Brooke Magnus is an Associate Professor in the Department of Psychology and Neuroscience at Boston College. She earned her PhD in Psychology (with a minor in Biostatistics) from the University of North Carolina at Chapel Hill's L.L. Thurstone Psychometric Laboratory. Her research focuses on psychometric model development for clinical and health outcomes, including item response theory (IRT) applications to survey data. She teaches statistics courses and mentors graduate students applying psychometric methods to substantive research areas. Her work emphasizes improving measurement practices in clinical settings, particularly through zero-inflated models for symptom data and IRT-based instrument validation. Key research areas include traumatic brain injury outcomes, neurodivergent youth bullying assessment, and pediatric health measurement. She collaborates across psychology, medicine, and public health disciplines. Recent work highlights advancements in TBI severity characterization, concussion assessment tool comparisons, and autism spectrum disorder psychometric analyses. Her methods bridge quantitative psychology and biostatistics to address gaps in clinical measurement precision.
Adam Zaremba is an Associate Professor in the Finance & Accounting department at MBS. His expertise spans asset pricing, investment strategies, portfolio management, international finance, and capital allocation. Prior to joining MBS in 2020, he was affiliated with the University of Dubai (UAE) for three years. Education: PhD in Finance from Poznan University of Economics and Business (2012) His research integrates artificial intelligence with financial markets, focusing on factor investing, cryptocurrency dynamics, ESG strategies, and climate finance. Recent work includes machine learning applications in factor return prediction, cross-sectional cryptocurrency interactions, and non-standard errors in crypto analytics. Themes in his publications highlight advancements in momentum investing , capital allocation , and risk modeling across traditional and emerging markets. He has contributed to journals like the Journal of Finance , Journal of Financial Economics , and Review of Finance .
Florent Krzakala is a Full Professor at École polytechnique fédérale de Lausanne (EPFL) in Switzerland, holding positions across multiple departments including the School of Basic Sciences (SB), School of Engineering (STI), and specifically within the Department of Physics (IPHYS) and Department of Electrical Engineering (IEM). He leads the Information, Learning and Physics Laboratory (IdePHICS) and maintains an office at ELD 239, Station 11, 1015 Lausanne. His research bridges statistical physics and computational disciplines, with significant contributions to understanding the theoretical foundations of machine learning and optimization problems. Dr. Krzakala received his MSc in Physics from Orsay, France in 1999, followed by a PhD in Statistical Physics from Orsay, Paris XI, France in 2002, and completed a postdoctoral position at Roma La Sapienza in 2004. This strong foundation in physics has informed his interdisciplinary approach to computational problems. His research interests span Statistical Physics, Machine Learning, Probability and Statistics, Computer Science, Information Theory, Inference on Graphs, Random Constraint Optimization, and Computational Optics. Krzakala's work focuses on applying methods from statistical physics to problems in theoretical computer science, probability, and machine learning. He investigates how concepts from disordered systems and phase transitions can illuminate computational barriers in optimization and inference tasks. His research has particular relevance for understanding the behavior of neural networks, compressed sensing, and high-dimensional statistical models. Analysis of his recent publications reveals a strong trend toward understanding the fundamental limits of learning in high-dimensional settings, with particular emphasis on phase transitions, statistical-to-computational gaps, and the theoretical properties of deep learning architectures. His work frequently bridges rigorous mathematical analysis with practical machine learning applications, demonstrating how insights from statistical physics can inform algorithm design and theoretical understanding in AI. Krzakala actively mentors the next generation of researchers, supervising numerous PhD students whose work continues to advance these interdisciplinary fields. His laboratory serves as a hub for researchers exploring the intersection of physics and computation, fostering collaborations across traditional disciplinary boundaries. He teaches advanced courses including Fundamentals of Inference and Learning, Statistical Physics, and Statistical Physics for Optimization & Learning, which examine the connections between physical principles and computational methods. His educational materials, including lecture notes on statistical physics methods in optimization and machine learning, have become valuable resources for students and researchers worldwide. As founder and scientific advisor of the startup Lighton, Krzakala has also demonstrated a commitment to translating theoretical insights into practical applications, particularly in the realm of optical computing for machine learning tasks.
