Bowei Xi is an Associate Professor at the Department of Statistics at Purdue University. He holds a Ph.D. in Statistics from the University of Michigan (2004) and has made significant contributions to adversarial machine learning, cybersecurity, and metabolomics. Education: Ph.D. in Statistics, University of Michigan (2004) His research focuses on: Adversarial Machine Learning – Developing defenses against attacks on AI systems Cybersecurity – Integrating statistical methods with network security Metabolomics – Applying statistical analysis to biological data Big Data – Creating scalable analytical frameworks Differential Privacy – Balancing data utility with privacy preservation Recent publications highlight: Advancements in causal inference under privacy constraints Novel cyber deception strategies for battlefield IoT Applications of graph neural networks with dropout techniques Statistical defense mechanisms against adversarial examples Awards include: Faculty of 1000 Evaluation (2011) US Patents #7272707 and #7490234 for application server optimization He teaches STAT 514: Design of Experiments with a focus on hands-on learning and statistical software (SAS) integration. His office hours and contact details are publicly listed for student engagement.
Maximilian Kasy serves as Professor of Economics at the University of Oxford's Department of Economics, actively contributing to research and teaching within econometrics and applied microeconomics frameworks. His research spans machine learning theory, algorithmic decision-making social impacts, publication bias mechanisms, adaptive experimental design, statistical decision theory, empirical Bayes methods, causal identification, and economic inequality with optimal taxation applications. This interdisciplinary work bridges economics, statistics, and machine learning to address methodological challenges in empirical research and policy evaluation, emphasizing social accountability in algorithmic systems and research transparency. Recent publications demonstrate expertise in adaptive experimental frameworks for policy optimization and novel methodologies for detecting and correcting publication bias, reflecting his commitment to improving empirical rigor and social relevance in economic research. Scientific recognition includes: Sloan Foundation grant (2022) Professor Kasy mentors students through Oxford's Summer School courses and coordinates the interdisciplinary Machine Learning and Economics Group, fostering collaboration across methodological domains. His research groups specifically focus on Econometrics and Applied Microeconomics, with significant engagement in UK and European contexts addressing Labour, Technology, and Inequality challenges.
Anders Bredahl Kock is a Professor of Economics at the University of Oxford, affiliated with St Hilda's College and the Aarhus Center for Econometrics (ACE). His research focuses on high-dimensional econometric methods, particularly in instrumental variable regression, panel data models, and statistical learning applications in economics. Professor Kock's research interests center on econometric theory with emphasis on high-dimensional statistics. His work develops innovative methods for handling large datasets in economic applications, including regularization techniques, hypothesis testing in high dimensions, and treatment effect estimation. He has made significant contributions to the understanding of moment inequalities, panel data models, and forecasting methodology. His recent publications demonstrate a strong focus on high-dimensional inference problems, with particular attention to instrumental variable methods, treatment allocation, and sequential decision making. His work bridges theoretical econometrics with practical applications in economics, showing how modern statistical techniques can address traditional economic questions with complex data structures. Award mentioned on August 22, 2022 Professor Kock collaborates extensively with leading researchers in econometrics, including David Preinerstorfer, Sophocles Mavroeidis, and Mehmet Caner. His research has been published in top-tier journals including Econometrica, Journal of Econometrics, and Journal of the American Statistical Association. His methodological contributions have practical implications for economic policy analysis and causal inference in observational studies.
Georg Spinner is a Lecturer and Head of the Research Group for Medical Image Analysis & Data Modelling at the Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW). His work integrates computational methods with biomedical research, focusing on stroke management, intracranial aneurysm risk modeling, and quantitative medical imaging. Primary affiliation: Zurich University of Applied Sciences Research focus: Bayesian networks, medical imaging, computational epidemiology Projects: GEMINI (digital twins for stroke), Stroke DynamiX (causal networks in stroke care), IVIM muscle activation studies Using Bayesian networks and advanced imaging techniques like IVIM DWI, his research aims to develop data-driven decision support systems for neurovascular diseases. He has contributed to modeling stroke health data and intracranial aneurysm risk stratification through international multicenter collaborations. His work spans computational biology, neuroinformatics, and digital health, with publications in journals like Medical Image Analysis and conferences including the International Conference on Computational and Mathematical Biomedical Engineering. Key methodologies include causal inference, dynamic disease modeling, and quantitative image analysis.
