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Gesine ReinertView profile

Research Professor

• Applied Probability
• Computational Biology
• Stein’s Method
• +2 more

Dr. Gesine Reinert is a **Research Professor of Statistics** at the **University of Oxford**, affiliated with the **Mathematical Institute** and **Department of Statistics**. She holds a PhD in Mathematics from the University of Zurich and has held positions at UCLA, USC, and King’s College Cambridge. Her primary roles include advancing statistical methodology and applying it to network analysis and computational biology. Education and Positions: PhD in Mathematics, University of Zurich (1994) University Lecturer, Department of Statistics, Oxford (2000–2014) Fellow, Keble College, Oxford (2000–present) Research Focus: Network statistics and probabilistic modeling of complex systems Applications in computational biology, including protein-protein interaction networks Stein’s method for approximating probability distributions Research Groups: Probability Computational Biology and Bioinformatics Computational Statistics and Machine Learning Key Collaborations and Tools: Development of statistical frameworks for network comparison and generative models Advances in graph neural networks and topological data analysis

• Remote Sensing
• Environmental Science
• Atmospheric Science
• +5 more

Prof. Yong Xue is a faculty member in the College of Science and Engineering , specializing in remote sensing and environmental science. His work focuses on developing advanced algorithms for atmospheric and environmental monitoring using satellite data. Research interests include aerosol optical depth retrieval, air pollution dynamics, machine learning applications in remote sensing, and geospatial data analysis. He has contributed to methodologies for satellite-based air quality estimation, dust storm detection, and pandemic decision support systems. Key contributions include: Development of aerosol retrieval algorithms for multiple satellite platforms (Himawari-8, MSG, GOES-16) Integration of deep learning for minimally supervised image classification High-performance computing frameworks for geospatial data processing Environmental modeling for air pollution transmission and public health impact analysis Notable achievements include the creation of global hourly aerosol datasets and diagnostic models for pandemic risk assessment. His work bridges computational methods with environmental science, emphasizing real-world applications in pollution monitoring and Earth observation.

Fabio FranchView profile

Lecturer

• Financial Economics
• Econometrics
• Data Science
• +6 more

Fabio Franch is a Lecturer-in-charge/Convenor at the University of New South Wales (UNSW), where he teaches data analysis courses. Previously, he held senior roles at the European Central Bank (2012–2024) and the Reserve Bank of Australia (2018–2020), focusing on financial stability and policy using econometrics, machine learning, and network analysis. He holds a Master’s from the University of Trento and a PhD from West Virginia University. His research spans financial contagion, policy nowcasting, and AI-driven quantitative methods. He is also an online instructor at the University of Arizona and University of Maryland University College. Education: Master’s Degree, University of Trento PhD, West Virginia University Key Research Interests: Financial stability, policy analysis, machine learning applications, social media analytics for elections, and quantitative modeling. His work bridges policy, technology, and financial systems, emphasizing data-driven decision-making. Recent articles explore temporal networks in financial contagion and cross-border regulatory impacts in the Eurozone.

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.

Brecht VerbekenView profile

Research Fellow

• Operations Research
• Stochastic Modeling
• Markov Chains
• +4 more

Brecht Verbeken is a Post-doctoral Researcher at Vrije Universiteit Brussel's Brussels Humanities, Sciences & Engineering Campus, specializing in interdisciplinary research bridging Operations Research, Stochastic Modeling, and Artificial Intelligence. His work integrates mathematical rigor with practical applications across manpower planning, large language models, and econometric forecasting. His primary research focuses on stochastic processes for workforce management, including maintainability and attainability in semi-Markov systems, eigenvalue regions for stochastic matrices, and causal inference methods like uplift modeling. Recent work explores lexical accuracy in LLM reasoning chains and the internalization of scientific literature by AI systems, demonstrating strong cross-disciplinary connections between mathematical theory and modern AI challenges. Analysis of his 2022-2025 publications reveals a clear trajectory from traditional stochastic modeling toward AI integration, with consistent contributions to manpower planning while expanding into NLP, causal inference, and econometrics. His mathematical work on stochastic matrix properties provides foundational support for applied research in workforce optimization and AI evaluation, indicating a versatile approach to complex system modeling.

Benjamin Ultan CowleyView profile

Professor

• Cognitive science
• Computer and information sciences
• Neurosciences
• +7 more

Benjamin Ultan Cowley is Professor of Learning in Humans and Machines at the University of Helsinki 's Doctoral Programme Brain & Mind and Doctoral Programme in Cognition, Learning, Instruction and Communication . He obtained his PhD in Computer Science from the University of Ulster in 2009 after completing his Bachelors in ICT at Trinity College Dublin (2003). As leader of the HiPerCog research group , his work focuses on high performance cognition – studying how humans master complex cognitive tasks. Current research integrates cognitive science with educational technology through projects like AiWo (Human-Centric AI for fieldwork operations) and AiPerCog (AI learning from human gameplay). He also investigates decision-making in extreme environments through avalanche research collaborations. Recent publications demonstrate his interdisciplinary approach: 2025 studies include SSSUMO for real-time submovement decomposition, Structured human-LLM interaction for AI content generation dynamics, and Mechanisms of learning in ADHD using EEG biomarkers. His work bridges neuroscience, computer science, and educational psychology while developing practical applications for ADHD treatment and outdoor safety. Dr. Cowley currently manages two active projects: AIWo (Business Finland, 2025–2027) and AI Personality and Cognition (Academy of Finland, 2023–2027), while serving as project manager for the GUESSED project on uncertainty in experiential decisions (2021–2025). His research has been funded by NordForsk, Business Finland, and the Academy of Finland.