Stefano CAMPOSTRINI is a Full Professor in the Department of Economics at Ca' Foscari University of Venice, specializing in Social Statistics (STAT-03/B). He serves as a Member of the technical-scientific Committee of the Ca' Foscari Challenge School and the Department of Economics' Committee. His research activities are supported by affiliations with the Research Institute for Social Innovation and the Research Institute for Innovation Management. Professor CAMPOSTRINI's research spans the intersection of statistical methodology, public health, and social policy. His work demonstrates expertise in advanced statistical techniques including Bayesian modeling, spatial analysis, and complex survey methodology. His primary focus areas include healthcare systems analysis, social innovation, public administration, and the economic aspects of health policy. He frequently addresses issues related to comorbidity patterns, healthcare service accessibility, and the application of artificial intelligence in healthcare settings. His publication record from 2021-2025 reveals significant trends in healthcare innovation, with particular emphasis on virtual hospital systems, AI applications in medicine, sustainable healthcare practices, and the statistical analysis of social services like early childhood education. His methodological contributions include novel approaches to analyzing regional health disparities and developing web-based tools for disease prevalence estimation. His research often employs expert consensus methods like Delphi techniques to address complex healthcare organizational challenges. Professor CAMPOSTRINI maintains active involvement in research initiatives through the Research Institute for Social Innovation and the Research Institute for Innovation Management. His work bridges advanced statistical methodology with practical applications in healthcare policy and social service delivery, making significant contributions to evidence-based decision making in public health and social policy domains across Italy and European contexts.
Professor Zoheir Sabeur is Professor of Data Science and Artificial Intelligence at Bournemouth University (2019–present) and Head of the Processes and Behaviour Understanding (PRO_BU) Research Group. He concurrently serves as Visiting Professor of Data Science at Colorado School of Mines (2017–present) and held the position of Science Director at the IT Innovation Centre, University of Southampton (2009–2019). Over three decades he has led more than 30 large-scale projects as Principal Investigator, securing over £12 million of funding from the European Commission, UKRI, DSTL, NERC, EPSRC and industry. Education PhD in Theoretical Physics, University of Glasgow (1990) MSc in Theoretical Physics, University of Glasgow (1986) BSc First-Class Honours in Physics and Applied Mathematics, Université d'Oran (1984) Advanced Leadership Programme, Ashridge Business School (2011) Research Interests Professor Sabeur’s research focuses on the fundamental theory and application of data science and artificial intelligence to understand complex human, natural and industrial processes and behaviours. His work spans multi-modal sensing, big-data analytics and machine-learning algorithms that extract actionable knowledge from large heterogeneous datasets. Application domains include: Healthcare: AI-driven diagnostics and prognostics for chronic diseases such as COPD, asthma and cancers through omics and phenotypic data integration. Environmental & Climate: Earth-observation analytics for wildlife migration and climate-change impact assessment using satellite data and global grid systems. Maritime & Cyber-Physical Security: Real-time risk assessment for shipping in extreme environments, smart-city safety and critical-infrastructure protection using computer vision and sensor fusion. Recent research has produced novel AI classifiers that analyse lung-auscultation audio signals to grade COPD severity, as well as digital-twin frameworks for detecting malicious behaviour in urban spaces. Scientific Awards & Recognition Fellow of the British Computer Society (FBCS) Fellow of the Institute of Marine Engineering, Science & Technology (FIMarEST) Chartered Engineer (CEng) and Chartered Physicist (CPhys) Multiple ORS Awards (1987, 1988, 1989) Grants & Doctoral Supervision Professor Sabeur has secured and led more than 40 funded projects since 1996, including recent grants such as INSIGHT (NIHR, 2024) and S4AllCities (H2020, 2020). He currently supervises three ongoing PhD students at Bournemouth University and has successfully graduated three others, covering topics from computational hydrodynamics to AI-based respiratory-disease analytics. He welcomes enquiries from prospective postgraduate researchers interested in data science, AI and interdisciplinary applications under schemes such as UKRI and Horizon Europe.
