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Michael NagelView profile

Researcher

• Bayesian Statistics
• Structural Equation Modeling
• High-Dimensional Data Analysis
• +7 more

Michael Nagel is a Research Associate at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has been involved in a DFG-funded project since 2021, focusing on advanced statistical modeling and behavioral economic analysis. Master of Science in Economics and Finance, University of Tübingen (2020) Bachelor of Science in Economics and Business Administration, University of Tübingen (2018) His research spans Bayesian statistics, high-dimensional data analysis, and behavioral economics, with a particular focus on emotional decision-making in sports contexts and statistical methodology for structural equation modeling. He applies machine learning techniques to social science questions, especially in modeling complex human behaviors under uncertainty. The two most recent publications highlight his dual focus: one on emotional drinking during soccer matches, linking suspense and surprise to alcohol use, and another on developing alternative Bayesian priors for high-dimensional estimation. These works reflect a strong interdisciplinary trend combining econometrics, psychology, and computational methods in social sciences. While no formal scientific awards are listed in the provided texts, his work has been presented at notable academic forums including the Cluster of Excellence 'Machine Learning in Science' conferences and the DGPs Congress. Michael Nagel has advised or collaborated on research projects, particularly within the Methods Center, and is supported by a DFG research grant. He actively contributes to open science through replication packages on GitHub. He is part of the research team led by Prof. Augustin Kelava, focusing on methodological innovation in the social sciences. He is affiliated with the Methods Center at the University of Tübingen, a hub for advanced quantitative methods in social sciences. The center fosters interdisciplinary collaboration, particularly in integrating machine learning with traditional social science research. His work is aligned with the Cluster of Excellence 'Machine Learning in Science', indicating strong integration into cutting-edge research networks.

Regina NuzzoView profile

Professor

• Statistics Education
• Statistical Communication
• Research Methodology
• +3 more

Dr. Regina Nuzzo is a Professor of Mathematics at Gallaudet University, affiliated with the School of Science, Technology, Accessibility, Mathematics, and Public Health. She holds a Ph.D. in Statistics from Stanford University (2003) and a master's in Science Writing from UC Santa Cruz (2004). Her work bridges statistical research, education, and public communication, emphasizing reproducibility and clear data interpretation. Dr. Nuzzo's research focuses on statistical methodology, particularly in addressing issues like p-value misuse, communication of risk and relative risk, and improving statistical literacy. She has authored influential articles in Nature , Science , and New York Times , including her landmark 2014 piece on p-values that earned an ASA award. She co-authored the seminal 2016 ASA P-Values Statement, reshaping statistical practice. Education: Ph.D. in Statistics (Stanford), B.S. in Industrial Engineering (USF) Teaching: Courses span applied statistics, probability, and research methods for social sciences Awards: ASA Excellence in Statistical Reporting Award (2014) Her advisory roles include mentoring doctoral students in linguistics and psychology, and she actively contributes to workshops on statistical communication for journalists and researchers. She promotes accessible quantitative education for diverse audiences, including deaf and hard-of-hearing learners.

Herbert HoijtinkView profile

Professor

• Bayesian Statistics
• Multivariate Statistics
• Structural Equation Modeling
• +3 more

Herbert Hoijtink is a Professor of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Hoijtink Methodology and Statistics research group and specializes in Bayesian statistical methods, particularly the evaluation of informative hypotheses. His work focuses on improving empirical research through prior knowledge integration, with applications in psychology, education, and biomedical sciences. Research interests include Bayesian model comparison, multivariate statistics, and structural equation modeling. He has developed the bain software package for Bayesian evaluation of inequality-constrained hypotheses. Key contributions include advancing sample size determination for multilevel models and promoting open science practices through the Open Empirical Cycle . Hoijtink has secured significant grants, including NWO VICI and Gravity grants (€27.6 million), and holds fellowships like the NIAS-KNAW residency (2019). He co-authored influential papers on Bayesian replication studies and contributed to the Redefine Statistical Significance debate. His work bridges methodological innovation with practical applications in clinical, developmental, and educational research. Key Projects: Bayesian sample size calculation for longitudinal data, NWO-funded studies on child development Labs/Teams: Methodology and Statistics group at Utrecht University Awards: Psychometric Society Award (2016), VICI Grant (2005)

Takashi HayashiView profile

Professor

• Microeconomic theory
• Decision theory
• Social choice
• +3 more

Professor Takashi Hayashi holds the rank of Professor in Economics at the Adam Smith Business School, University of Glasgow. He previously held positions at the University of Texas at Austin and completed his PhD at the University of Rochester in 2004. His research focuses on decision-making under uncertainty and over time, encompassing both individual and collective contexts. Key areas include social choice theory, welfare economics, and the axiomatic analysis of consistent/inconsistent decision-making processes. He is affiliated with the Microeconomics research cluster at Glasgow. His recent work explores belief aggregation, dynamic collective choice, and intertemporal decision mechanisms. Articles from 2024–2022 emphasize topics like heterogeneous discounting, self-control models, and envy-free solutions under labor disparities. Hayashi has supervised three PhD students: Song Xihao, Zhang Tiannan, and Zhao Xinchi. He teaches Microeconomic Analysis (MA) and has authored books on macroeconomics and microeconomics. His experimental studies investigate partial equilibrium mechanisms and inter-sectoral coordination.

