Chris Monico is an Associate Professor in the Department of Mathematics & Statistics at Texas Tech University . He has been a faculty member there since 2003, following post-doctoral research at the University of Notre Dame. Education B.S. in Mathematics – Monmouth University M.S. in Mathematics – University of Notre Dame Ph.D. in Mathematics – University of Notre Dame Research Focus Monico’s scholarship centers on the intersection of cryptology , computational algebra , and number theory . A significant recent thrust has been the application of machine-learning techniques to mathematical finance , evidenced by work on random-forest models for option pricing and high-frequency trading risk metrics. Parallel lines of inquiry include post-quantum cryptographic schemes built on tropical algebra and semigroup actions, as well as classical problems in Ramsey theory and combinatorial semigroups . Publication Trends Between 2015 and 2025 Monico has published prolifically, with a clear shift around 2020 toward mathematical finance and machine-learning applications , alongside continued output in algebraic cryptanalysis and combinatorics . His 2024–2025 articles emphasize data-driven models in trading, whereas 2020–2021 works concentrate on cryptanalyses of tropical and group-based key-exchange systems. Earlier contributions focus on computational number theory and semigroup-based cryptography. Contact Information Email: c.monico@ttu.edu Phone: 806-834-4144 Office: Department of Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409-1042 Advising & Grants No specific doctoral or master’s students, funded grants, or named awards are detailed in the provided text. Laboratory or Research Group The text does not mention any dedicated laboratory or research group.
Dr. Matteo Fasiolo is a Senior Lecturer in the School of Mathematics at the University of Bristol, specializing in Statistical Science. His research focuses on advanced statistical modeling with significant applications in electricity demand forecasting and medical statistics, leveraging Generalized Additive Models (GAMs) as a core methodology. His primary research interests span: Generalized Additive Models and their extensions for complex data structures Covariance matrix modeling for high-dimensional energy forecasting Probabilistic forecasting techniques for uncertainty quantification Statistical machine learning including variational inference and contrastive learning Applications in electricity grid management and medical diagnostics Recent publications (2023-2025) reveal a dual focus: developing scalable statistical methods for electricity net-demand prediction in Great Britain using additive covariance models, and applying distributional regression to medical challenges like kidney function decline and cardiovascular risk prediction. His work on SoftCVI demonstrates innovation in variational inference, while extensions to GAMs address both mean modeling and full distributional forecasting. Dr. Fasiolo has supervised at least one student as indicated by university records. His research outputs include 16 publications and 2 publicly available datasets, reflecting active contributions to methodological statistics and domain-specific applications in energy systems.
Gregory F. Lawler is the George Wells Beadle Distinguished Service Professor in the Department of Mathematics at the University of Chicago. He also maintains appointments in the Department of Statistics and has affiliations with Computational and Applied Mathematics and Financial Mathematics programs. His academic background includes: B.A. (1976) from the University of Virginia Ph.D. (1979) from Princeton University under Edward Nelson Professor Lawler is a leading researcher in probability theory with a focus on conformally invariant processes , particularly the Schramm-Loewner Evolution (SLE) and various forms of random walks. His work bridges pure mathematics and statistical physics, establishing rigorous connections between discrete models and their continuous scaling limits. He has made fundamental contributions to understanding loop-erased random walks, Brownian motion, and the geometric properties of random curves, with applications in statistical mechanics for understanding two-dimensional critical phenomena. Analysis of his recent publications reveals a sustained focus on the mathematical foundations of SLE, with particular attention to natural parametrization, loop measures, convergence questions, and the relationship between discrete models and their continuous limits. His work consistently combines complex analysis with probabilistic methods to establish rigorous results about random curves. Professor Lawler is actively involved in mentoring through the University of Chicago's REU program and has developed extensive educational materials for students at all levels. His notes on probability theory and stochastic calculus are widely used resources within the mathematics community. He has authored several influential books including "Random Walk and the Heat Equation," "Conformally Invariant Processes in the Plane," and "Random Walk: A Modern Introduction" (with Vlada Limic), which have become standard references in probability theory.
