Simone Kelly is an Associate Professor at Bond Business School , Bond University, and serves as Associate Dean for Student Affairs & Service Quality. Her research focuses on finance, accounting, and commodity pricing, with emphasis on real options, corporate governance, and market dynamics in sectors like gold mining and carbon emissions trading. She is affiliated with the Centre for Data Analytics and actively mentors PhD students. PhD in Finance from the University of Queensland (2004) Key research areas: Real Options, Commodity Pricing, Corporate Governance Recipient of the KPMG Teaching Prize (1998) Member of the Accounting & Finance Association of Australia and New Zealand, Australian Society of CPAs, and Women in Finance Her recent work explores term structure models for carbon prices, oil, and copper commodities, utilizing state space and Monte Carlo simulation techniques. She has developed open-source software (LSMRealOptions, NFCP) to advance quantitative finance research. Awards include the 1998 KPMG Teaching Prize, and her research has been cited 326 times (Scopus h-index of 4).
Milena Shattuck is a Doctoral Lecturer and Advisor in the Department of Anthropology at Hunter College, part of the City University of New York (CUNY) system. She teaches the Human Biology Senior Capstone course and serves as an academic advisor to human biology majors within the School of Arts and Sciences. Dr. Shattuck's academic background includes: PhD in Anthropology from the University of Illinois, Urbana-Champaign BS in Biology from the University of California, San Diego BA in Biological Anthropology from the University of California, San Diego As a biological anthropologist, Dr. Shattuck's research focuses on understanding human evolution through multiple perspectives. Her primary interest lies in genetics, particularly as it relates to brain and behavioral evolution. She has conducted collaborative research incorporating behavior, endocrinology, morphology, life history, and cultural anthropology to better understand human biology. Her work often examines evolutionary patterns in vertebral morphology, longevity, and brain size across mammalian species, with particular attention to primates and hominins. Her research demonstrates an interdisciplinary approach that bridges biological anthropology, evolutionary biology, and comparative anatomy. Dr. Shattuck's publication record shows consistent research activity with a focus on evolutionary biology, particularly examining relationships between skeletal morphology, longevity, and brain evolution in mammals. Her work frequently combines genetic, morphological, and comparative approaches to understand evolutionary patterns. She has published in prestigious journals including Nature Ecology and Evolution, Proceedings of the National Academy of Sciences, and American Journal of Physical Anthropology. Dr. Shattuck serves as an advisor to human biology majors at Hunter College. Prior to joining Hunter College's Department of Anthropology as a doctoral lecturer in 2017, she worked as a visiting assistant professor at Duke University's Department of Evolutionary Anthropology and as an adjunct faculty member at both New York University and Hunter College. Her career path reflects the transition many academics make from research-intensive positions to teaching-focused roles in higher education.
Christos Dimitrakakis is a Full Professor of Data Science at the University of Neuchâtel, Switzerland, where he is affiliated with the Institute of Computer Science within the Faculty of Science. His research focuses on artificial intelligence, particularly reinforcement learning, fairness, and algorithmic privacy. He has held academic positions at several prestigious institutions including the University of Oslo, Chalmers University of Technology, and the University of Lille. His educational background includes: 1997: BEng from the University of Manchester 1998: MSc from the University of Essex 2006: PhD from IDIAP/EPFL under the supervision of Samy Bengio Professor Dimitrakakis specializes in reinforcement learning and decision making under uncertainty, with particular expertise in reverse reinforcement learning, preference elicitation, and human-AI collaboration. His research bridges theoretical foundations with practical applications, especially in autonomous systems and privacy-preserving AI. He has made significant contributions to understanding representations of uncertainty, differential privacy, and group fairness in algorithmic decision-making. His work often combines Bayesian inference with reinforcement learning frameworks to develop robust and fair AI systems that can operate effectively in uncertain environments. His recent publications demonstrate a strong focus on theoretical foundations of reinforcement learning while addressing practical challenges in fairness, privacy, and decision making. The research shows an evolving trajectory from core reinforcement learning techniques toward applications in fair and transparent AI systems, with increasing emphasis on real-world constraints and ethical considerations. His work spans multiple domains including game theory, matching markets, and bandit algorithms, reflecting an interdisciplinary approach to AI research. Notable scientific achievements include: Best Paper Award at Cooperative-AI@NeurIPS 2021 for "Interactive Inverse Reinforcement Learning for Cooperative Games" Student paper award at EAAMO'22 for "On Meritocracy in Optimal Set Selection" Professor Dimitrakakis actively mentors PhD students and has supervised numerous researchers who have gone on to academic and industry positions. His current research group at the University of Neuchâtel includes five PhD students working on reinforcement learning, fairness, and uncertainty in AI systems. He has secured funding for projects including a national initiative to develop intelligent bird deterrent systems for crop protection, demonstrating the practical impact of his research. His group collaborates with industry partners such as Agroscope, and he maintains academic connections with institutions worldwide. His research group at the University of Neuchâtel is actively working on interdisciplinary projects, most notably the development of an "intelligent scarecrow" system for bird pest control in agriculture. This project combines computer vision, reinforcement learning, and behavioral modeling to create adaptive AI systems that can minimize crop damage while accounting for bird behavior patterns. The group maintains expertise in both theoretical foundations and practical implementations of AI systems.
