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Beatrice AlexView profile

Senior Lecturer

• Text Mining
• Natural Language Processing
• Computational Linguistics
• +12 more

Dr. Beatrice Alex is a Senior Lecturer and Chancellor's Fellow at the University of Edinburgh, jointly affiliated with the Edinburgh Futures Institute (EFI) , the School of Literatures, Languages and Cultures , and the School of Informatics . She is also a Turing Fellow at The Alan Turing Institute . Her work bridges computational linguistics, healthcare informatics, and digital humanities. She leads the Edinburgh Language Technology Group (LTG) and the Edinburgh Clinical NLP Group , focusing on text mining and natural language processing applications. Her educational background includes a PhD in Computational Linguistics from the University of Edinburgh (2008), an MSc and Euromasters in Speech Processing (2002), and a BA in Languages, Translation and Interpreting from Heriot-Watt University. Dr. Alex’s research centers on text mining and NLP for extracting information from raw text and speech transcripts. Her work spans healthcare (e.g., predicting multimorbidity, clinical NLP for radiology), digital humanities (e.g., analyzing literary texts, heritage metadata), and social science (e.g., neighborhood effects on health). She develops tools for automated metadata generation, bias detection in archival descriptions, and phenotyping from brain imaging reports. Her recent publications (2024–2025) reflect a strong trend toward interdisciplinary research, combining NLP with public health, gender studies, and heritage science. Key themes include topic modeling of local newspapers for health insights, gender bias detection in catalogues, and automated neuroradiological support systems . The work often involves collaboration across computer science, medicine, and the humanities. Honorable Mention - CHI 2025 best papers Turing Fellowship (awarded 2016 and 2018) Dr. Alex is actively involved in PhD supervision and teaches courses such as Text Mining for Social Research and Narrative and Computational Text Analysis . She has served as PI and Co-I on numerous research projects, including the Advanced Care Research Centre and AIM-CISC . She co-chaired the HealTAC 2021 conference and serves on the editorial board of Frontiers in Digital Health and the Journal of Open Humanities Data . She is also co-convener of the Data Science and Digital Humanities special interest group at The Alan Turing Institute. She leads and participates in several research groups and initiatives: Edinburgh Language Technology Group (LTG) – cross-college NLP R&D Edinburgh Clinical NLP Group – healthcare applications Scottish Gaelic Algorithmic Research Group – humanities text mining Lothian Diary Project – pandemic experience and community narratives Palimpsest Project – literary Edinburgh mapping She is a core developer of the Edinburgh Geoparser , contributing to spatial humanities research.

Thor Einar Gjerstad AndersenView profile

Professor

• Sports Injury Prevention
• Football Medicine
• Sports Physiology
• +5 more

Thor Einar Gjerstad Andersen is a Professor at the Oslo Sports Trauma Research Center, Department of Sports Medicine at the Norwegian School of Sport Sciences. He serves as the Medical Director of the Norwegian FA's Sports Medical Center (Idrettens helsesenter) and as Chief Medical Officer of the Football Association of Norway. With a background as a Physical Medicine and Rehabilitation specialist and authorized Sports Medicine & Exercise Physician, he brings extensive clinical experience to his academic role. Dr. Andersen's research focuses on sports injury prevention, particularly in football, with special emphasis on football injuries, football physiology, and physical and rehabilitation medicine. His work spans multiple areas including neurodegenerative disease in female football players, groin and pelvic injuries, concussion management, and training load monitoring. He employs both prospective cohort studies and advanced statistical methodologies to investigate injury mechanisms and prevention strategies. His recent publications (2021-2025) demonstrate strong focus on women's football medicine, heading in football, mental health in professional footballers, and innovative approaches to injury risk modeling. The research often involves large-scale collaborations across Norwegian and international institutions, with particular attention to practical applications in football medicine. As an educator, he serves as a senior lecturer in the Master of Sports Physiotherapy Education Program and leads football medicine research initiatives. His clinical background as former team physician for the Norwegian Men's A-team (2002-2014) informs his practical approach to sports medicine research. Dr. Andersen also maintains active involvement in football beyond academia, having played professionally for IK Start (winning championships in 1978 and 1980), served as a coach, and held various leadership positions including executive board member at IK Start.