Professor Shanlin Fu is a distinguished academic at the University of Technology Sydney (UTS), holding the position of Professor in the School of Mathematical and Physical Sciences and affiliated with the Centre for Forensic Science. He serves as the Program Director for the Bachelor of Forensic Science program and is a Research Integrity Adviser for the Faculty of Science. With over $10 million in competitive research funding from ARC, NHMRC, and other national and international schemes since 2008, Professor Fu leads the Drugs and Toxicology Group, focusing on developing sensitive methods for clinical diagnosis, therapeutic drug monitoring, and drugs of abuse testing. Professor, UTS School of Mathematical and Physical Sciences (2019-present) Associate Professor, UTS School of Chemistry and Forensic Science (2015-2019) Senior Lecturer, UTS School of Chemistry and Forensic Science (2008-2014) Professor Fu earned his PhD in Medicinal and Pharmaceutical Chemistry from the University of Sydney (1989-1992), an MSc in Phytochemistry from Peking Union Medical College (1982-1985), and a BSc in Biology from Nanjing Normal University (1978-1982). Prior to his academic career at UTS, he served as a Senior Hospital Scientist at the Northern Sydney Area Health Service (2000-2008) and as a Senior Research Scientist at The Heart Research Institute (1993-2000). Professor Fu's research spans analytical chemistry, forensic chemistry, medical biochemistry, pharmacology, pharmaceutical sciences, forensic toxicology, and clinical toxicology. His work focuses on three main areas: Forensic Chemistry concerning identification of drugs of abuse including new psychoactive substances; Forensic Toxicology focusing on detection of drugs in biological matrices for clinical and medico-legal purposes; and Clinical Toxicology aiming to understand mechanisms of substance abuse harms. His research has strong real-world applications, with his patented 'Cathinone Test' already commercialized for law enforcement and potential healthcare settings. Analysis of Professor Fu's recent publications reveals a strong emphasis on developing innovative analytical methods for drug detection, particularly for new psychoactive substances. His work increasingly incorporates multi-omics approaches (metabolomics, lipidomics, proteomics) and machine learning techniques to enhance detection capabilities. There's a clear trend toward translating laboratory research into practical field applications, with numerous color spot tests and portable detection methods being developed for law enforcement use. His research also shows expanding applications in equine doping control and postmortem analysis. Vice-Chancellor's Medal for Research Excellence through Collaboration or Partnership (2023) UTS Teaching and Learning Award for Team Teaching (2022) MAPS Research Translation Award (2022) As a member of the HDR Panel since 2022, Professor Fu actively supervises Masters Research and PhD students in forensic science. His extensive grant portfolio includes leadership of the ARC Research Hub for Integrated Device for End-user Analysis at Low-levels and the Australian Centre for cannabinoid clinical and research excellence (ACRE). He has established key collaborations with Australian Federal Police, NSW Forensic and Analytical Science Service, Racing NSW, and international institutions including University of Copenhagen and University of Dundee. His research impact extends beyond academia through commercialization of detection technologies that improve efficiency and accuracy of illicit drug detection. Professor Fu heads the Drugs and Toxicology Group at the Centre for Forensic Science, which maintains strong industry partnerships with forensic laboratories and law enforcement agencies. His group is currently developing a multiplexer device that can simultaneously detect multiple new psychoactive substances including cathinones, NBOMEs, piperazines, and fentanyl analogues. The group's work bridges fundamental research with practical applications, with several technologies moving from the laboratory to real-world implementation in forensic and healthcare settings.
Gourab Ray is an Associate Professor in the Department of Mathematics and Statistics at the University of Victoria, Faculty of Science. He holds a PhD from the University of British Columbia, Vancouver. His research focuses on the intersection of probability theory, geometry, and mathematical physics, particularly large-scale patterns in stochastic models inspired by physics. Key areas include random planar maps, random walks, lattice spin models, dimer models, Gaussian free field properties, and Liouville quantum gravity. Recent work emphasizes establishing Gaussian free field-like behaviors in dimer models across various graphs and surfaces. He teaches courses such as MATH 236: Introduction to Real Analysis and MATH 555: Topics in Probability. His publications span leading journals including Inventiones Mathematicae , Annals of Probability , and Probability Theory and Related Fields . Notable contributions include studies on unimodular hyperbolic triangulations, half-planar map classifications, and conformal invariance in dimer models. No specific awards are listed for Dr. Ray, though his work has been recognized in peer-reviewed venues. He actively contributes to academic service, including roles on graduate committees and research collaborations. His research group engages with theoretical and applied aspects of probability theory, often bridging discrete and continuous mathematical frameworks.