Mark Edward Borsuk is the James L. and Elizabeth M. Vincent Professor in the Department of Civil and Environmental Engineering at Duke University’s Pratt School of Engineering. He leads the Borsuk Lab, which specializes in interdisciplinary modeling of coupled social, environmental, and technical systems. His research spans climate change, ecosystem services, water resources, land use, and environmental health, using advanced methods such as Bayesian networks, agent-based modeling, game theory, and risk analysis. He co-directs the Center on Risk within Duke’s Science & Society Initiative and is an Associate of the Duke Initiative for Science & Society. B.S.E. in Civil Engineering and Operations Research, Princeton University, 1995 M.S. in Statistics and Decision Sciences, Duke University, 2001 Ph.D. in Environmental Science and Policy, Duke University, 2001 Postdoctoral Training, EAWAG (Swiss Federal Institute for Aquatic Science and Technology), Systems Analysis, Integrated Assessment, and Modelling (SIAM) Dr. Borsuk’s research focuses on integrating scientific data across disciplines to support decision-making under uncertainty. He is a leading expert in Bayesian network modeling applied to environmental and human health regulation. His work combines risk analysis, game theory, and agent-based modeling to assess climate change and environmental policy. He has developed novel frameworks for valuing ecosystem services, modeling landowner behavior, and assessing geoengineering risks. His lab emphasizes interdisciplinary collaboration, stakeholder engagement, and quantitative decision support. His recent publications reflect a strong trend toward integrating machine learning, causal inference, and spatial modeling into environmental assessment. Topics include solar radiation modification governance, land-use policy forecasting, invasive species impacts, and urban green space valuation. His work increasingly leverages big data (e.g., Zillow, remote sensing) and probabilistic programming to enhance model transparency and predictive accuracy. Chauncey Starr Distinguished Young Risk Analyst Award, Society for Risk Analysis, 2013 Early Career Research Excellence Award, International Environmental Modelling and Software Society, 2008 Earl I. Brown Outstanding Civil Engineering Faculty Award, Duke University, 2018 Best Paper, Integrated Environmental Assessment and Management Journal, 2012 Excellence in Mentoring Award, Dartmouth College Postdoctoral Association, 2010 Best Paper in Integrated Modelling, Environmental Modelling & Software Journal, 2008 Dr. Borsuk has been a principal investigator on grants from NSF, EPA, NIH, NIEHS, and USFS. He mentors a diverse group of graduate students and postdoctoral fellows, including Kim Bourne, Jon Holt, Chris Krapu, and Ryan Calder. He teaches courses such as Risk and Resilience Engineering, Engineering Economics, and Independent Study in Civil and Environmental Engineering. He is actively involved in advising and curriculum development through the Bass Connections Energy & Environment Research Team. He leads the Borsuk Lab, a dynamic research group focused on systems, risk, and decision analysis. The lab is a key contributor to the Bridge Collaborative—a partnership between Duke, The Nature Conservancy, IFPRI, and PATH—where it develops quantitative models to support cross-sectoral decision-making. The lab also investigates landowner decision-making in New England forests and the governance of solar geoengineering, using agent-based and deliberative modeling approaches.
Dr. Harald Lohre is a Quantitative Finance researcher affiliated with Lancaster University Management School as an Honorary Researcher and with the Hamburg Financial Research Center as a Research Fellow. His career spans leadership roles in quantitative equity research and portfolio management at Robeco, Invesco, and Deka Investment GmbH. Doctorate in Finance from University of Zurich Diploma in Mathematical Finance from University of Konstanz Former Fellow at Cambridge Judge Business School His research focuses on factor investing , portfolio optimization , and causal inference in finance , with publications in journals like Journal of Empirical Finance and Quantitative Finance. Recent work explores covariance matrix estimation and causal network modeling for systematic investing strategies. Scientific achievements include: Sir Clive Granger Memorial Best Paper Prize Bernstein Fabozzi/Jacobs Levy Award EFM 2020 Top Download Award Multiple CFA Society Germany Investment Research Awards He has supervised five PhD students and contributes to academic governance as an Associate Editor for the Journal of Systematic Investing and committee member of Inquire Europe. His work bridges academic rigor with industry applications in risk-based portfolio construction.