• Statistics
• Data Science
• Machine Learning
• +3 more

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.

Stephan SmeekesView profile

Professor

• Econometrics
• Data Science
• Time Series Analysis
• +7 more

Stephan Smeekes is a Professor of Econometrics at the Department of Quantitative Economics, School of Business and Economics, Maastricht University. His research focuses on statistical analysis of time series data, blending econometrics, statistics, and data science. Specializing in modeling complex dynamic systems with multiple time series, he emphasizes uncertainty quantification using bootstrap techniques, applied to macroeconomics, finance, climate, environmental, and medical domains. He led significant projects including the NWO Veni project on Bootstrap Methods for Time-Varying Processes and NWO Vidi project on Inference for High-Dimensional Econometric Time Series. As a former member of the Dutch Young Academy (De Jonge Akademie), he contributes to interdisciplinary research and innovation. His teaching spans statistics, econometrics, and computational data science at both undergraduate and graduate levels. Smeekes develops open-source software like the bootUR R package for bootstrap unit root tests, desla for high-dimensional inference, and PAGFL for latent group structure estimation. His work bridges academic insight with real-world impact through collaborations across disciplines.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web
• +5 more

Benno Kruit is a Visiting Professor at the Department of Artificial Intelligence, Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on knowledge representation, semantic web technologies, and AI applications in data interpretation and system dynamics. He has contributed to projects involving knowledge graphs, ontology engineering, and question answering systems. Key areas include semantic interpretation of tables, fault diagnosis in cyber-physical systems, and scalable simulation models. His work spans interdisciplinary topics such as environmental data analysis, agricultural informatics, and multilingual entity disambiguation. Kruit has co-developed platforms like TAKCO and Tab2Know for knowledge extraction from tables. He teaches the course Knowledge and Data and has contributed datasets like Tab2Know evaluation data to the research community. Recent research highlights include exploring open-source RAG systems, ontology-based simulation frameworks, and constrained LLM approaches for official statistics querying. His publications reflect a strong emphasis on practical applications of AI in knowledge integration and domain-specific problem-solving.

Glenn MagermanView profile

Associate Professor

• Production Networks
• International Trade
• Global Value Chains
• +1 more

Glenn Magerman is an Associate Professor of Economics (with tenure) at ECARES, Solvay Brussels School of Economics and Management, Free University of Brussels. Current Affiliations: CEPR Research Affiliate (trade and IO programs) CESIfo Research Network Affiliate Complex Adaptive Supply Networks Research Accelerator Member Institute of European Studies Affiliated Professor I3h Affiliated Researcher VIVES KU Leuven Fellow Research Interests: His work focuses on production networks, international trade, and micro-to-macro economic transmission, with particular emphasis on global value chain structures and implications as demonstrated in his Oxford Martin School public lecture “(Global) value chains: challenges and opportunities”. This research bridges theoretical microeconomic foundations with macroeconomic policy applications. Ongoing Grants: BELSPO HAIOPOLICY Grant (lead promotor, 881k euro, 2023-2027): Inequality in production networks and consumption heterogeneity AIML4OS (collaborator, 4 mln euro, 2024-2028): Foundations and Advances of Machine Learning in Official Statistics BELSPO BE-PIN (collaborator, 2 mln euro, 2023-2027): Post-Covid academic and policy collaboration and preparedness Current Roles: Academic consultant to the European Central Bank Research Network “Challenges to Monetary Policy” (2025).

