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Amanda LubyView profile

Assistant Professor

• Forensic Statistics
• Bayesian Methods
• Item Response Theory
• +3 more

Amanda Luby is an Assistant Professor of Statistics at Carleton College, affiliated with the Department of Mathematics and Statistics. She holds a PhD in Statistics from Carnegie Mellon University (2019) and previously taught at Swarthmore College for five years. Her research focuses on statistical methods for forensic decision-making, particularly applying Item Response Theory (IRT) to fingerprint and firearm analysis. She advocates for improving statistical education by integrating computing and theory in courses, and actively collaborates on criminal justice projects. Key research interests include forensic statistics, Bayesian methods, and pedagogical innovation. She co-authored a 2024 paper addressing methodological flaws in forensic firearm comparison studies and received the 2023 Stephen E Fienberg Young Investigator Award for her work on firearms analysis with Erwin Mattijssen. Upcoming activities include presentations at ENAR 2025 and JSM 2025 on data-driven justice and forensic science advancements. Dr. Luby leads a research group at Carleton and contributes to the Center for Statistics and Applications in Forensic Evidence (CSAFE). She teaches courses like Data Science and Statistical Inference, emphasizing applied statistical techniques. Her work bridges academic research and real-world applications in criminal justice systems.

Pamela ShawView profile

Adjunct Associate Professor

• Measurement Error
• Survival Analysis
• Clinical Trials
• +4 more

Pamela Shaw is an Adjunct Associate Professor at the University of Pennsylvania's Department of Biostatistics, Epidemiology, and Informatics, and a Senior Investigator at the Kaiser Permanente Washington Health Research Institute. She holds a PhD in Biostatistics from the University of Washington and has extensive experience in clinical trials, measurement error correction, and survival analysis. Her research focuses on improving statistical methods for observational studies and electronic health record (EHR) data, addressing covariate and outcome errors in HIV/AIDS and chronic disease studies. She co-authored the textbook Essentials of Probability Theory for Statisticians (2016). Education: PhD in Biostatistics, University of Washington, 2006 MS in Mathematics, University of Washington, 1994 BA in Mathematics and French, Grinnell College, 1990 Research Interests: Dr. Shaw specializes in methodological advancements for error-prone data, including measurement error correction in nutritional epidemiology, survival analysis with correlated errors, and optimal study designs for two-phase sampling. Her work bridges biostatistics and clinical application, with a focus on infectious diseases, cancer immunotherapy, and public health outcomes. Grants & Collaborations: NIH R01 Grant: Statistical Methods for Correlated Outcome and Covariate Errors in HIV/AIDS Studies PCORI Methods Grant: Addressing Errors in EHR-Based Studies Labs/Teams: Leads the Shaw-Shepherd Research Collaborative, advancing statistical methods for observational data quality and audit-driven inference.

Peter JonesView profile

Associate Professor

• Cognitive Psychology
• Behavioral Neuroscience
• Learning and Memory
• +3 more

Dr. Peter Jones is an Associate Professor in Psychology at the University of Plymouth, within the School of Psychology under the Faculty of Health. He leads the undergraduate Psychology programs and supervises PhD students in areas such as cognition, learning, and health behavior. His teaching focuses on cognitive psychology, biological psychology, and psychobiological aspects of health. Research interests include associative learning mechanisms, spatial cognition, and the neural basis of memory. Notable work involves studies on perirhinal cortex function, redundancy effects in causal learning, and theory protection in human cognition. His interdisciplinary contributions span forensic anthropology, educational technology, and particle physics collaborations. Recent articles highlight investigations into neural mechanisms (e.g., perirhinal cortex lesions), educational frameworks for digital engineering, and collaborative efforts in war casualty identification. While primarily a psychologist, his work occasionally intersects with chemistry, geology, and meteorology, reflecting a broad scientific curiosity. No scientific awards are explicitly noted. Dr. Jones has advised three completed PhD students: Tara Zaksaite (2017), Stuart Spicer (2020), and Abbie Ball (2020). His research also extends to healthcare adherence studies and rheumatoid arthritis patient experiences, demonstrating a commitment to applied psychological and medical research.