Hyunjoo Kim Karlsson is a researcher at the Department of Economics and Statistics, School of Business and Economics, Linnaeus University. Her work focuses on statistics and finance, particularly in high-dimensional data analysis, wavelet decomposition, and machine learning applications. Doctoral thesis: Dynamics of macroeconomic and financial variables in different time horizons (2012), Jönköping International Business School. Her research spans shrinkage estimators, outlier detection, time series modeling, and multivariate analysis under multicollinearity. Recently, she has expanded into statistical learning and mixed data sampling (MIDAS) for economic nowcasting. Key publication trends include oil price impacts on economies, exchange rate dynamics, and nonlinear financial modeling using wavelet methods and machine learning. She collaborates with researchers like Krister Månsson and R. Scott Hacker. Hyunjoo is part of the Deterministic and Stochastic Modelling group within Linnaeus University's Data Intensive Sciences and Applications (DISA) center, contributing to interdisciplinary sustainable co-creation projects.
Brian Caffo is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, with an active research profile evidenced by 2024-2025 publications and GitHub contributions. He is a key contributor to Neuroconductor (an R platform for medical imaging analysis) and the Acute to Chronic Pain Signatures (A2CPS) project, while maintaining significant educational impact through Coursera data science courses and open-source textbooks. His research spans biostatistics, neuroimaging analysis, and causal inference methodology, with emphasis on functional MRI data, multi-omics integration, and machine learning applications in public health. Current work focuses on advanced neural architectures for brain imaging, causal mediation in autism research, and biomarker discovery for chronic pain and neurodegenerative diseases. Analysis of his 15 most recent publications reveals dominant themes in transformer-based neuroimaging analysis (28%), causal inference applications (27%), and multi-omics/pain signature research (20%). His work consistently bridges statistical methodology with clinical applications in HIV prevention, autism spectrum disorders, and long COVID prediction through large collaborative projects like the National COVID Cohort Collaborative. Dr. Caffo leads the JHU Data Science Lab and contributes to multiple interdisciplinary initiatives including shell MEA development for neural organoids and DREAM-02 HIV microbicide trials. His GitHub profile shows active maintenance of educational repositories like LittleInferenceBook and regmodsbook, supporting widespread adoption of data science methods in biomedical research.
Dr. Sven Klaaßen serves as a Research Fellow at the University of Hamburg's Hamburg Business School within the Professorship for Statistics with Application in Business Administration, collaborating closely with Prof. Dr. Martin Spindler since 2021. His research focuses on developing advanced statistical methodologies for complex data environments. His academic credentials include: Ph.D. in Statistics from Hamburg Business School (2020) Visiting Scholar at MIT Department of Economics (2022) M.Sc. in Business Mathematics from University of Hamburg (2016) BSc in Business Mathematics from University of Hamburg (2014) Dr. Klaaßen's research program centers on Machine Learning, Causal Inference, Deep Learning, and High-Dimensional Statistics, with particular emphasis on developing robust inference techniques for modern data challenges. His work bridges theoretical statistics with practical applications in business analytics and econometrics, often addressing the complexities of high-dimensional datasets where traditional methods fail. Analysis of his recent publications reveals a clear trajectory toward integrating machine learning with causal inference frameworks, exemplified by his leadership in the DoubleML software ecosystem. His research increasingly tackles multimodal data challenges while maintaining rigorous statistical foundations, with applications spanning economics, operations research, and business decision systems. As an active member of Prof. Spindler's research group, Dr. Klaaßen contributes to collaborative projects developing open-source statistical tools and advancing methodological frontiers in causal machine learning. The team maintains strong industry and academic partnerships focused on translating theoretical innovations into practical analytical solutions.
Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.