Pierre Marquis is a distinguished Professor of Computer Science at Université d'Artois , affiliated with the Centre de Recherche en Informatique de Lens (CRIL-CNRS, UMR 8188) . Since December 2024, he has served as the vice-president for research and doctoral studies at Université d'Artois. His research focuses on Artificial Intelligence , particularly knowledge representation , automated reasoning , inconsistency handling , and knowledge compilation , with recent emphasis on Explainable AI (XAI) . Research Interests: Marquis's work spans foundational AI topics including abduction , induction , belief revision , and preference modeling . He has pioneered knowledge compilation techniques to optimize AI tasks and developed frameworks for reasoning under inconsistency through paraconsistent logics and argumentation. His EXPEKTATION chair (2020-2026) under France's national AI program drives his current focus on interpretable machine learning models. Scientific Awards: 2025: CNRS Silver Medal 2022: AAIA Fellow 2017: Senior Member of Institut Universitaire de France (IUF) 2009: EurAI (ECCAI) Fellow Doctoral Students: Mentoring Clément Lens (critical patient monitoring systems) and Mehdi Sabiri (data-knowledge integration for AI explanations). Collaborating with students like Louenas Bounia (formal XAI models) and Romain Wallon (pseudo-Boolean constraints). Grants & Projects: Leads the EXPEKTATION research chair (2020-2026) and participates in ANR PING/ACK (2019-2023), ANR THEMIS (2021-2025), CNRS IRP MAKC (2020-2024), and H2020 TAILOR (2020-2024). Previously led PIA4 MAIA (2023-2032) and Pint (2022-2023). Labs & Teams: Active in CRIL-CNRS, contributing to PyXAI (Python XAI library) and d4 (model counting), while mentoring teams on consensus belief merging and dynamic constraint processing .
Edward T Crane is a Heilbronn Associate Professor at the School of Mathematics, University of Bristol, specializing in Probability, Analysis and Dynamics within the Pure Mathematics department. His research is affiliated with the Heilbronn Institute for Mathematical Research. Dr. Crane's research interests span multiple areas of mathematics, with particular focus on: Probability theory and stochastic processes Asymptotic analysis Circle mathematics and unit disk problems Riemann surfaces and connected components Polynomial mathematics and edge theory His recent research has focused on stochastic models, branching processes, and large deviation principles. Crane has published in top probability journals including Stochastic Processes and their Applications, Annals of Probability, and Annals of Applied Probability. His work often bridges theoretical mathematics with applications in areas like queueing theory and biological modeling, demonstrating both theoretical depth and practical relevance across multiple domains of mathematical research. Dr. Crane maintains an active research profile with 17 academic publications to date, with his most recent work in 2024 examining the limit point in Jante's law process and establishing its absolutely continuous distribution properties. His professional affiliations include: Heilbronn Institute for Mathematical Research Dr. Crane holds academic qualifications including a B.A. from Cambridge, A.M. from Harvard, and Ph.D. from Cambridge. He can be contacted at Edward.Crane@bristol.ac.uk and maintains an ORCID profile at https://orcid.org/0000-0002-4215-2884.
Adriano Jorge Cardoso Moreira is an Associate Professor with Habilitation at the Department of Information Systems, School of Engineering, Universidade do Minho, Portugal. He is also a Senior Researcher at the Algoritmi Research Centre and Scientific Coordinator of the Urban and Mobile Computing department at Centro de Computação Gráfica. His research focuses on indoor positioning , mobile and context-aware computing , urban computing , and simulation of wireless networks . Research Interests : Indoor Positioning, Mobile Computing, Urban Mobility, Sensor Networks, Wi-Fi and UWB Localization, Smart Cities. Leadership : Coordinated the Computer Communications and Pervasive Media Group (2008-2016), Scientific Committee member (Director of MAP-tele PhD program in multiple terms), and leads the Master in Telecommunications and Informatics since 2021. Publications : Over 100 papers, including IEEE Transactions and Sensors journal articles, with an h-index of 23 and 2136 citations. Awards : First and second prizes in EvAAL-ETRI Indoor Localization Competitions (2015, 2016, 2017).
Dr. Husam Al-Najjar is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He serves as the Course Director for the Bachelor of Information Systems (BIS) program. With expertise in geospatial technology and machine learning, Dr. Al-Najjar focuses on predicting and mitigating natural hazards and environmental issues to contribute to a sustainable digital earth. Dr. Al-Najjar earned his PhD from the University of Technology Sydney. Before joining academia, he worked in project management and has received numerous prestigious awards, scholarships, and grants throughout his career. Dr. Al-Najjar's research primarily centers on the application of machine learning techniques to address complex environmental challenges. His work spans geospatial AI, natural hazard prediction (particularly landslides and bushfires), and sustainable development. He has developed innovative approaches that integrate physical models with machine learning algorithms to improve prediction accuracy in data-scarce environments. His research also extends to remote sensing applications, urban planning, and smart city technologies, with a strong emphasis on practical solutions for real-world problems. Analysis of Dr. Al-Najjar's recent publications reveals a strong focus on applying explainable AI techniques to natural hazard prediction, particularly landslides. His work consistently bridges the gap between theoretical machine learning approaches and practical geospatial applications. He has made significant contributions to integrating physical models with AI, developing methods for handling imbalanced data through generative adversarial networks, and improving feature selection for remote sensing applications in environmental monitoring. Best Paper Award at the ISPRS Geospatial Week in Enschede, the Netherlands As an educator, Dr. Al-Najjar is actively involved in mentoring and teaching. He serves as Course Director for the Bachelor of Information Systems program and teaches courses in GIS, Information Systems, IS development methodologies, Design & Innovation, and Project Management. He welcomes prospective PhD candidates interested in his research areas and emphasizes the importance of detailed research proposals that demonstrate novelty and significance. His teaching philosophy focuses on fostering an engaging and inclusive learning environment that promotes student success and well-being. Dr. Al-Najjar is affiliated with 'The Trustworthy Digital Society' concentration at UTS and serves as a referee and holds editorial roles in respected journals. His work contributes to the development of geospatial AI frameworks that support decision-making in environmental management and disaster preparedness.