Timo SchmidView profile

Professor

• Survey Statistics
• Small Area Estimation
• Poverty Measurement
• +5 more

Prof. Dr. Timo Schmid is the Chair of Statistics and Econometrics at Otto Friedrich University Bamberg and leads the Bamberger Centrum für Empirische Studien (BACES). Previously, he held the Chair of Applied Statistics at Freie Universität Berlin. He studied Mathematics at the University of Tübingen and worked as a consultant at A.T. Kearney before completing his PhD in Economic and Social Statistics at the University of Trier (2010–2012), supported by a sdw doctoral scholarship. His research focuses on Survey Statistics, Small Area Estimation, Poverty Measurement, Spatial Analysis, and Big Data applications (e.g., mobile network data). He is also a Board member of the German Statistical Society (DStatG) and editor of its journal Wirtschafts- und Sozialstatistisches Archiv . Key recent work includes integrating geospatial and survey data for poverty estimation in West Africa, developing machine learning-based mixed models for small area prediction, and applying mobile network data for regional unemployment analysis. His methodological contributions span variable selection in mixed models, kernel density estimation for grouped data, and robust spatial estimation techniques. Education: PhD in Economic and Social Statistics (University of Trier, 2012), Mathematics studies at University of Tübingen. Advising: Leads a team including Dr. Silvia Förtsch, Dr. Florian Meinfelder, and others. Supervises student researchers like Michael Mühlbauer and Franz Prücklmair. Grants & Projects: Collaborates on international projects involving geospatial data integration and methodological advancements in official statistics.

Thomas McAndrewView profile

Associate Professor

• Biostatistics
• Infectious Disease Modeling
• Data Science
• +3 more

Thomas McAndrew is an Associate Professor in the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. His research focuses on integrating surveillance data with human judgment to improve forecasts of infectious diseases, particularly influenza. He has developed novel ensemble algorithms like the Cluster-Aggregate-Pool (CAP) method and collaborates with public health officials to enhance decision-making through computational models. McAndrew holds a PhD in Mathematical Sciences from the University of Vermont, an MS in Biostatistics from Georgetown University, and a BS in Biomathematics from the University of Scranton. He has industry experience as an Associate Director of Biostatistics at the Cardiovascular Research Foundation, leading clinical trial analyses. His education includes: PhD in Mathematical Sciences, University of Vermont MS in Biostatistics, Georgetown University BS in Biomathematics, University of Scranton Research Interests: Chimeric forecasting (human judgment + computational models) Ensemble algorithms for influenza prediction Public health decision support systems Surveillance data integration techniques His lab, Compuncertlab , focuses on computational uncertainty reduction in health systems. Grants & Funding: Supported by NSF, CDC, CSTE, and MIDAS. His work emphasizes practical applications in pandemic preparedness and outbreak response. Courses Taught: Includes statistical literacy, outbreak science forecasting, and population health data analytics at both undergraduate and graduate levels.

• Statistical Quality Control
• Business Analytics
• Data Science
• +4 more

Professor Nick Fisher is affiliated with the University of Sydney , contributing to academic and applied research at the intersection of statistics and business analytics. His work emphasizes quantitative methods in industry, performance measurement systems, and the role of statistical principles in strategic decision-making. He has authored notable books on statistical quality control and business success, such as The Road to Quality Control (2019) and Analytics for Leaders (2013). His research interests span Statistical Quality Control , Business Analytics , Data Science , and Stochastic Modeling , with a focus on bridging theoretical statistical frameworks and their practical applications in industry, public policy, and education. He frequently explores topics like performance metrics design, enterprise culture optimization, and the impact of emerging technologies on traditional statistical methodologies. Recent articles highlight trends in leveraging statistical tools for national epidemic management, evaluating research collaborations, and addressing challenges in public perception of scientific issues. His interdisciplinary approach has also led to contributions in areas like historical academic figures (e.g., Willem van Zwet, Jae C. Lee) and the evolution of quality management practices globally. Professor Fisher has collaborated with researchers such as David Trewin, Alan Smith, and others on projects aligning with his expertise. While no specific grants or students are listed, his extensive publication record suggests active involvement in funded research and academic leadership. He maintains a professional homepage and is based at F07 - Carslaw Building.