Patrick J. Curran is a Professor in the Department of Psychology at the University of North Carolina, affiliated with the L.L. Thurstone Psychometric Laboratory and the Quantitude initiative. His research focuses on quantitative methodologies, particularly longitudinal data analysis and structural equation modeling, applied to developmental psychopathology and adolescent substance use. He collaborates on statistical education through YouTube tutorials, a blog (Help Desk), and workshops, alongside a podcast with Greg Hancock. His work bridges methodological innovation with substantive research in psychology and public health. Key contributions include advancing latent curve models with structured residuals, integrative data analysis techniques, and the dissemination of statistical tools. His research spans disciplines such as developmental psychology, clinical psychology, and epidemiology, addressing topics like alcohol use trajectories, chronic depression mechanisms, and pandemic-related disparities in healthcare workers’ mental health. Lab Affiliations: L.L. Thurstone Psychometric Lab, Quantitude Collaborations: Dan Bauer (statistical education), Greg Hancock (podcast), and interdisciplinary teams in public health and clinical research His publications emphasize methodological rigor and practical application, often addressing gaps in longitudinal data analysis and measurement equivalence. Notable work includes the latent curve model with structured residuals (LCM-SR) and integrative data harmonization strategies for prevention science.
Brian Kulis is an Associate Professor at Boston University with appointments in the Department of Electrical and Computer Engineering, Computer Science, Systems Engineering, and the Faculty of Computing and Data Sciences. He holds the Peter J. Levine Career Development Professorship and has previously been an Amazon Scholar at Alexa AI (2019–2023) and an assistant professor at Ohio State University (2012–2015). His research focuses on machine learning, including large-scale optimization, metric learning, deep learning, Bayesian methods, and applications in audio and visual data analysis. He earned his PhD in Computer Science from the University of Texas at Austin (2008) and a BS in Computer Science and Mathematics from Cornell University. Key awards include the NSF CAREER Award (2015), CVPR Best Student Paper (2008), and ICML Best Student Paper (2007, 2005). His work spans publications in top venues like CVPR, NeurIPS, ICML, and ECCV, emphasizing scalable algorithms and domain adaptation. Current research explores metric learning, adversarial audio augmentation, and HPC anomaly detection. He advises multiple PhD students and collaborates on grants such as the NSF Traineeship for Sustainable Energy Solutions (2024). He teaches advanced courses in machine learning, deep learning, and data structures. His lab focuses on foundational and applied ML challenges, with affiliations in the Intelligent, Autonomous & Secure Systems group. Recent service includes senior area chair roles at AAAI, NeurIPS, and ICML.
Lincoln Quillian is Chair and Professor of Sociology at Northwestern University, holding the Gordon S. Fulcher Professorship of Decision-Making. He earned his Ph.D. from Harvard University in 1997 and previously taught at the University of Wisconsin. His research focuses on racial discrimination, residential segregation, urban sociology, and quantitative methods. Key projects include a meta-analysis of global hiring discrimination and analyses of residential mobility patterns. Education: Ph.D. in Sociology, Harvard University (1997) Research Interests: Racial discrimination, urban inequality, quantitative methods, and policy impacts on housing and labor markets. His work combines experimental and observational approaches to address systemic inequities. Recent publications examine persistent racial discrimination in hiring and housing across countries and time. He teaches advanced statistical methods and urban sociology courses. At Northwestern, Quillian chairs the IPR’s Neighborhoods and Community Safety program, emphasizing policy-relevant research. His findings inform debates on segregation, economic inequality, and anti-discrimination policies.
Dr. Marina Bock is a Chartered Civil Engineer and Lecturer in Civil Engineering at Aston University's College of Engineering and Physical Sciences. She specializes in structural engineering with expertise in metallic structures, additive manufacturing, and numerical modeling. Currently accepting PhD students, her work bridges academic research and industry applications in sustainable construction. Her educational background includes: PG Cert in Building and Design and Construction Technology, University of Wolverhampton (2017-2018) PhD in Local Buckling and Web Crippling Response of Stainless Steels, Universitat Politècnica de Catalunya (2010-2015) MSc in Patch Loading of Hybrid Plate Girders, Universitat Politècnica de Catalunya (2004-2010) Dr. Bock's research integrates laboratory experiments and numerical modeling to advance metallic structural systems, with pioneering work in additive manufacturing for construction. Her investigations span stainless steel design code development, corrosion prevention in reinforced concrete using hydrogels, and cold-formed steel behavior. Recent projects focus on sustainable infrastructure solutions through novel composite materials. Analysis of her 2022-2025 publications reveals dominant themes in additive manufactured aluminum structures, cold-formed steel design methodologies, and sustainable paving materials for urban heat island mitigation. Her work consistently addresses practical engineering challenges through experimental validation and code-compliant design solutions. Scientific recognition includes: IStructE Academic Research Award Commendation (2021) for research on aluminum SHS/RHS under biaxial bending Dr. Bock has secured significant research funding including a Royal Society Research Grant (£20k, 2023) for additive manufactured Al7075 aluminum and Innovate UK funding (£437k) for UV-reflective resin-based paving. Previous internal projects (£20k) focused on structural aluminum applications. She supervises PhD research in additive manufacturing and corrosion prevention while maintaining industry collaborations. Her experimental work utilizes advanced university laboratories for structural testing, with collaborations spanning European research consortia and industrial partners. Current projects involve multi-institutional teams developing reusable structural systems and solar-energy-harvesting building envelopes.