Dr. Guy Hawkins is an Associate Professor in the School of Psychological Sciences at the University of Newcastle, Australia. His research focuses on developing and testing computational and mathematical models of cognitive processes, with a primary interest in decision-making. His work spans from low-level speeded perceptual decisions through to high-level cognition, including statistical reasoning and consumer preferences. Dr. Hawkins earned his PhD in 2013 and his Bachelor of Psychology (Honours 1) in 2008, both at the University of Newcastle. Prior to his current position, he held postdoctoral research positions at the University of Amsterdam's Brain and Cognition Center (2014-2016) and UNSW Sydney (2013-2014). In 2017, he was awarded an Australian Research Council Discovery Early Career Researcher Award (DECRA). His research examines the decision mechanisms and strategies that people use to select consumer products and service options. A key finding is that people make fewer reasoning errors when information is presented as counts ('8 out of 10') rather than probabilities ('80%'). This has practical applications in healthcare, where physicians could describe prognoses using counts to help patients make better-informed treatment choices. His work also investigates how people update their decision caution relative to time constraints and option quality. Dr. Hawkins' recent publications reveal a strong focus on evidence accumulation models, decision-making under time pressure, and the relationship between cognitive processes and neural activity. His research often employs computational modeling approaches to understand the psychological processes underlying decision behavior. 2024 John Keats Early Career Award, Society for Mathematical Psychology 2020 William K. Estes Early Career Award, Society for Mathematical Psychology 2018 Fellow of the Psychonomic Society 2017 Australian Research Council Discovery Early Career Researcher Award 2017 Vice-Chancellor's Award for Early Career Research and Innovation Excellence, University of Newcastle 2013 Clifford T. Morgan Best Article Award, Psychonomic Society Dr. Hawkins collaborates extensively with researchers across the globe, including at universities in Australia, USA, Canada, UK, The Netherlands, and Norway. He is part of the Newcastle Cognition Laboratory and the Functional Neuroimaging Laboratory. His current research projects include investigating cognitive neuroscience frameworks for attentional control, evaluating human-machine interfaces in vehicles, and studying perceptual inference anomalies in schizophrenia.
Hui Zou is a Professor in the Department of Statistics at the University of Minnesota Twin Cities. His work spans advanced statistical methodologies and their applications in high-dimensional data analysis, machine learning, and 5G technology. Key Research Areas: High-Dimensional Statistics, Regularization, Sparse Principal Component Analysis, Censored Regression, and Neurobehavioral Statistics. Notable Collaborations: National Science Foundation (NSF) grants for projects like evolutionary modeling of 5G measurements and inference procedures for high-dimensional regression. Recent publications focus on regularization techniques, 5G throughput prediction, and robust regression methods. His work contributes to UN Sustainable Development Goals through technological and statistical innovation. Grants: Multiple NSF-funded projects (2022-2026, 2020-2023, 2019-2023, etc.) on statistical modeling and high-dimensional inference. Collaborative Efforts: Work with researchers like Ding, Qian, Zhang, and Cook on 5G networks and robust regression models.
Aarya Patil is an LSST Discovery Alliance Catalyst Fellow at the Max Planck Institute for Astronomy in Heidelberg, Germany. She specializes in large-scale data-driven studies of the Milky Way's formation and evolution, leveraging computational methods and open-source software development. PhD in Astronomy & Astrophysics (University of Toronto) Key contributor to Astropy project (finance committee member) Active in science education through Astropy Training School and Pan-African School for Emerging Astronomers Research Focus Aarya's work combines galactic astrophysics with statistical computing , particularly through: Functional Principal Component Analysis (FPCA) of stellar spectra Chemical tagging validation via spectral structure analysis Development of open-source tools like fpca.py and delfiSpec Application of Sequential Neural Likelihood (SNL) for stellar parameter inference Scientific Contributions specdims repository implements methods to extract intrinsic spectral features while accounting for systematics, enabling studies of chemical homogeneity in galactic structures like the M67 open cluster. Awards & Recognition Data Sciences Institute Doctoral Student Fellowship (University of Toronto) Google Summer of Code participant (2017) and mentor (2021) Community Engagement Active in open science initiatives, Aarya serves on the Astropy project's finance committee and organizes educational programs bridging data science and astronomy.