• Hydrology
• Environmental Hydraulics
• Flood Forecasting
• +3 more

Claudia Brauer is a Lecturer at Wageningen University & Research's Department of Hydrology and Environmental Hydraulics, within the Faculty of Science. Her research focuses on hydrological modeling, flood forecasting, and machine learning applications in environmental systems. She has contributed to projects in Thailand, the Netherlands, and Italy, emphasizing operational hydrological systems and catchment dynamics. Education: She holds a PhD from Wageningen University (2014) under Prof. R. Uijlenhoet and Dr. R. Teuling, focusing on parametric models for hydrological processes in catchments. Earlier degrees include a MSc and BSc in Civil Engineering (ir. title). Research interests span radar rainfall nowcasting, reservoir operation optimization, salt intrusion modeling in estuaries, and open-source hydrological tool development. She has co-developed the Wageningen Lowland Runoff Simulator (WALRUS) and contributed to the pysteps library for rainfall nowcasting. Recent work includes machine learning applications for real-time reservoir management in Thailand and LSTM-based salt intrusion forecasts in the Rhine-Meuse delta. She has supervised 4 PhD candidates, including ongoing projects on flood forecasting in Thailand and salt intrusion dynamics. Her research has led to over 50 publications in journals like Journal of Hydroinformatics and Hydrology and Earth System Sciences. She actively participates in international conferences and collaborates with institutions in Thailand, Italy, and across Europe.

• Hydrology
• Meteorological Radar
• Nowcasting
• +7 more

Remko Uijlenhoet is a Professor in Hydrology and Environmental Hydraulics at Wageningen University & Research (WIMEK). His research focuses on hydrometeorology, remote sensing of precipitation, and environmental fluid dynamics. He leads projects addressing plastic pollution in rivers, microwave remote sensing applications, and flood forecasting. He has supervised numerous PhD candidates and contributed to datasets like gridded precipitation interpolation and radar rainfall adjustments. Key collaborations involve institutions in Ghana, Switzerland, and the Netherlands. His work has been featured in media for innovations like using mobile phone signals for environmental monitoring. He actively participates in lectures and conferences on topics ranging from drought propagation to urban flood resilience.

• Computer Vision
• Deep Learning
• Continual Learning
• +4 more

Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.

Enrico CamporealeView profile

Senior Lecturer

• Machine Learning
• Space Physics
• Space Weather
• +4 more

Dr. Enrico Camporeale is a Senior Lecturer in Digital Environment at Queen Mary University of London's School of Physical and Chemical Sciences. He holds affiliations with the Centre for Experimental and Applied Physics and the Dutch National Center for Mathematics and Computer Science (CWI) as a part-time researcher. His work bridges machine learning, computational physics, and space physics, focusing on space weather forecasting, plasma turbulence, and radiation belt dynamics. Research interests include machine learning applications in geophysics, solar wind turbulence, and physics-informed neural networks. Leads projects like the VIDI grant 'Real-time forecasting of killer electrons on satellite orbits' and the CWI-INRIA collaboration on data-driven space weather predictions. His research employs advanced computational techniques to model space plasma phenomena, with a focus on developing predictive models for geomagnetic storms and radiation belt electron behavior. He has contributed to journals like Physical Review Letters , Space Weather , and Journal of Geophysical Research . Publications emphasize interdisciplinary approaches, combining machine learning with classical physics to address challenges in space environment prediction and understanding plasma dynamics at small scales.

Ioanna MiliouView profile

Senior Lecturer

• Data Science for Social Good
• Nowcasting
• Forecasting
• +6 more

Ioanna Miliou is a Senior Lecturer in the Department of Computer and Systems Sciences (DSV) at Stockholm University, affiliated with the Data Science Research Group. She holds a Ph.D. in Computer Science from the University of Pisa and a diploma in Electrical and Computer Engineering from the National Technical University of Athens. Her research focuses on Data Science for Social Good, Nowcasting, and Forecasting, leveraging Big Data Analytics, Data Mining, and Machine Learning. Key applications include healthcare (e.g., sepsis prediction, glucose forecasting), public health (influenza nowcasting), and peace/sentiment analysis using global news data. She leads projects such as A3S (AI asthma app), OrganoFeed (organoid research), and MASICU (ICU mortality prediction). Teaching includes courses like Foundations of Data Science and Data Mining. She advises Ph.D. students Franco Rugolon and Lena Mondrejevski. Her work emphasizes explainable AI, federated learning for privacy, and multimodal data integration. She contributes to initiatives like the Data Science Research Group and collaborates on EU-funded projects.