Samory Kpotufe is an Associate Professor of Statistics at Columbia University's Faculty of Arts and Sciences, affiliated with the Data Science Institute (DSI) as a Foundations of Data Science Co-Chair. He holds additional affiliations in Cybersecurity, Health Analytics, and Smart Cities. His academic journey includes a PhD in Computer Science from UC San Diego (2010), followed by research roles at the Max Planck Institute, Toyota Technological Institute at Chicago, and Princeton University's ORFE department. His research focuses on nonparametric methods and high-dimensional statistics, emphasizing adaptive procedures that self-tune to unknown data structures (e.g., manifolds, sparsity) while addressing modern application constraints like computational efficiency and labeling costs. Key themes include transfer learning, active learning, and online algorithms. Notable contributions span theoretical guarantees for nearest-neighbor methods, covariate shift adaptation, and contextual bandits. His work often bridges statistical theory and practical machine learning challenges, with applications in IoT, cybersecurity, and anomaly detection. He has led collaborative grants, such as the NSF CPS project on data augmentation for IoT systems. As a DSI member and Foundations Co-Chair, he contributes to advancing data science foundations through interdisciplinary collaboration. His lab's research frequently explores the interplay between algorithmic performance and intrinsic data properties.
Niklas Linde is a full professor at the University of Lausanne's Faculty of Geosciences and Environment, leading the Department of Earth Sciences. He holds a PhD in Geophysics from Uppsala University (2005) and has held roles including Assistant Professor (2008), Associate Professor (2013), and Full Professor (2019). His research focuses on transforming geophysical signals into realistic hydrogeological models with rigorous uncertainty quantification. Key areas include probabilistic inversion, Bayesian methods, and geostatistical modeling applied to environmental and subsurface processes. Education: PhD in Geophysics (Uppsala University, 2005), postdoctoral positions at Lawrence Berkeley National Lab (USA), CNRS-CEREGE (France), and ETH Zurich (Switzerland). He joined UNIL in 2008 as an Assistant Professor in Environmental Geophysics. Research interests span geophysical inversion techniques, subsurface heterogeneity characterization, and the integration of geophysical and hydrological data. Current projects emphasize Bayesian approaches for model selection and rare event estimation, supported by grants from the European Commission and Swiss National Science Foundation. Collaborations involve international teams addressing challenges in hydrogeology, rock fracture dynamics, and 4D hydrogeology. Publications reflect advancements in inverse problem solving, stochastic simulation, and machine learning applications. His work bridges theory and practice, with field studies in alpine environments, fractured media, and environmental monitoring. Students under his supervision have explored topics like deep generative networks and Bayesian hydrogeological inversion. Advising: Supervised over a dozen PhD students, including recent works on variational Bayesian methods and geophysical data fusion. Grants include projects on uncertainty quantification and experimental design. Active in scientific societies and editorial roles, contributing to methodological advancements in Earth sciences.
Tobias Oechtering is a Professor at the Division of Information Science and Engineering within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. His research focuses on information theory, privacy-preserving technologies, statistical signal processing, machine learning, and smart grid systems. He has held academic positions at KTH since 2008, advancing from Post-Doctoral Researcher to Assistant Professor (2010–2013), Associate Professor (2013–2018), and Professor (2018-present). He has supervised over 20 PhD students and contributed to numerous postdoctoral programs. Research Interests: - Network information theory and physical-layer security - Privacy mechanisms with provable guarantees - Distributed statistical inference and sensor calibration - Reinforcement learning and privacy-aware machine learning - Smart grid privacy and energy management - Wireless communication algorithms and signal processing - Networked control systems and stability analysis He currently supervises 7 PhD students and hosts 3 postdocs. His work has led to over 150 peer-reviewed publications, with recent contributions in privacy-preserving smart grid strategies, adversarial inference control, and information-theoretic security. He has served as editor for IEEE Transactions on Information Forensics and Security and held leadership roles in KTH's Digitalisation Research Platform.