Joshua S. Speagle is an Assistant Professor at the University of Toronto , specializing in computational astrophysics, Bayesian statistics, and machine learning applications to astronomical problems. His work bridges theoretical modeling with practical software development, notably as the creator of the dynesty package for dynamic nested sampling in Bayesian inference. Research Interests : Computational astrophysics and statistical methods Bayesian inference and dynamic nested sampling Stellar evolution and galaxy formation Machine learning for astronomical data analysis Recent Publications (2025–2024) focus on generative models for inverse problems, galaxy formation at high redshift, stellar oscillation analysis, and simulation-based inference techniques. His software tools like dynesty and brutus are widely used in the astronomical community for Bayesian posterior and evidence estimation. Contact: j.speagle@utoronto.ca | Personal Website | ORCID: 0000-0003-2573-9832
Anders Rahbek is a Professor at the Department of Economics, Faculty of Social Sciences, University of Copenhagen. He has held this position since 2007 and has also served as a visiting Professor at Oxford University during Hilary Terms 2011-2012. His academic career at the University of Copenhagen spans from Assistant Professor (1996-1999) to Associate Professor (1999-2007) and finally to Professor (2007-present). Education: PhD in Econometrics, Institute of Mathematical Sciences (IMF), University of Copenhagen, 1996 Cand.Scient.Oecon (M.Phil), Mathematics and Economics, IMF, 1992 MSc in Econometrics, London School of Economics, 1991 MA in Mathematics, University of Pennsylvania, 1988 Professor Rahbek's research focuses on financial econometrics and time series analysis , with particular expertise in bootstrap methods, GARCH and volatility modeling, cointegration analysis, duration modeling, and count models. His work bridges theoretical econometrics with practical applications in financial and macroeconomic data analysis. He has developed innovative approaches for analyzing time series with time-varying volatility and has made significant contributions to bootstrap methodology in econometrics. His recent publications (2020-2025) demonstrate a continued focus on boundary problems in statistical testing, bootstrap methodology for complex time series models, and applications to financial volatility modeling. Key themes include GARCH-X models, cointegration in high-dimensional settings, Hawkes processes, and threshold autoregressions. His work often involves collaboration with leading econometricians like Giuseppe Cavaliere, Heino Bohn Nielsen, and Rasmus S. Pedersen. Scientific Awards: NYKREDIT RESEARCH AWARD (2014) Research Prize 2012: Reinholdt W. Jorck and Wife's Foundation (2012) Professor Rahbek has secured significant research funding as Principal Investigator, including multiple DFF-Advanced Grants (2012-2026) focusing on bootstrap methods and duration models in econometrics. He serves as Associate Editor for Econometric Theory and has previously held editorial positions at Econometrics Journal, Scandinavian Journal of Statistics, and Journal of Time Series Analysis. His Google Scholar h-index stands at 30 (as of December 2023). He is actively involved in international research networks, having initiated the Econometric Time Series European Research Network (ETSERN) in 2008. His teaching includes Financial Econometrics, Advanced Econometrics, and introductory Econometrics courses, with focus on volatility models, cointegration, and likelihood-based methods.
Rasmus Søndergaard Pedersen is Associate Professor at the Department of Economics, University of Copenhagen , Faculty of Social Sciences. His research focuses on financial and time-series econometrics, with special emphasis on heavy-tailed distributions, time-varying volatility and GARCH-type models. Education: Ph.D. in Economics, University of Copenhagen (2012–2015) M.Sc. in Economics (cand.polit.), University of Copenhagen (2010–2012) Exchange student, University of California, San Diego (2010–2011) B.Sc. in Economics, University of Copenhagen (2006–2010) Research Interests: His work spans time-series econometrics, theoretical econometrics, financial econometrics, multidimensional time-series models, GARCH and BEKK specifications, heavy-tailed processes, bootstrap inference on parameter boundaries, high-dimensional VARs and robust inference in financial markets. Scientific Awards: Winner, Econometric Game 2012 Selected Young Economist, 5th Lindau Meeting on Economic Sciences Research Visits & Collaboration: Visiting researcher, Imperial College London (Jan–Jun 2014) Active participant in Econometric Society World Congress, Lindau Meetings, EC² conferences and numerous workshops on time-series econometrics Teaching & Guidance: He teaches Econometrics C and Financial Econometrics A at the University of Copenhagen and serves on assessment committees and research networks.