• Macroeconomics
• Urban Economics
• Regional Economics
• +4 more

Gabriel Ehrlich is the Director of the Research Seminar in Quantitative Economics (RSQE) at the University of Michigan's Department of Economics, within the College of Literature, Science, and the Arts. He holds a Ph.D. in Economics from the University of Michigan and leads RSQE’s forecasting of the U.S. and Michigan economies, presenting regularly to state officials. His research focuses on macroeconomics, urban and regional economics, and forecasting, with notable work on Medicaid expansion impacts, housing policy, and labor markets. His analyses have been featured in the Washington Post , Economist , and New England Journal of Medicine . Key projects include assessments of Michigan’s Medicaid expansion, brownfield redevelopment, and the Great Lakes Restoration Initiative. His academic contributions span economic forecasting methodologies, hedonic pricing models, and firm reputation dynamics. Collaborations include work with the Congressional Budget Office and interdisciplinary teams on issues like electric vehicle labor dynamics and public debt implications. RSQE, under his leadership, produces quarterly U.S. and Michigan economic outlooks, complemented by fiscal impact analyses. His research bridges microdata and macroeconomic trends, emphasizing policy relevance and methodological innovation.

Fotis PapailiasView profile

Senior Lecturer

• Time Series Econometrics
• Financial Forecasting
• Macroeconomic Analysis
• +4 more

Fotis Papailias is a Senior Lecturer in Banking & Finance at King’s Business School and Deputy Director of the Data Analytics for Finance and Macro (DAFM) Research Centre. His research focuses on time series econometrics, financial and macroeconomic forecasting, resampling procedures, portfolio selection, and technical trading strategies. He has consulted for hedge funds, Oxford Economics, and the European Commission, applying his academic work to investment strategies via the quantf research website. Education: Advanced qualifications in economics and econometrics (specific details not publicly disclosed). Affiliations: King’s Business School, DAFM Research Centre. His research interests emphasize the development of methodologies for analyzing financial and macroeconomic data, including big data applications for nowcasting, volatility discovery, and structural break analysis. Key contributions include enhancing forecasting accuracy through novel econometric techniques and improving portfolio allocation via covariance averaging. Recent publications highlight trends in leveraging alternative datasets (e.g., Airbnb) and central bank communication for real-time economic assessment. His work bridges academic rigor and practical investment strategies, with a focus on trend-following strategies and hedging mechanisms. Grants & Consulting: Collaborations with the European Commission, Oxford Economics, and small hedge funds. Labs/Teams: Leads the DAFM Research Centre, focusing on quantitative finance and macroeconomic research.

Afshin AshoftehView profile

Assistant Professor

• Data Science
• Big Data
• Predictive Modeling
• +4 more

Afshin Ashofteh is an Assistant Professor at NOVA Information Management School (Nova University of Lisbon), a Research Fellow at Statistics Portugal (INE), an Integrated Researcher at MagIC, and a member of Technical Advisory Groups in WHO and UN DESA. He holds a Ph.D. in Information Management (Statistics & Econometrics), MBA in Finance, Post-graduate degree in Statistical Systems (Central Banks), and degrees in Mathematical Statistics. Certified in Risk Management and ISMS by TÜV NORD. His research focuses on Data Science, Big Data, predictive models, and their applications in financial risk, official statistics, and actuarial sciences. His award-winning work on credit scoring with Big Data (published in Expert Systems with Applications) highlights contributions to machine learning methodologies. He has contributed to international projects like the Strategic Planning Group of the International Data & Statistical Literacy Project (ISI) and judged the ISI Statistical Literacy Poster Competition. His speaking engagements include talks on Data Science, Open Banking, and Big Data mining at institutions like Universidade do Porto and CARMA2018.