Ethan McCormick is an Assistant Professor in the School of Education at the University of Delaware, specializing in longitudinal and psychometric modeling. He holds a Ph.D. in Psychology from the University of North Carolina at Chapel Hill (2020) and a B.S. in Biochemistry from the University of Arkansas (2013). His research focuses on integrating short-term and long-term longitudinal models to study behavioral and cognitive changes across the lifespan, with recent emphasis on educational data analysis and nonlinear random effects modeling. He is a Resident Faculty member of the University of Delaware’s Data Science Institute and previously served as an Assistant Professor of Methodology & Statistics at Leiden University (2022–2024). Dr. McCormick’s grants include the NWO Veni SSH Grant (2024–2027) for tracking educational outcomes via statistical modeling and the Jacobs Foundation Fellowship (2024–2026) for studying complex growth in math ability. His work bridges methodological rigor with applied neuroscience, examining brain-behavior relationships in developmental contexts through large-scale collaborations. Professional Experience : Assistant Professor, University of Delaware (2024–present); Assistant Professor, Leiden University (2022–2024) Key Research Themes : Longitudinal modeling, time series analysis, psychometrics, developmental cognitive neuroscience Awards : NWO Veni SSH Grant, Jacobs Foundation Fellowship His recent articles emphasize improving time-series methodologies, addressing limitations of two-time-point studies, and advancing models for asymmetric temporal dynamics. He collaborates internationally on projects simulating developmental datasets and analyzing neural correlates of behavior.
Samuel Kou is the Chair of the Department of Statistics and a Professor of Biostatistics at Harvard University. He holds dual affiliations with the Harvard T.H. Chan School of Public Health and the Department of Statistics, Faculty of Arts and Sciences. With a Ph.D. in Statistics from Stanford University (2001), he has held academic positions at Harvard since 2001, advancing from Assistant Professor (2001–2005) to John L. Loeb Associate Professor (2005–2008), and ultimately Professor (2008–present). His research focuses on stochastic inference in biophysics, Bayesian modeling, nonparametric methods, and Monte Carlo techniques, with applications in single-molecule biophysics, financial modeling, and big data analytics. Notable contributions include the development of the equi-energy sampler and foundational work on stochastic networks in nanoscale biophysics. Publications span high-impact journals like the Journal of the American Statistical Association and Biometrika, with a consistent emphasis on bridging statistical theory and real-world applications in biology and finance. His work often integrates computational methods to address complex systems at the molecular and macroeconomic scales. Administratively, he oversees the Department of Statistics and collaborates across interdisciplinary initiatives. His educational background includes a B.S. in Computational Mathematics from Peking University (1997) and an M.S. in Statistics from Stanford (2000).
Carol Frost, PhD, is an Assistant Professor in the Department of Renewable Resources at the University of Alberta's Faculty of Agricultural, Life and Environmental Sciences. Her research focuses on arthropod community ecology, particularly biodiversity conservation and the impacts of human activities on ecological functions. She holds a PhD in Ecology from the University of Canterbury (2014), an MSc in Entomology from McGill University (2009), and a BSc in Animal Biology from the University of Alberta (2006). Her work spans three key areas: (1) identifying low-cost industrial/urban modifications for biodiversity conservation, (2) advancing ecological network approaches to predict community dynamics, and (3) documenting Alberta's arthropod biodiversity. Recent studies include analyzing pollinator networks, habitat edge effects, and agricultural landscape impacts on hoverflies and canola crops. Frost teaches courses such as Principles of Managing Natural Diversity (REN R 364/765), Exploratory Data Analysis (REN R 581), and Statistical Methods for Environmental Sciences (REN R 582). Her research outputs emphasize ecosystem resilience, keystone species roles, and multitrophic interactions, with a focus on applied conservation solutions.