Jonas StriaukasView profile

Assistant Professor

• Econometrics
• Statistical/Machine Learning Methods
• Mixed Frequency Data Econometrics
• +3 more

Jonas Striaukas is an Assistant Professor of Statistics and Finance at the Copenhagen Business School (CBS), Department of Finance, and a Marie Skłodowska-Curie Action fellow. His research focuses on econometrics, statistical/machine learning methods, and mixed frequency data modeling. He holds a PhD from Université Catholique de Louvain (2022), supervised by Professors Andrii Babii and Eric Ghysels. Prior to CBS, he was a research fellow at Fonds de la Recherche Scientifique—FNRS in Belgium. Research Interests: High-dimensional econometric modeling, nowcasting techniques, factor-augmented regressions, and applications of machine learning to financial and macroeconomic data. His work emphasizes robust statistical methods for time series analysis and sparse/dense model comparisons. Key Projects: Developed the 'midasml' and 'FAS' R packages for econometric analysis. Conducted a 2022 project at the National Bank of Belgium on machine learning-based nowcasting models. Affiliations: Department of Finance, Center for Statistics, CBS. Member of the Marie Skłodowska-Curie fellowship program.

• Macroeconomic Forecasting
• Machine Learning
• Econometrics
• +6 more

Jad Beyhum is an Associate Professor in Economics at KU Leuven, affiliated with the Faculty of Economics and Business and its Economics Research Group. He specializes in econometrics and statistics, focusing on high-dimensional data analysis, instrumental variable methods, and survival analysis techniques. Current research themes include high-dimensional econometrics (e.g., LASSO-type procedures, factor models) and instrumental variables techniques (e.g., dynamic treatments, censored data). His methodological contributions target econometric challenges in nonlinear panel data, competing risks models, and duration outcomes. Key applications span corporate bankruptcy prediction, program evaluation, and macroeconomic forecasting. Recent publications highlight innovations in instrumental variable estimation under censoring, testing treatment effect homogeneity, and combining machine learning with traditional econometric frameworks. He actively supervises projects on topics like "Beyond Instrumental Variables" and "New methods to control unobserved heterogeneity" . Selected project leadership roles: "Predicting bankruptcy with machine learning methods" (Promotor, 2023-2027) "Partial identification methods for survival analysis" (Co-promotor, 2023-2027)

Michael BaiduView profile

Research Fellow

• Tropical convection
• Mesoscale Convective Systems
• High Resolution Numerical Weather Prediction Models
• +2 more

Dr. Michael Baidu is a Research Fellow at the University of Leeds' Institute of Climate and Atmospheric Science (ICAS) within the Faculty of Environment. His work focuses on atmospheric boundary layer modeling and mesoscale convective systems (MCSs) through the UM Boundary-layer REpresentation with Land-Atmosphere (UMBRELLA) project and the Advancing Nowcasting with Deep Learning (ANDeL) initiative for West Africa. Current Roles: Research Fellow (UMBRELLA project), Data Scientist (Leeds Institute for Data Analytics) Key Projects: Tropical MCS dynamics, deep learning nowcasting, earthquake detection models Education: PhD in Atmospheric Science (University of Leeds), PGDip in Earth System Physics (ICTP Italy), MPhil/BSc in Meteorology (KNUST Ghana) His research combines numerical weather prediction models, observational data analysis, and machine learning techniques to study boundary layer processes and improve weather forecasting capabilities in tropical regions. Professional affiliations include Royal Meteorological Society committees and organizing roles in ICAS research groups. He also contributes to student education through meteorology course instruction and Python-based data analysis training.

Marwa MajdiView profile

Researcher

• Atmospheric Aerosols
• Fog and Cloud Microphysics
• Weather Forecasting
• +6 more

Marwa Majdi is a Research Assistant Professor in the Atmospheric Sciences Department at the University of North Dakota , affiliated with the John D. Odegard School of Aerospace Sciences . Her work integrates atmospheric modeling, machine learning, and UAS operations to advance weather forecasting and visibility prediction. Research Interests : Marwa specializes in atmospheric aerosols, fog and cloud microphysics, multiscale weather modeling, and machine learning applications for aviation weather systems. She has developed UAS-focused technologies like the AeroVis system for visibility nowcasting and cloud ceiling estimation. Publications : Her recent work includes 2024 studies on wind turbulence nowcasting, 2022 papers on aerosol assimilation and UAS visibility systems, and 2019–2018 research on wildfire impacts on air quality and aerosol properties in the Euro-Mediterranean region. Grants and Awards : She secured a $30,000 UND Early Career Scholars Program (ECSP) grant (2023) Submitted proposals to NASA, FAA, and Air Force Office of Scientific Research for UAS safety and atmospheric modeling projects. Education : Ph.D. in Atmospheric Sciences, École des Ponts ParisTech and LMD-Ecole Polytechnique (2015–2018) M.S. in Environmental Quality Processes, University of Paris-Est Marne La Vallée (2014–2015) Experience : University of North Dakota (2024–present) Postdoctoral Research Fellow at UND (2020–2023) Lecturer at University of Eastern Paris (2017–2019)