Frazier Bindele serves as an Associate Professor in the Department of Mathematics and Statistics at the University of South Alabama's College of Arts and Sciences. His academic profile centers on developing advanced statistical methodologies with practical applications across diverse research domains. His educational foundation includes: Ph.D. in Statistics from Auburn University (2012) MS Diploma in Mathematics from the International Centre for Theoretical Physics (ICTP) (2007) M.S. in Mathematics from Marien Ngouabi University (2004) B.S. in Mathematics from Marien Ngouabi University (2003) Dr. Bindele's research specializes in Nonparametric Statistics and Robust Estimation, with particular emphasis on rank-based and signed-rank methodologies. He develops innovative approaches for regression models under complex data scenarios, especially those involving missing responses and covariates. His work bridges theoretical rigor with practical implementation, addressing challenges in single-index models, varying coefficient frameworks, and functional linear modeling where traditional methods fail under non-ideal conditions. Analysis of his publication trajectory reveals a concentrated focus on robust statistical inference for missing data problems (both MAR and MNAR mechanisms), with significant contributions to distribution theory through novel distribution families. His recent work demonstrates increasing sophistication in handling high-dimensional settings and developing computationally efficient algorithms for real-world data analysis, while maintaining theoretical guarantees of robustness.
Professor Randy J Read FRS is a prominent structural biologist at the Cambridge Institute for Medical Research (CIMR), University of Cambridge, where he leads research in structural biology methods and applications. He holds a position in the Department of Haematology and maintains the Structural Medicine research group focused on protein structure determination through X-ray crystallography and cryo-electron microscopy. His work has significantly advanced computational methods for macromolecular structure determination. Read's research interests span structural biology methodology development, particularly maximum likelihood approaches for protein crystallography. His group developed the Phaser software, which has become a standard tool in structural biology. His research also encompasses structural studies of medically-relevant proteins, including bacterial toxins like pertussis toxin and Shiga-like toxins, as well as serpins and other proteins involved in disease processes. His work bridges computational method development with biological applications, focusing on how protein structure informs function in disease contexts. His recent publications demonstrate continued leadership in structural biology methodology, with significant contributions to integrating AlphaFold predictions with experimental approaches, likelihood-based methods for cryo-EM data analysis, and advanced molecular replacement techniques. The Phaser software continues to evolve as a critical tool for the structural biology community. Fellow of the Royal Society (FRS) Wellcome Trust Principal Research Fellow Extensive contributions to structural biology methodology Author of over 150 publications in top scientific journals Read supervises research staff and students, with notable group members including Airlie McCoy and Alisia Fadini. His laboratory receives funding from the Wellcome Trust and the National Institutes of Health. His work has established him as a leading figure in the development of computational methods for protein structure determination, while maintaining strong connections to medically-relevant biological questions.
Prof. Dr. Christiane Fuchs is a full professor at the Faculty of Economics of Bielefeld University and heads the Data Science Group . She is also leading the Biostatistics Research Group and Core Facility Statistical Consulting at Helmholtz Munich . Her academic affiliations include the Bielefeld Graduate School of Economics and Management and the Bielefeld Center for Data Science (BiCDaS) . Education : MSc in Computational Modeling, Brunel University West London (2003) Diploma in Mathematics with Computer Science, University of Hanover (2005) PhD in Statistics, Ludwig Maximilian University of Munich (2010) Research interests span stochastic modeling , Bayesian inference , uncertainty quantification , and statistical applications in economics, medicine, and epidemiology. She specializes in diffusion processes , high-dimensional data analysis , and integrated statistical methods for cross-domain data (genomics, clinical, environmental). Recent publications focus on AI-driven clinical decision support systems , spatial epidemiology , fractional diffusion modeling , and statistical serology validation . Her work bridges methodological innovation in Bayesian statistics with real-world applications in infectious disease dynamics and hematological malignancies . Principal investigator in third-party funded projects from DFG , BMBF , NIH , and Helmholtz Association , including the UQ Consortium (2019-2024) and KoCo19 prospective COVID-19 cohort (2020-2024). She has developed statistical software packages like stochprofML and adaSC3 , and contributed to network-regularized regression methods. As Vice Rector for Research and Networking at Bielefeld University since 2023, she drives institutional research strategy while maintaining active roles in scientific societies including the International Society for Bayesian Analysis and Deutsche Statistische Gesellschaft .