Claudia FecarottiView profile

Assistant Professor

• Reliability Engineering
• Maintenance Modelling and Optimization
• Infrastructure Asset Management
• +6 more

Claudia Fecarotti is an Assistant Professor in Resilient Asset Management and Maintenance at Eindhoven University of Technology (TU/e), Netherlands, since January 2019. She is affiliated with the Operations, Planning, Accounting and Control Group (OPAC) within the Department of Industrial Engineering and Innovation Sciences, as well as EAISI and the 4TU Centre for Resilience Engineering. Prior to TU/e, she was a Research Associate at the University of Nottingham's Resilience Engineering Research Group (RERG), where she earned her PhD in Reliability Engineering and Infrastructure Asset Management in 2018. Education: BSc in Civil Engineering, University of Palermo, Italy MSc in Transportation Systems and Infrastructures, University of Palermo, Italy PhD in Reliability Engineering and Infrastructure Asset Management, University of Nottingham, UK Her research focuses on reliability engineering, maintenance modeling and optimization, and infrastructure asset management. She develops methodologies for planning, operating, and maintaining complex safety-critical systems (railways, fuel cells, offshore oil/gas, agricultural robots) to enhance performance and reduce costs. Her work bridges academic rigor with industrial relevance. Recent research trends include condition-based maintenance for multi-component systems, intervention planning for interconnected infrastructures, and digital tools for railway and modular construction systems. Her work aligns with UN Sustainable Development Goals for resilient infrastructure and sustainable cities. Scientific Awards: IMechE’s Best Young Researcher Award 2013 IMechE’s Donald Julius Groen Prize 2017 She is actively involved in projects like Modular prefabricated construction (2024–2027) and serves on the editorial board of the Journal of Infrastructure Systems (ASCE). Her expertise spans Petri nets, fault tolerance, and systems performance optimization.

Wuchen LiView profile

Assistant Professor

• Transport information geometry
• Complex dynamical systems
• Partial differential equations (PDEs)
• +8 more

Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .

Jacques de SwartView profile

Professor

• Bayesian Statistics
• Responsible AI
• Strategic Decision Making
• +5 more

Prof. Jacques de Swart is a Professor of Applied Mathematics at Nyenrode Business University, where he is part of the Faculty Research Center for Accounting, Auditing & Control. He holds a unique part-time role, dedicating one day per week to academia while serving as a Partner at PwC’s Consulting practice for the remaining four days. His expertise bridges mathematical rigor with business applications, focusing on audit processes, Bayesian statistics, and strategic decision-making aligned with ESG principles. Prof. de Swart’s academic journey includes a Mathematics degree from Utrecht University, with studies at La Sapienza University in Rome. He completed his PhD in Computational Science at the University of Amsterdam under the Centre for Mathematics and Computer Science (CWI). Later, he earned an MBA from Rotterdam School of Management, Erasmus University. His research interests revolve around Bayesian statistical methods, responsible AI integration, and strategic decision-making frameworks that prioritize environmental, social, and governance (ESG) considerations. He actively develops and teaches modules in machine learning, statistics, and programming, emphasizing their practical applications in auditing and business analytics. His publications highlight the application of Bayesian approaches to audit sampling and transparency, advocating for open-source tools to bridge auditors and statisticians. Recent work explores strategic decision-making frameworks that align business goals with sustainability and ethical practices. Earlier contributions include foundational research on computational methods for orbit calculations and Fekete point configurations. Prof. de Swart supervises students in their master’s thesis projects, fostering the next generation of analytical thinkers. His work reflects ongoing engagement with industry partnerships and data-driven innovation. He also chairs the Steering Group for Statistical Audit and the Watersportpark Ouderkerkerplas Foundation, advocating for accessibility in water sports for people with disabilities.