Dawei Han serves as Professor of Hydroinformatics at the University of Bristol's School of Civil, Aerospace and Design Engineering, leveraging advanced computational techniques to address hydrological challenges. Holding a B.Eng. and M.Sc. from Huabei alongside a Ph.D. from Salford, he is recognized as a Chartered Engineer (C.Eng.) and Fellow of the Chartered Institution of Water and Environmental Management (FCIWEM). His academic credentials include: Bachelor of Engineering (B.Eng.) from Huabei Master of Science (M.Sc.) from Huabei Doctor of Philosophy (Ph.D.) from University of Salford Professor Han's research focuses on integrating hydroinformatics with practical water management solutions, particularly in urban environments. His work pioneers applications of machine learning for rainfall nowcasting, radar-based hydrological monitoring, and climate change impact assessment. Key innovations include DREE-RF for rainfall energy estimation and frameworks for urban flood resilience, emphasizing data-driven approaches to enhance prediction accuracy and risk mitigation strategies. Analysis of his 2024-2025 publications reveals dominant themes in urban hydrology (40%), flood risk management (30%), and climate-remote sensing integration (30%). His research spans global contexts from UK catchments to Iraqi rainfall systems, consistently employing computational methods like neural networks and WRF modeling to address data-scarce environments and extreme weather events. Professional recognition includes: Fellow of the Chartered Institution of Water and Environmental Management (FCIWEM) While specific student supervision details are unavailable, his extensive publication record indicates active mentorship in hydroinformatics. Research grants likely support his work on radar remote sensing and urban climate adaptation, though explicit funding sources aren't documented in the source material. His affiliation with Bristol's engineering school positions him within interdisciplinary teams addressing infrastructure resilience, though laboratory-specific information remains unreported.
Andreas Groll is a Professor at the Technical University of Dortmund, affiliated with the Department of Statistical Methods for Big Data under the Faculty of Statistics. His research focuses on variable selection, regularization techniques in generalized linear models, categorical data analysis, and sports statistics, particularly predicting international soccer and tennis tournaments. He leads a working group including researchers like Dr. Daniel Horn and Dr. Rouven Michels. Key research areas include semiparametric regression and event data analysis. Recent work explores machine learning applications in sports analytics and healthcare, such as predicting hospital readmissions and modeling environmental data. Groll has published extensively in journals like Journal of Quantitative Analysis in Sports and Statistical Modelling .
Jeff Sadler is an Assistant Professor in the Department of Biosystems & Agricultural Engineering at Oklahoma State University, where he also serves as an Extension Specialist for Water Resources with OSU Extension. He leads the WaDE (Water Data and Education) Lab, focusing on data science and machine learning applications in water resources. Education: PhD in Civil and Environmental Engineering, University of Virginia (2019) MS in Civil Engineering, Brigham Young University (2015) BS in Civil Engineering, Brigham Young University (2013) Research Interests: Jeff’s research lies at the intersection of data science and water resources. He specializes in machine learning, particularly physics-guided and process-aware deep learning, for modeling stream temperature, water quality, flood dynamics, and hydrological forecasting. His work emphasizes real-time decision support, reproducible modeling, and integrating domain knowledge into data-driven systems. Recent Research Trends: His recent publications demonstrate a strong focus on advanced deep learning architectures (e.g., graph neural networks, recurrent models), data assimilation, multi-task learning, and surrogate modeling for environmental systems. Applications center on the Delaware River Basin and coastal Virginia, with implications for climate change adaptation and infrastructure resilience. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Jeff mentors graduate students and supervises master's and doctoral research. He is actively funded through multiple grants from the USDA, NOAA, and USGS, supporting projects in water quality monitoring, rural health, evapotranspiration forecasting, and integrated hydrological modeling. Labs and Teams: He leads the WaDE Lab, which develops data-driven tools for water resource education and management. He has collaborated extensively with researchers from the U.S. Geological Survey, University of Virginia, and other institutions on cyberinfrastructure, reproducible modeling, and environmental machine learning.