Hyoungah ParkView profile

Assistant Professor

• Human Smuggling and Trafficking
• Immigration Policy
• Homeland Security
• +6 more

Hyoungah Park is an Assistant Professor of Criminal Justice at Saint Peter's University. He holds a Ph.D. in Criminal Justice from Rutgers University (2018) and has over 27 years of law enforcement experience in South Korea. His career includes roles as a police officer (Lieutenant to Deputy Inspector), Korean Embassy consul in Cambodia, and executive official in the Blue House (Korean presidential office). He has taught at Rutgers University as a Part-Time Lecturer and Washburn University as an Assistant Professor, covering criminology, forensic investigation, and policing topics. Education: Ph.D. in Criminal Justice from Rutgers University (2018). Research Interests His work focuses on human smuggling/trafficking, immigration policy, homeland security, futuristic criminology (AI-driven crime prediction), policing strategies, CPTED, and gender-based victimization. He actively uses machine learning (neural networks, random forests) for crime prediction and has authored a book on 'North Korean Migrants in China' and another upcoming on human trafficking. Key Contributions Leads the Futuristic Criminology Division in KOSCA and collaborates with undergraduate students on research. His studies span Northeast Asia's human trafficking dynamics, AI applications in crime prevention, and CPTED implementation.

• Medical Statistics
• Artificial Intelligence in Healthcare
• Machine Learning
• +3 more

Lei Clifton is a Research Fellow and Official Fellow in AI & Machine Learning at Reuben College, University of Oxford. She leads a team of researchers in the Nuffield Department of Population Health and contributes to the Pandemic Science Institute’s preparedness efforts. With a PhD in Statistical Machine Learning (2007, University of Manchester), her expertise spans large-scale clinical data analysis and healthcare AI deployment. She advises the NIHR Research Support Service on study design and collaborates with Prof David Clifton’s 'AI for Healthcare' CHI Lab. Her research focuses on medical statistics and AI applications in observational studies and electronic health records, including synthetic data generation, immune protein biomarker discovery, and scalable AI implementation in healthcare systems. She co-leads projects like the Implementation framework for AI deployment and dysregulated immune protein prediction in Multiple Myeloma. Key areas of contribution include healthcare AI ethics, cross-institutional model validation, and LLM applications in medicine. Her work bridges statistical rigor with real-world clinical challenges, emphasizing generalizability and fairness in machine learning systems.

Kelsey ShoubView profile

Assistant Professor

• Public Policy
• Policing and Race
• Political Science
• +3 more

Kelsey Shoub is an Assistant Professor of Public Policy at the University of Massachusetts Amherst, School of Public Policy. Her research focuses on how descriptive identities of officials and civilians intersect with policy contexts to influence outcomes, particularly in policing and race dynamics. She employs statistical methods, machine learning, and experimental approaches. Shoub co-authored Suspect Citizens: What 20 Million Stops Tell Us About Policing and Race , co-winning the 2018 C. Herman Pritchett Book Award. Her work appears in journals like Science Advances and Journal of Public Administration Research and Theory , alongside general-audience platforms like The Monkey Cage (Washington Post). Education: Ph.D. and M.A. in Political Science from the University of North Carolina; dual B.A. in Political Science and Philosophy from Ohio State University. Prior positions include Assistant Professor of Political Science at the University of South Carolina and post-doctoral research at the Center for Effective Lawmaking (affiliate status retained). Research emphasizes racial, gender, and language dimensions of policy impacts. Key areas include policing disparities, elite communication, and historical institutional legacies. Recent work examines traffic stop outcomes for marginalized groups, partisan positioning in congressional speech, and trust in police agencies. Awards: C. Herman Pritchett Book Award (2018) Advising/Grants: No formal advisee list provided. Active in interdisciplinary research teams at UMass and the Center for Effective Lawmaking. Labs/Teams: Affiliated with the Center for Effective Lawmaking, focusing on legislative process and policy impact analysis.