Find Academic Supervisors

• Political Economy
• Social Science Methodology
• Natural Disasters
• +3 more

Thomas Plümper is a Professor at Vienna University of Economics and Business (WU Wien) specializing in Political Economy, Social Science Methodology, and Natural Disasters. His research integrates spatial analysis with political science to examine crisis response dynamics, particularly in pandemic and disaster contexts. Plümper's research interests center on methodological rigor in political science, with emphasis on spatial contagion patterns, robustness testing, and causal inference. His work bridges quantitative methodology with substantive applications in disaster politics and pandemic policy, notably through extensive collaboration with Eric Neumayer on COVID-19 spatial dynamics. Recent publications demonstrate his pivot toward analyzing political protests against containment policies and data integrity issues in authoritarian regimes. His publication trends reveal a methodological trajectory: early work focused on dyadic data analysis and robustness tests (2010-2015), transitioning to spatial disaster economics (2016-2020), and culminating in pandemic-related spatial epidemiology since 2020. Key thematic clusters include political methodology (35% of output), disaster politics (30%), and pandemic policy analysis (25%), characterized by innovative spatial econometric applications. Scientific awards highlight his interdisciplinary impact: ESRC Transformative Grant (2012) for methodological innovation SAGE Best Paper Award in EU Politics (2005) University of Essex Teaching Award (2004) Suedwestmetall Price for economic research (2002) Plümper maintains active scholarly engagement through 47 journal peer reviews (including BMJ and Social Science Quarterly), 12 research proposal evaluations (e.g., Riksbank Jubilee Fund), and editorial board membership. His consulting activities with institutions like University of Colorado demonstrate policy-relevant expertise. Collaborative networks are anchored in his long-term partnership with Eric Neumayer, producing 12 co-authored publications since 2020. Current research focuses on spatial contagion mechanisms in crises, examining how disasters and pandemics propagate across political boundaries while interacting with institutional responses. His lab work centers on developing spatial-effect variables for dyadic data analysis, with recent Stata modules (SPAGG, SPUNDIR) becoming field standards.

Jichun XieView profile

Associate Professor

• Genomics
• Bioinformatics
• Computational Biology
• +7 more

Jichun Xie is an Associate Professor in both the Department of Biostatistics & Bioinformatics and the Department of Computer Science at Duke University . He is also affiliated with the Duke Cancer Institute and the Duke Center for Statistical Genetics and Genomics . His research spans computational biology, genomics, and statistical methods for single-cell data and rare variant analysis. Education: Ph.D. in Biostatistics, University of Pennsylvania (2011) Research Focus: Dr. Xie develops computational frameworks for single-cell RNA sequencing analysis, rare variant association studies, and integrative genomics. His work bridges statistical inference with biological discovery, particularly in cancer immunology, aging, and immune disorders. Publication Trends: Recent papers emphasize single-cell data analysis (e.g., SifiNet, B-Lightning), rare variant mapping (DYNATE), and immune-genomic interactions in chronic GVHD and cancer. Methodologically, he pioneers hierarchical multiple testing and co-expression graph topologies. Grants: Duke Program of Training in Pulmonary Research (PROSPER), NIH/NHLBI (2022-2026) New computational methods for rare variant subregion detection, NIH/NHGRI (2022-2026) Training Program in Bioinformatics at the Intersection of Cancer Immunology and Microbiome, NIH/NCI (2020-2026) Labs and Teams: Dr. Xie leads the Xie Lab , which actively trains students and researchers in computational methods. The lab collaborates with immunologists and oncologists, producing tools like DART and TEAM for biomedical analysis.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +3 more

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

Momoko HayamizuView profile

Associate Professor

• Computational Biology
• Theoretical Biology
• Discrete Mathematics
• +4 more

Momoko Hayamizu is an Associate Professor at Waseda University's Faculty of Science and Engineering, Department of Applied Mathematics . Her work bridges discrete mathematics with computational biology , focusing on phylogenetic tree/network analysis, single-cell RNA-seq trajectory inference, and algorithm development. She holds a Ph.D. in Mathematical Sciences from The University of Tokyo and an M.D. from SOKENDAI. Research Interests : She specializes in combinatorics, graph theory, and discrete algorithms for modeling biological processes like cell differentiation and viral evolution. Her research emphasizes phylogenetic network theory , tree-based models , and software development (e.g., Treefit). Academic Contributions : Her work includes structure theorems for phylogenetic networks , algorithms for network orientation , and quantitative trajectory analysis methods . Publications span journals like SIAM Journal on Discrete Mathematics and IEEE/ACM Transactions on Computational Biology . Awards & Recognition : Recipient of MEXT's Young Scientists’ Prize , multiple WASEDA e-Teaching Awards , and international scholarships like the Anita Borg APAC . She actively contributes to educational outreach via YouTube lectures and high school collaborations. Leadership & Collaboration : Organized ICIAM 2023 Mini-symposium on phylogenetics, collaborated with institutions like IMI Kyushu University and RIKEN , and developed open-source tools like Treefit for single-cell data analysis.

Ramya Korlakai VinayakView profile

Assistant Professor

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +3 more

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison's College of Engineering. Her research bridges theoretical machine learning with human-centered AI applications. Education: Postdoctoral Researcher, Paul G. Allen School of Computer Science and Engineering, University of Washington Ph.D. and M.S. in Electrical Engineering, California Institute of Technology (Caltech) B.Tech. in Electrical Engineering, Indian Institute of Technology Madras (IIT M) Research Focus: Her work centers on machine learning foundations with emphasis on statistical inference, crowdsourcing, and human-AI collaboration. Key themes include modeling heterogeneous human preferences, developing robust out-of-distribution detection systems, and creating culturally-aware generative models. She pioneers methods for pluralistic alignment where AI systems accommodate diverse human values through ideal point modeling and adaptive feedback mechanisms. Publication Trends: Recent publications (2023-2025) reveal three converging trajectories: (1) Human-in-the-loop frameworks for reducing false positives in safety-critical systems, (2) Metric learning innovations using limited preference data, and (3) Critical examinations of cultural limitations in generative AI. Her work consistently addresses real-world challenges like cognitive overload in crowdsourcing and biases in face generation systems. Research Infrastructure: She leads an active research group with a dedicated lab website (https://ramyakv.github.io/) focusing on developing theoretically-grounded yet practically-deployable AI systems that respect human plurality.

Maoran XuView profile

Assistant Professor

• Bayesian Inference
• High-Dimensional Data Analysis
• Latent Variable Models
• +4 more

Maoran Xu serves as an Assistant Professor in the Department of Statistics at Indiana University Bloomington, where he develops advanced probabilistic models for high-dimensional data analysis. His academic credentials include: Ph.D. in Statistics, University of Florida (2018-2022) B.S. in Mathematics and Applied Mathematics, Fudan University (2014-2018) Professor Xu's research program centers on Bayesian inference frameworks with emphasis on efficient sampling and low-dimensional structure discovery. His work tackles critical challenges in high-dimensional data through innovative approaches to latent factors, clustering, and sparse models. He specifically develops optimization-enhanced Bayesian methods for building likelihood-based models on geometric spaces, enabling robust uncertainty quantification for network data and complex structured datasets. His methodologies bridge theoretical statistics with practical applications in modern data science challenges. Contact information is available through email (mx13@iu.edu) and his professional website (https://xumaoran.com/), with office location in Swain Hall East 229 at Indiana University.