Federico A Veneri Guarch is a Uruguayan Fulbright scholar, economist, and statistician affiliated with the Department of Statistics at Iowa State University as a Researcher (2019–Present). He joined the department as a Ph.D. student in 2019 and contributes to CSAFE (Center for Statistics and Applications in Forensic Evidence) with simulation experiments and machine learning applications in forensic contexts. Education: PhD in Statistics (2024) from Iowa State University MSc in Statistics (2021), Iowa State University MSc in Mathematical Engineering (2019), Universidad de la Republica (UdelaR) BSc in Statistics (2018) and BSc in Economics (2013), both from UdelaR Research interests span statistical modeling , policy evaluation , and machine learning applications , particularly in forensic statistics and score likelihood ratios (SLR) . His work includes evaluating SLR methods for forensic evidence assessment and developing ensemble approaches to address complex dependence structures in common source problems. Publications highlight his focus on statistics , machine learning , and forensic science , including submissions on SLR evaluation and ensemble systems for forensic evidence. These works intersect statistical foundations , performance metrics , and probative value assessment . Scientific awards and scholarships : Statistical Significance Award (2022) - 2nd place at JSM Fulbright Scholarship (2019) sponsored by the U.S. and ANII Uruguay ANII National Graduate Scholarship (2016) from Uruguay Professional experience includes: Graduate Research Assistant at CSAFE (2020–Present): Developed code for simulation experiments, implemented performance metrics for SLR, and presented at ASA-JSM and AAFS conferences Graduate Teaching Assistant (2019–2020): Assisted in Stat 326 and Stat 526 courses External Consultant at Inter-American Development Bank (2016–2019): Led data collection across Latin America and the Caribbean, created a government expenditure database on citizen security, and developed ML algorithms for domestic violence prevention Research Assistant & Consulting at CINVE (2013–2016): Provided consultancy to government and industry, developed scoring tools for banking institutions, and conducted policy impact evaluations Skills include R , Python , and statistical analysis . He has also contributed to hotspots policing studies in Uruguay and data science tools workshops .
Prof. Ahmet Sermet Anagün serves as Professor and Head of the Department of Industrial Engineering at Izmir University of Economics, Faculty of Engineering since September 2017, with administrative leadership beginning in 2019. Previously, he held equivalent roles at Eskişehir Osmangazi University (2015-2017) following a 35-year academic progression from Research Assistant to full Professor. His educational credentials include: Ph.D. in Industrial Engineering, Cleveland State University (1993) M.S. in Industrial Engineering, Anadolu University (1985) B.S. in Industrial Engineering, Anadolu University (1982) Research focuses on Quality Control and Statistical Analysis, with significant contributions to fuzzy logic applications in control charts and neural networks for biosignal processing. His methodology integrates experimental design, Taguchi techniques, and machine learning for industrial optimization problems spanning manufacturing, environmental engineering, and educational technology. Analysis of his 14 most recent publications reveals dominant trends in fuzzy control systems (60% of works) applied to quality assurance, with emerging interdisciplinary research in biosurfactant production and educational technology analysis using CHAID methods. His approach consistently bridges statistical theory with practical engineering solutions. No scientific awards were documented in the provided materials. Prof. Anagün actively supervises Master's theses and senior capstone projects across engineering disciplines, teaching core courses in Statistical Quality Control, Engineering Statistics, and Human Factors Engineering. His administrative service includes University Erasmus Coordination and KOSGEB representation, though specific grant funding details remain unlisted.
Eva María Arias de Reyna Domínguez serves as a Full Professor in the Department of Signal Theory and Communications at the University of Seville. Her research is centered within the Signal Processing and Communications research group (TIC-155), where she has led numerous national and international projects focused on advanced signal processing techniques for wireless communications and localization systems. Her research interests span Signal Processing , Wireless Communications , and Ultra-Wideband Localization , with particular expertise in Expectation Propagation algorithms, UWB signal processing, and crowd-based learning for IoT applications. Her work bridges theoretical signal processing with practical implementations in digital communications and indoor positioning systems. Analysis of her 15 most recent publications reveals a consistent focus on Expectation Propagation techniques for digital communications (constituting 40% of recent work), UWB localization algorithms (30%), and channel equalization methods (20%). Her research demonstrates a progression from fundamental signal processing algorithms toward IoT-integrated spatial field estimation and machine learning applications. She has advised doctoral student Irene Santos Velazquez (2018 thesis on Expectation Propagation for digital communications) and participated in significant research projects including ATENEA (Artificial Intelligence for Art Fabric Analysis), Finite-Length Iterative Decoding, and multiple national grants under Spain's TEC and CSD programs. Her laboratory work centers on the Signal Processing and Communications research group, which has received continuous consolidation funding from 2005-2017.