Patrick LaubView profile

Senior Lecturer

• Actuarial Science
• Data Science
• Artificial Intelligence
• +3 more

Patrick Laub is a Senior Lecturer at the UNSW School of Risk and Actuarial Studies, where he teaches courses in artificial intelligence and machine learning with a focus on risk and insurance applications. His academic work bridges the gap between advanced computational methods and practical actuarial problems. Patrick holds a joint PhD in computational applied probability completed between the University of Queensland and Aarhus University. He also possesses degrees in software engineering and mathematics, providing him with a strong interdisciplinary foundation for his research. His research focuses on computationally challenging problems in actuarial data science, with particular emphasis on natural catastrophe modeling and artificial intelligence applications. Key areas of investigation include Hawkes processes for modeling contagion in insurance claims, Approximate Bayesian Computation for fitting complex insurance loss models, and Empirical Dynamic Modeling for analyzing complex temporal dependencies. His work addresses critical challenges in risk assessment, particularly for extreme events where traditional statistical methods may be inadequate. Analysis of Patrick's recent publications reveals a strong trend toward integrating advanced statistical methodologies with practical actuarial applications. His work spans from theoretical developments in point processes and Bayesian inference to practical implementations in computational environments. A notable theme is the application of machine learning techniques to traditional actuarial problems, particularly in the areas of catastrophe modeling and risk prediction. Patrick has developed and taught innovative courses since 2022, including 'Artificial Intelligence & Deep Learning and their Applications to Risk and Insurance' (ACTL3143 and ACTL5111) and 'Statistical Machine Learning for Risk and Actuarial Applications' (ACTL5110). These courses reflect his commitment to preparing students for the evolving landscape of data-driven risk management in the insurance industry. His research is supported by UNSW's strong infrastructure for computational research, though specific lab affiliations are not explicitly mentioned in the available information. Patrick maintains an active research program with numerous publications across statistics, actuarial science, and computational methods.

Eric NalisnickView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +5 more

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.

Giuseppe LopomoView profile

Professor

• Game Theory
• Auction Theory
• Mechanism Design
• +3 more

Giuseppe Lopomo is the Alan D. Schwartz Distinguished Professor of Business Administration at Duke University's Fuqua School of Business, with a courtesy appointment in the Economics Department. He holds a Laurea Magna cum laude from Bocconi University and a Ph.D. in Business Administration from Stanford. Prior to Duke, he was an Assistant Professor at NYU Stern and visited the University of Michigan. He teaches Managerial Economics and Competitive Analysis to MBA students and has instructed undergraduate and Ph.D. courses. His research focuses on applied game theory, auction theory, and mechanism design, addressing topics like procurement, surplus extraction, and optimal contracting. His work appears in top journals such as The American Economic Review and The Review of Economic Studies. Education: Laurea Magna cum laude, Bocconi University (Italy) Ph.D. in Business Administration, Stanford Graduate School of Business Research Interests: Professor Lopomo's work explores strategic interactions in economic systems, particularly through applied game theory and mechanism design. His studies often address practical challenges in procurement processes, auction design, and the robustness of economic mechanisms under uncertainty. Key themes include optimizing market structures, managing private information, and ensuring efficiency in resource allocation. Recent Trends in Publications: Recent work emphasizes mechanisms under uncertainty, surplus extraction, and the interplay between budget constraints and economic outcomes. His 2023 paper on procurement quality concerns and 2022 studies on detectability and duality highlight his focus on real-world applications of theoretical models. Awards & Recognition: While no specific awards are listed, his endowed chair position reflects his academic distinction. His extensive publication record in top-tier journals underscores his scholarly impact. Advising & Grants: No specific advising records or grant details are provided in the texts. His teaching and research activities are central to his current professional profile. Labs/Teams: No formal lab or team affiliations are mentioned, though his collaborations are implied through co-authored publications.

Sergio BacalladoView profile

Associate Professor

• Bayesian methods
• Bayesian nonparametrics
• Markov models
• +3 more

Sergio Bacallado serves as an Associate Professor in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, where he is affiliated with the Statistical Laboratory. His academic home is within one of the world's leading mathematics departments, contributing to both theoretical and applied statistical research. Dr. Bacallado's research program centers on Bayesian methods and nonparametrics, with particular emphasis on Markov models and their applications to biological systems and biophysics. His work bridges theoretical statistics with practical applications in drug discovery, pandemic analysis, and microbiome research, demonstrating how sophisticated statistical methods can solve complex problems in the life sciences. His publication record from 2015-2024 reveals a consistent trajectory of methodological innovation with increasing interdisciplinary impact. Early work focused on theoretical foundations of Bayesian nonparametrics and Markov processes, while more recent publications address pressing challenges in pharmaceutical science (molecular docking, adverse drug reaction prediction) and public health (pandemic analysis). This evolution demonstrates his ability to adapt statistical theory to emerging scientific needs. Though no specific awards are listed in the available information, his publications in top-tier journals including the Journal of the American Statistical Association, Annals of Applied Statistics, and Journal of the Royal Statistical Society Series B indicate recognition within the statistical community. As an Associate Professor at Cambridge, Dr. Bacallado likely supervises PhD students and postdoctoral researchers in statistical methodology, though specific advisees aren't mentioned in the available materials. His collaborations span computational chemistry, epidemiology, and microbiome research, suggesting an active role in interdisciplinary grant-funded research. Based at the Statistical Laboratory within DPMMS, he contributes to one of the world's premier centers for mathematical statistics, working alongside colleagues who advance both theoretical foundations and practical applications of statistical science.

