interdisciplinary collaborations in Legal Informatics and Healthcare. Key tools developed include Symboleo for contract specification and jUCMNav for URN modeling. Scientific Contributions include editorial roles at Requirements Engineering Journal and Software and Systems Modeling , leadership in the Requirements Engineering Conference (General Chair 2015, Program Co-Chair 2018), and IEEE Senior Membership. Labs and Teams include the Contract Specification and Monitoring Lab and affiliations with the Telfer Health Transformation Exchange and SDL Forum Society (former Chair). His work bridges academic rigor with industrial applications, particularly in Canada’s healthcare and energy sectors.
Dr. Martin Dröge is a Researcher at Humboldt University Berlin since 2021, affiliated with the Chair of Digital History. He previously worked as a research assistant at the University of Paderborn (2012-2021) and Paderborn's AI-SKILLS project. His academic career spans roles at the LWL Institute for Westphalian Regional History and the Freiherr-vom-Stein Society. Education: Magister Artium in modern and contemporary history, media studies, and educational science from University of Paderborn Research focuses on text mining , data visualization , gender history , and Nazi Germany studies . His publications blend digital methodologies with regional historical analysis, including monographs on Karl Friedrich Kolbow and Nazi masculinity ideologies. Recent work includes a 2022 interactive Python textbook for historians and exploratory text analysis of Prussian governance. His 2015 monograph on Kolbow received critical acclaim from esteemed journals. Scientific Award: Ignaz-Theodor-Liborius-Meyer Prize (2017) for regional history contributions
Angel Sanchez Barbie serves as a Professor at Miguel Hernández University, affiliated with the Department of Statistics, Mathematics and Computer Science and the Operational Research Center Institute (Instituto Centro de Investigación Operativa). His research focuses on Statistics and Operational Research, with specialized expertise in Computational Statistics, Econometrics, and Exploratory Data Analysis. These areas are directly reflected in his teaching across diverse programs including Business Administration, Law, Sports Science, and the Master's in Computational Statistics and Data Science for Decision Making. He actively contributes to the university's research ecosystem through membership in the Computational Statistics Research Group and the Operational Research Center Institute, where his work bridges theoretical statistics with practical applications in economic and sports analytics.
Esteban Cabello Garcia serves as an Adjunct Professor at Miguel Hernández University within the Department of Statistics, Mathematics and Computer Science. He holds the position of Official Postgraduate Program Delegate for the first year in the Doctoral Program in Statistics, Optimization and Applied Mathematics, with institutional affiliation at the Center for Operational Research in Elche, Alicante. His research focuses on applied statistical methodologies across multiple domains including Data Science, Machine Learning, and specialized quantitative fields. Primary expertise encompasses Survey Statistics (particularly Small Area Estimation), Econometrics, and Actuarial Science applications, with emphasis on computational techniques for decision-making processes and exploratory data analysis. Teaching responsibilities span undergraduate, master's, and doctoral programs, covering courses such as Statistical Techniques for Learning, Exploratory Data Analysis, and Actuarial Techniques in Insurance across business, law, and data science curricula.
Prof. Dr. Stefan Harmeling is a Distinguished Professor at TU Dortmund University 's Faculty of Computer Science and leads the Harmeling Lab . Previously, he served as Professor for Machine Learning at Universität Düsseldorf (2014-2022) and held postdoctoral positions at the University of Edinburgh (2005-2007) and Max Planck Institutes in Tübingen (2007-2014). His research spans machine learning , deep learning , reinforcement learning , and causality , with applications in medical imaging , seismology , and political discourse analysis . Education : Dipl. Math. in Mathematical Logic, University of Münster (1998) M.Sc. in Computer Science, Stanford University (2000) Dr. rer. nat. in Machine Learning, University of Potsdam (2005) His scientific contributions include 15 recent publications (2021-2025) covering topics from cardiac MRI analysis to LLM applications in political deliberation . These works demonstrate expertise in generative models , self-supervised learning , and adversarial robustness . Notable awards : 2022: Helsinki Tomography Challenge winner 2012: Günter Petzow Prize 2011: DAGM Prize 2009: COSMOSTAT Best Contribution 2009: GREAT08 Challenge Top Score As an advisor, he has mentored 8 PhD students including Tobias Uelwer (2023 PhD), Sebastian Konietzny , and Maike Behrendt . His teaching portfolio includes courses on probabilistic reasoning, machine learning, and deep learning at TU Dortmund. He also serves as area chair for TinyPapers@ICLR 2024 and organizer for multiple NeurIPS/ICML workshops.
Mikko Airavaara is a Professor at the Department of Pharmacology and Drug Therapy within the University of Helsinki's Institute of Sustainability Science (HELSUS). He serves as a supervisor in the Doctoral Programme in Brain & Mind and Drug Research, focusing on neuropharmacology, gene therapy, and personalized medicine. Academic Rank: Professor University: University of Helsinki School: Institute of Sustainability Science (HELSUS) Department: Department of Pharmacology and Drug Therapy Research Interests: His work spans Pharmacy , Neurosciences , and Drug Research , with recent studies on gene replacement strategies for neurodegenerative diseases, GDNF signaling in behavioral neuroscience, and proteasome activation for Parkinson's therapy. He also investigates mitochondrial dysfunction and drug interactions in stroke and metabolic disorders. Scientific Awards: Innoopeli Prize (2022): Recognizing innovative research in drug development. Grants & Projects: Currently leads projects funded by the Academy of Finland (PROFI program), Finnish Parkinson Foundation, and Juselius Foundation, focusing on neurodegenerative therapies and transporter pharmacogenomics.
Dr. Filip Szczypinski is a Royal Society University Research Fellow and Assistant Professor of Chemistry Automation at Durham University, affiliated with the Department of Chemistry. His research integrates computational modeling, laboratory automation, and data-driven approaches to advance supramolecular chemistry and materials discovery. PhD in Chemistry from the University of Cambridge Postdoctoral research at Imperial College London and University of Liverpool His work focuses on digital supramolecular chemistry , combining: Dynamic covalent chemistry Molecular recognition systems Automated synthesis platforms Recent research explores: Explainable AI for molecular design Polymorphism prediction in organic cages Autonomous robotics for chemical discovery Key trends in his publications include computational modeling of synthetic feasibility, AI-driven molecular assembly, and sustainable chemical processes. Scientific Awards : Royal Society University Research Fellow His lab bridges computational design with robotic experimentation , aiming to transform supramolecular chemistry into a predictive discipline aligned with UN Sustainable Development Goals.