Erman Acar is an Assistant Professor for Explainable AI in Finance at the University of Amsterdam, affiliated with the Socially Intelligent Artificial Systems (SIAS) group at the Informatics Institute (IvI) and the Cognition, Language and Computation (CLC) lab at the Institute of Logic, Language and Computation (ILLC). His research focuses on Neuro-Symbolic AI architectures that integrate machine learning with symbolic approaches (logical or causal) to enhance reasoning and explainability in single/multiagent scenarios, applied to financial services. Education : PhD in Computer Science (University of Mannheim, 2017), MSc in Computational Logic (TU-Wien/TU-Dresden, 2012). Previous Roles : Postdoctoral researcher at VU Amsterdam (2018-2021), Leiden University (2021-2022). Research Trends : His recent work emphasizes causal discovery via meta-reinforcement learning, aligning with his Neuro-Symbolic AI and XAI themes. A PhD position (2022) highlights his focus on deploying these approaches for fairness and transparency in fintech. Teaching : Co-teaches the course Interpretability & Explainability in AI (University of Amsterdam, 2023) and leads the AI4Fintech initiative. Labs & Teams : Collaborates with the SIAS group (IvI), CLC lab (ILLC), and the upcoming AI4Fintech hub in Amsterdam.
Dr. Julia Mink is a Tenure Track Assistant Professor at the University of Bonn, specializing in empirical environmental and health economics. Her research focuses on quantifying societal costs of pollution, climate change, and inequality through quasi-experimental methods and large-scale data analysis. Current affiliations: Institute for Applied Microeconomics, University of Bonn Previous affiliations: French National Research Institute for Agriculture, Food and Environment (INRAE) Her research combines environmental economics with health outcomes, emphasizing: Health impacts of air pollution and agricultural pesticides Inequality distribution in pollution exposure Climate resilience via healthcare systems Long-term socioeconomic impacts of disasters Recent publications analyze air pollution healthcare costs, pesticide effects on child development, and climate shocks in sub-Saharan Africa. She collaborates with researchers in Berkeley, Paris, and within the University of Bonn. Teaching roles: Environmental Economics (Graduate), Introductory Econometrics (Undergraduate) Argelander Professorship recipient
Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.
Shuhan Yuan is an Assistant Professor in the Computer Science Department at Utah State University (USU). He holds a Ph.D. in Computer Science from Tongji University, China, and bachelor's and master's degrees from Huaqiao University, China. Prior to USU, he was a postdoctoral researcher at the University of Arkansas under Dr. Xintao Wu. Affiliations: Utah State University, Computer Science Department Research Focus: Data Mining, Machine Learning, Anomaly Detection in Cyberspace, Explainable AI, Fairness, and Privacy-Preserving Techniques His research emphasizes developing trustworthy anomaly detection models that ensure explainability, robustness, and fairness. Key contributions include work on adversarial attacks, counterfactual explanations, and fair data generation. He has advised multiple PhD and Master’s students, focusing on anomaly detection applications in cybersecurity and fraud detection. Selected publications highlight advancements in log anomaly detection, backdoor attack defenses, and causal inference for root cause analysis. His work has been presented at top conferences like ICLR, ICML, and AAAI. He co-organized the Trustworthy Anomaly Detection tutorial at SDM24.
Jeremy Blackburn is an Associate Professor at Binghamton University's School of Computing, where he joined in fall 2019. His research focuses on data science, large-scale measurements, and modeling of toxic online behavior, hate speech, and extremist web communities, with significant media coverage from The Washington Post, New York Times, and BBC. Education: PhD, University of South Florida MSc, University of South Florida BSc, University of South Florida Dr. Blackburn's work centers on computational analysis of social dynamics , specializing in fringe online communities and cyber social threats . His methodology combines large-scale data collection with advanced modeling to dissect hate speech evolution, platform migration effects, and disinformation campaigns. Key contributions include developing detection frameworks for coordinated aggression and analyzing ideological spaces across platforms like Reddit, Telegram, and Rumble. Recent publications (2023-2025) reveal expanding research into adaptive hate speech detection systems, LLM capabilities for counterfactual reasoning, and cross-platform extremism analysis. His work increasingly integrates vision-language models while maintaining focus on real-world applications for content moderation, with growing attention to emerging platforms like Lemmy and Rumble's podcast ecosystem. Scientific Awards: No major scientific awards were explicitly documented in source materials Dr. Blackburn secured two significant NSF SaTC grants: (1) a 2023 Medium grant for iDRAMA.cloud (measuring information manipulation), and (2) a 2021 Small grant for detecting influence campaign accounts. While specific advisees aren't listed, his active research profile suggests graduate student mentorship in data science and cybersecurity projects. His research infrastructure includes the Social Media Analysis Toolkit (SMAT) and datasets like pushshift reddit/telegram collections, supporting collaborative work on cyber social threats through his lab environment focused on computational social science and security.