Svein Olav Glesaaen NybergView profile

Associate Professor

• Bayesian Statistics
• Probability Theory
• Stochastic Processes
• +7 more

Svein Olav Glesaaen Nyberg is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway. He has been affiliated with the university since 2003, contributing to research and education in Bayesian statistics, probability theory, and stochastic processes. His work spans interdisciplinary domains including Artificial Intelligence , Biomechatronics , and Collaborative Robotics , alongside Physics and Applied Mathematics . Education: PhD (University of Oslo, 1996), MSc (University of Oslo, 1991). Prior Roles: Postdoc at University of Edinburgh (1996-98), Senior Knowledge Engineer at Computas AS (1998-2002), and teaching positions at Volda University College (2002-03). His research integrates Bayesian statistical inference with practical applications in energy systems, robotics, and materials science. Recent work includes modeling human trajectories in industrial settings using Ornstein-Uhlenbeck processes , EEG-EMG signal fusion for rehabilitation robotics, and Bayesian analysis of power grid stability . He has also published on ecological trends in Norwegian smooth snakes, linking climate change to reproductive behaviors. Scientific Awards and recognitions include authoring the textbook The Bayesian Way: Introductory Statistics for Economists and Engineers (2018) and its Norwegian counterpart Statistikk - en bayesiansk tilnærming (2016). He holds a certificate in university pedagogy (UniPed, 2009).

• Clinical Trials
• Medical Statistics
• Geriatrics
• +6 more

Dr. Nina Wilson is an active researcher at Newcastle University specializing in clinical trials methodology across diverse medical domains including geriatrics, stroke rehabilitation, dental public health, respiratory medicine, and dermatology. Her work focuses on pragmatic trial design, statistical analysis, and health services research with particular expertise in adaptive and non-inferiority trial frameworks. Research Focus Areas: Clinical trials in sarcopenia/frailty interventions (MET-PREVENT trial) Respiratory disease management (DIBS bronchiectasis trials) Stroke rehabilitation technology assessment (RATULS trial) Dental public health interventions for smoking cessation Dermatology biomarker research in psoriasis treatment Statistical methodology for expert elicitation and adaptive trials Her recent publication record (2021-2025) demonstrates consistent leadership in multicenter randomized controlled trials with emphasis on pragmatic trial design, resource planning for adaptive methodologies, and statistical innovation in outcome measurement. Key trends include growing focus on geriatric pharmacotherapy, respiratory disease management, and methodological contributions to trial economics and operational planning. Dr. Wilson has made significant contributions to major trials including MET-PREVENT for sarcopenia, DIBS for bronchiectasis, MultICath for urological care, and RATULS for stroke rehabilitation. Her collaborative work spans medical statistics, clinical implementation science, and health economics, with frequent partnerships across Newcastle University's medical research units.

Merrill LiechtyView profile

Clinical Professor

• Bayesian Statistics
• Portfolio Selection
• Higher Moment Estimation
• +5 more

Merrill Liechty is a Clinical Professor in the Decision Sciences and MIS department at Drexel University's LeBow College of Business . While his primary responsibility involves teaching statistics, his research emphasizes Bayesian statistics applied to portfolio selection, higher moment estimation, and social interaction analysis using functional Near-Infrared Spectroscopy (fNIRS). Research interests span Bayesian statistical modeling, financial econometrics, and computational methods for multivariate analysis. He has also pioneered studies on face-to-face human communication through non-obfuscated fNIRS data, co-founding Brytfish to translate these findings into practical conversation skill training. Publication trends reveal expertise in Bayesian portfolio optimization, MCMC algorithms, and statistical modeling for finance and supply chain reliability. His work frequently integrates theoretical rigor with real-world financial applications. 2016 : Fellow of the Institute of Strategic Leadership (LeBow College of Business) 2015 : Fellow of the Institute for Strategic Leadership 2004-2005 : Mini-Grant recipient (LeBow College Center of Teaching Excellence) Liechty has also served as a statistical consultant at Duane Morris LLP and Zeichner Ellman & Krause LLP, and previously held a corporate role as Chief Data Officer at Old Dominion Racing.

Michael MumaView profile

Professor

• Robust Data Science
• Signal Processing
• Machine Learning
• +5 more

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.