Heike HofmannView profile

Professor

• Statistical Visualization
• Forensic Data Analysis
• Machine Learning for Forensics
• +4 more

Heike Hofmann is a Professor in the Department of Statistics at the University of Nebraska-Lincoln. Her research spans statistical visualization, forensic data analysis, and machine learning applications in crime scene evidence evaluation. Key contributions include developing interactive frameworks for forensic bullet comparisons, advancing reproducibility in toolmark analysis, and creating explainable machine learning pipelines. Email: hhofmann4@unl.edu Research Interests focus on: Visual statistical inference and human factors in data interpretation 3D imaging and algorithmic matching for firearms/toolmark identification Psychometrics and mixed-effects modeling Open-source software development for statistical analysis (e.g., R packages) Recent Work Trends (2021-2025) emphasize: Forensic error rate quantification Interactive visualization tools (e.g., ggplot2 extensions) Machine learning integration in forensic science Large-scale data graphics validation for public understanding Automated matching algorithms for bullets/cartridge cases

Albert VexlerView profile

Professor

• Biostatistics
• Epidemiology
• Statistical Methods
• +7 more

Albert Vexler, PhD, is a Professor in the Department of Biostatistics at the University at Buffalo, within the School of Public Health and Health Professions. He specializes in advanced statistical methodologies with applications to biomedical and health-related research. His work focuses on biomarker analysis, nonparametric testing, ROC curve methodologies, and empirical likelihood techniques. Education: PhD in Statistics and Probability Theory from the Hebrew University of Jerusalem (2003), followed by a Postdoctoral Research Fellowship at the National Institute of Child Health and Human Development (2004–2007). Research Interests: Dr. Vexler’s research spans biostatistical methods, including optimal designs, censored data analysis, sequential analysis, and Bayesian approaches. He develops tools for handling complex data structures such as skewed biomarker measurements and limits of detection in clinical studies. His work emphasizes robust statistical inference and practical applications in clinical trials and public health. Publications: His recent work highlights advancements in hypothesis testing, biomarker evaluation, and statistical computing. Key areas include density-based empirical likelihood tests, multivariate dependence measures, and computational tools for analyzing biomedical data. Professional Activities: He serves as an Associate Editor for Biometrics and Journal of Applied Statistics , and has reviewed for top-tier journals such as the Journal of the American Statistical Association and Biometrika . He contributes to statistical software development, including R packages like dbEmplikeGOF and testforDEP . Lab/Teams: Leads a research group focused on innovative statistical methods for health sciences, collaborating on projects involving clinical trial design, epidemiological modeling, and precision medicine.

Milan DespotovićView profile

Professor

• Renewable Energy Systems
• Solar Energy Forecasting
• Energy Efficiency in Buildings
• +3 more

Milan Despotović serves as a Professor in the Department for Energy and Process Engineering at the Faculty of Engineering, University of Kragujevac. His academic career spans over a decade since his election in 2012, with research deeply rooted in energy systems and sustainable engineering practices. His primary research focuses on solar energy forecasting , renewable energy integration , and energy efficiency optimization across building systems and industrial processes. Notable work includes developing advanced metrics for solar irradiance prediction, analyzing electric vehicle adoption barriers in Serbia, and optimizing thermal performance in sports facilities repurposed as emergency warming shelters. His methodology frequently combines statistical modeling , artificial intelligence , and thermodynamic analysis to address energy challenges. Analysis of his recent publications reveals a strong trend toward hybrid forecasting systems that integrate machine learning with meteorological data, particularly for short-term solar irradiance prediction. His work increasingly addresses practical implementation challenges in Serbian energy infrastructure, including grid integration of renewables and sustainable transportation adoption. The interdisciplinary nature of his research bridges traditional process engineering with modern computational techniques. Professor Despotović maintains active research output through the SciDAR repository and contributes to the Faculty's mission in energy engineering education. His office (B-III-9) and direct contact (+381 34 335 990 ext. 730) indicate ongoing engagement with academic activities at the Kragujevac campus.

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.

Liat LevanonView profile

Professor

• Evidence Law
• Criminal Justice Theory
• Statistical Evidence
• +4 more

Liat Levanon is a Reader in Evidence Law and Philosophy at The Dickson Poon School of Law, King's College London. She holds a PhD from the University of Cambridge and an LLB from the Hebrew University of Jerusalem. Previously, she was a lecturer at Brunel University, held a post-doctorate at the University of Zurich's Faculty of Law, and was a visiting scholar at the Max Planck Institute for Foreign and International Criminal Law. Her career also includes work at the Israel Democracy Institute. Her research focuses on evidence law theory (especially truth-finding mechanisms), statistical evidence interpretation, probabilistic reasoning in legal contexts, justification of criminal punishment, restorative justice, and sentencing procedures. She co-edits the Criminal Justice Theory Blog with Mark Dsouza (UCL) and teaches Evidence Law, Criminal Law, and Penology. Levanon's publications critically analyze forensic evidence reliability, legal epistemology, and the ethical foundations of punishment. Recent work explores fingerprint analysis in trials and the relationship between evidence, respect, and truth in judicial processes. Her earlier studies address police entrapment, sexual assault evidence practices, and anti-terrorism legislation. She currently supervises PhD candidates in law and philosophy. While no specific grants or awards are listed, her extensive academic output demonstrates sustained engagement with legal theory and practice. Liat's office is located at SW3.12 Somerset House East Wing, London WC2R 2LS, with office hours Monday 11:00-12:00 and Thursday 12:00-14:00