Flavio Bezerra Costa serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University's College of Engineering. His research focuses on critical areas of modern power systems, including smart grid technologies, renewable energy integration, power system protection, and advanced applications of signal processing and artificial intelligence in electrical power networks. Dr. Costa's research interests span a comprehensive range of power system topics with particular emphasis on Smart Grid technologies, Integration of Renewable Energy Systems, Power System Protection, Control, and Monitoring, Power Quality analysis, Power Systems and Power Electronics, AC/DC Microgrids, High-Voltage Direct Current (HVDC) Electric Power Transmission Systems, and the application of Signal Processing and Artificial Intelligence (including Machine Learning) in power systems. His work bridges traditional power engineering with modern computational techniques to address contemporary grid challenges. Analysis of Dr. Costa's recent publications reveals a consistent focus on wavelet transform applications for power system protection and monitoring, particularly in the areas of fault detection, classification, and location. His research demonstrates strong integration of machine learning techniques with traditional power system protection methods, with significant contributions to transformer protection, transmission line fault analysis, and microgrid stability. The work shows an evolving trajectory from fundamental wavelet-based protection techniques toward more sophisticated AI-enhanced approaches for modern power grid challenges. Dr. Costa maintains an active research program with numerous publications in top-tier IEEE journals and conferences, demonstrating his significant contributions to the field of power systems engineering and protection.
Kristen L. Corbosiero is a Professor in the Department of Atmospheric & Environmental Sciences at the University at Albany, State University of New York. Her primary research focuses on understanding the structure, intensity changes, and environmental interactions of tropical cyclones, including processes like secondary eyewall formation and rapid intensification. She utilizes both observational data and numerical models to explore topics such as cloud microphysics, vertical wind shear effects, and the role of lightning in storm dynamics. Education: PhD, Atmospheric Science, University at Albany, SUNY, 2005 MS, Atmospheric Science, University at Albany, SUNY, 2000 BS with Distinction, Atmospheric Science, Cornell University, 1997 Research Interests: Dr. Corbosiero investigates tropical cyclone behavior, including the formation of hurricane rainbands and secondary eyewalls, the impact of environmental conditions on storm evolution, and the influence of cloud microphysical parameterizations. Her work also addresses the predictability of heavy rainfall events linked to tropical systems, such as atmospheric rivers and remnant cyclones. Articles & Research Trends: Her recent publications emphasize ventilation processes in tropical cyclones, diurnal pulsations in hurricane structure, and the climatological significance of downshear reformation. She collaborates on projects funded by NASA, NOAA, and UCAR, advancing the understanding of cyclone dynamics and forecast improvement. Advising & Students: Dr. Corbosiero has mentored numerous graduate and undergraduate students, many of whom have contributed to studies on tropical cyclone evolution, precipitation patterns, and mesoscale meteorology. Notable advisees include Nicholas Johnson (ventilation in sheared storms) and Alex Mitchell (eastern Pacific cyclone variability). Labs & Teams: Her research group actively explores topics like tropical cyclone predictability, lightning activity in storms, and the North American Monsoon System’s interaction with eastern Pacific systems. Current projects involve ensemble-based sensitivity analysis and high-resolution numerical simulations.
Jana Mareckova is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research (SIEW), part of the School of Economics and Political Science (SEPS) at the University of St. Gallen. She joined the university in 2020 after completing a postdoc at SEW-HSG following her PhD from the University of Konstanz (2019). Her research focuses on causal machine learning, shrinkage methods, regularization techniques, and labor economics. She explores applications in labor market outcomes and fairness, leveraging econometric tools to address real-world economic questions. Education: PhD in Econometrics, University of Konstanz (2019); Postdoc at SEW-HSG (pre-2020). Research interests include shrinkage estimation for categorical regressors, causal inference via machine learning, and predicting economic outcomes using noncognitive skills. Her work bridges statistical theory with practical policy analysis, as seen in her 2021 Journal of Econometrics publication on shrinkage methods. Recent projects emphasize causal forests and comprehensive frameworks for policy evaluation. No scientific awards are listed, though her contributions to causal ML and econometric methods are notable. She has no documented advising or grant information. Her research is affiliated with SIEW, focusing on empirical economic research.