Carina GeldhauserView profile

Researcher

• Artificial Intelligence
• Climate Modeling
• Mathematical Analysis
• +7 more

Carina Geldhauser is a Senior Scientist at ETH Zurich's Department of Mathematics, with affiliations spanning institutions like Lund University (Faculty of Engineering) and the University of Sheffield (School of Mathematics and Statistics). She holds a PhD in Applied Mathematics from the University of Bonn and a dual degree in Mathematics and Protestant Theology. Her research bridges fundamental mathematical analysis, stochastic processes, climate modeling, and AI ethics. Key research areas include machine learning reliability, stochastic models of turbulence, and ethical implications of technology. She has pioneered work on point vortex models in fluid dynamics and CO2 emissions monitoring. Her interdisciplinary projects include applying machine learning to biblical manuscripts and exploring the 'algorithmizability' of ethics. Awarded the Global Young Academy membership (2022), Falling Walls Science Summit recognition (2024 diversity track), and the TU Dresden Graduate Academy award (2019). She has led funded projects on sustainable development and stochastic turbulence models, securing grants like FORMAS' 3-year initiative (2022) and the Crafoord Foundation's support (2021). Geldhauser is deeply engaged in academia: teaching applied mathematics, mentoring through the International Mathematics Master (IMM), and advocating for equal opportunities. She volunteers with Studies Without Borders, focusing on scholarships for disadvantaged students, and communicates mathematics via science slams and IMAGINARY projects.

Ralf MöllerView profile

Professor

• Artificial Intelligence
• Humanities-Centered AI
• Causal and Probabilistic Models
• +5 more

Prof. Ralf Möller is a Professor of Artificial Intelligence in Humanities at the University of Hamburg's Faculty of Humanities, Department of Philosophy. He leads the Institute of Humanities-Centered Artificial Intelligence (CHAI) and serves as spokesperson for the 'Data Linking' research field within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA, 2019–2025). His research focuses on intellectics, causal and probabilistic-relational models, and AI applications in humanities, emphasizing sustainable data management and multimodal foundation models. He heads the Data Linking Lab and the CHAI Institute, overseeing projects like the TAMAR initiative for manuscript research. His work bridges technical AI advancements with humanities needs, addressing challenges in data curation, federated information systems, and ethical AI integration. He contributes to interdisciplinary collaborations, including ethics committees and cultural heritage preservation initiatives. Key research themes include lifted inference in probabilistic graphical models, temporal data prediction, and synergistic OCR-LLM systems for damaged documents. His projects emphasize scalability, sustainability, and human-centered design principles. Supervising doctoral candidates in AI and humanities intersections, he advocates for AI systems grounded in cultural and ethical considerations. Labs/Teams: CHAI Institute, Data Linking Lab. Current Projects: UWA Cluster (2019–2025), Data Linking Infrastructure development, Humanities-Centered AI applications. Grants include leadership roles in institutional and collaborative research funding.

• Cognitive Systems
• Learning Analytics
• Computational Modelling
• +7 more

Andrew Gibson is a Senior Lecturer in Information Science at the School of Information Systems, Faculty of Science, Queensland University of Technology (QUT). His research focuses on cognitive systems, learning analytics, and the intersection of technology with wellbeing. He holds a PhD in Information Science and has extensive industry experience in IT, education, and startups. Education: PhD in Information Science (QUT) Bachelor of Educational Studies (University of Queensland) Postgraduate Diploma in Information Technology (QUT) Diploma in Teaching (Brisbane College of Advanced Education) Teaching: Unit Coordinator for IAB303 (Data Analytics for Business Insight) Unit Coordinator for IFN619 (Data Analytics for Strategic Decision Makers) His research interests include cognitive reflexivity, quantum-like models, and the application of classical pragmatism to learning analytics. He leads projects such as 'Improving Outcomes through Accessible Assessment and Inclusive Practices' funded by an Australian Competitive Grant. He has supervised multiple PhD students and is currently accepting new research students. Key awards include the 2017 Best Full Paper at LAK17 for 'Reflective Writing Analytics for Actionable Feedback.' He is affiliated with QUT's Centre for Data Science and develops open-source tools like GoingOK for reflective journaling.

Guillaume PirotView profile

Research Fellow

• Subsurface Characterization
• Spatial Statistics
• Bayesian Inference
• +4 more

Guillaume Pirot is a Research Fellow at the School of Earth and Oceans, University of Western Australia. He specializes in subsurface characterization, spatial statistics, and Bayesian inference, with a focus on uncertainty quantification in geological models. His work integrates geophysical data inversion and computational methods to address challenges in hydrogeology and resource exploration. Currently, he co-coordinates Work Package 5 of the Loop project, focusing on uncertainty management in multi-disciplinary modeling frameworks. Education: PhD in Hydrogeology (University of Neuchâtel, 2015), focusing on stochastic heterogeneity modeling of braided river aquifers. Prior experience includes 7 years as an IT consultant in retail before transitioning to academia. Research interests span 3D geological modeling, spatial statistics, Bayesian inversion, and the rigorous treatment of uncertainty across data acquisition to predictive modeling. His contributions include open-source tools like dh2loop and Blockworlds for geological data processing and uncertainty quantification. Scientific awards include the MinEx CRC 2022-2023 Publication Prize and Prix Adrien Guebhard-Severine (2015). He collaborates extensively on projects involving geophysical integration, probabilistic inversion, and multi-disciplinary modeling platforms like Loop. Advising and grants: Co-supervises PhD students within the Loop project framework. Involved in grants such as the ARC-funded Loop 3D Linkage-2 project (2023-2026). His work emphasizes translational research bridging computational methods with practical geological exploration challenges. Labs/Teams: Active contributor to the Loop project team, developing tools for uncertainty-aware geological modeling. Collaborates with interdisciplinary groups in geophysics, hydrology, and computational science.

Darrell WorthyView profile

Associate Professor

• Reinforcement Learning
• Decision-Making
• Categorization
• +11 more

Dr. Darrell Worthy is an Associate Professor in the Department of Psychology at Texas A&M University, affiliated with the Neuroscience & Personality Processes research cluster. His work integrates behavioral, computational, and neuroscience methods to study learning and decision-making. Key research areas include reinforcement learning, decision-making under uncertainty, and the impact of clinical issues like substance abuse and depression on cognitive processes. He explores both basic cognitive mechanisms and individual differences, bridging sub-disciplines through neuroscientific approaches. His research employs formal mathematical models and fMRI to test hypotheses about cognitive processes. Notable contributions include studies on frequency-based decision-making, the inverse base-rate effect, and the effects of acute stress on decision strategies. He is actively involved in mentoring students, with current openings for 2024-2025 academic year. Scientific achievements include the 2016 Best Article award in Cognitive, Affective, and Behavioral Neuroscience. His lab (Worthylab) focuses on strategic decision-making models, with recent work on reward segmentation and category learning. Collaborative projects address topics like zoonotic disease communication and dopamine's role in decision-making.

Jason ScholzView profile

Professor

• Strategy, management and organisational behaviour
• International and comparative law
• Applied ethics
• +3 more

Professor Jason Scholz is an Innovation Professor at RMIT University's Research & Innovation Capability department. His research focuses on ethical AI, autonomous systems, decision-making frameworks, and their applications in defense, humanitarian contexts, and organizational behavior. He has contributed to developing trustworthy AI methodologies and addressing ethical challenges in technology integration. His research interests span strategy and organizational behavior, international law, applied ethics, data science, and machine learning. Key areas include AI ethics in defense, policy automation in command and control systems, and humanitarian implications of autonomous weapons. His work often bridges technical innovation with ethical and legal considerations. Recent publications explore frameworks for research impact assessment, trusted human-AI networks, and ethical AI in defense. He has also authored foundational texts on trusted autonomy and error modeling in machine learning. His interdisciplinary approach addresses global challenges in technology governance and decision-making processes. No scientific awards or grants are explicitly listed in the provided texts. He advises no students, and specific lab affiliations or teams are not mentioned.

Stanton HudjaView profile

Assistant Professor

• Experimental economics
• Behavioral economics
• Computational economics
• +1 more

Stanton Hudja is an Assistant Professor of Business Analytics and Strategy at Illinois Institute of Technology. He holds a Ph.D. in Economics from Purdue University (2020), an M.S. in Economics from Johns Hopkins University, and dual undergraduate degrees in Economics and Mathematics from the University of Maryland. His research focuses on experimental economics, particularly exploration-exploitation tradeoffs and decision-making under uncertainty. Current projects investigate collective experimentation dynamics, information asymmetry effects, and legal decision-making processes. His academic work spans experimental methodologies applied to criminal justice systems, innovation contests, and behavioral policy design. Key themes include understanding how individuals balance exploration of new options versus exploitation of known strategies, and the implications for economic systems and legal frameworks. Research outputs include analyses of prosecutor discretion, jury decision thresholds, and gender effects in legal error tolerance. His recent work (2023–2025) emphasizes multi-agent experimentation models, public goods provision mechanisms, and algorithmic decision-making frameworks derived from experimental economics principles. Teaching responsibilities include courses on business analytics and strategy at the Mies (Main) Campus. Office hours for Fall 2025 combine on-campus and virtual availability.