Professor Richard Samworth is a leading academic at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics and serving as Director of the Statistical Laboratory . His research focuses on Nonparametric and High-dimensional Statistics , addressing challenges in data analysis, statistical learning, and computational methods. Research Interests : Richard Samworth's work emphasizes Nonparametric Statistics , High-dimensional Data , and Statistical Learning . His research spans topics such as Missing Data , Changepoint Detection , Log-concave Density Estimation , and Subgroup Analysis , with applications in Machine Learning and Data Science . Key methodologies include Score Matching , Random Projections , and Minimax Estimation . Recent publications highlight advancements in Semi-Supervised Learning , Robust Statistical Testing , and High-dimensional PCA with heterogeneous missingness. His work bridges theoretical rigor with practical applications, particularly in Statistical Algorithms and Optimization .
Siva Balakrishnan is an Associate Professor at Carnegie Mellon University with a joint appointment in the Department of Statistics and Data Science and the Machine Learning Department . He holds an affiliation with the Dietrich College of Humanities and Social Sciences. Previously, he was a postdoctoral researcher at UC Berkeley's Department of Statistics, advised by Martin Wainwright and Bin Yu, and earned his Ph.D. in Computer Science from CMU's Language Technologies Institute under Jaime Carbonell. His research focuses on statistical machine learning, causal inference, and high-dimensional statistics, with notable contributions to domain adaptation, optimal transport, and robust statistics. Education: Ph.D. in Computer Science, Carnegie Mellon University (Language Technologies Institute) Postdoctoral Researcher, University of California, Berkeley (Department of Statistics) Research Interests: His work bridges theoretical foundations and algorithmic development, emphasizing robust statistical methods and their applications in causal inference, public policy, and machine learning. Key areas include nonparametric methods, optimization, and high-dimensional data analysis. He has pioneered techniques in domain adaptation, such as the RLSbench framework for relaxed label shift scenarios. Awards & Grants: Amazon Research Award (2021) Google Research Scholar Award (2021) NVIDIA Pioneer Award (2018) IMS Lawrence D. Brown Student Award (2020, 2022) National Science Foundation Grants (CCF-1763734, DMS-1713003, etc.) Professional Activities: He serves as an Associate Editor for JASA and on the editorial boards of Foundations and Trends in Statistics . His work has been featured in top venues like NeurIPS, ICML, and the Annals of Statistics. He currently holds a sabbatical at UC Berkeley's Department of Statistics (Spring 2025). Labs & Collaborations: He actively participates in the Statistics and Machine Learning Reading Group and the Causal Inference Working Group , fostering interdisciplinary research in CMU's vibrant academic community.
Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.
Xiaoxiao Zhou is an Assistant Professor in the Department of Biostatistics at the University of Alabama at Birmingham (UAB), affiliated with multiple centers including the Center for Outcomes and Effectiveness Research and Education (COERE), Center for Clinical and Translational Science (CCTS), and the Global Center for Craniofacial, Oral and Dental Disorders (GC-CODED). She holds a PhD in Statistics from The Chinese University of Hong Kong (2022) and completed a postdoctoral fellowship at Duke University's Department of Statistical Science. Her research focuses on causal inference, Bayesian methods, longitudinal data analysis, and survival analysis, with applications in Alzheimer’s disease, cardiovascular conditions, and neurodegenerative disorders. Dr. Zhou’s work integrates advanced statistical techniques with medical and behavioral data, including neuroimaging and latent variable modeling. Key areas include handling intercurrent events in clinical trials, causal mediation analysis, and joint modeling of longitudinal and survival outcomes. She collaborates widely with clinicians and biostatisticians to address real-world challenges in healthcare and disease progression studies. Her scholarly contributions span over a dozen peer-reviewed articles, emphasizing methodological innovations in biostatistics and their practical applications. She advises students such as Zhenying Ding and actively participates in academic committees. Outside academia, she enjoys outdoor activities like mountain hiking and weight lifting.
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.
Assoc Prof Xiang Liming is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (Students). She holds editorial roles at *Computational Statistics & Data Analysis* and *Statistics in Medicine*. With a PhD in Statistics (City University of Hong Kong, 2002), her research focuses on survival analysis, longitudinal data analysis, and biostatistical methods. Notable contributions include methodologies for semi-competing risks, interval-censored data, and mixture models. Her work bridges statistical theory with biomedical applications, addressing challenges in clinical trials and public health. Awards include the 2009 IIE Transactions Best Paper Award and the Outstanding Research Thesis Award (2002–2003, CityU). Education: PhD in Statistics, City University of Hong Kong (2002) Postdoctoral Research: Hong Kong University of Science and Technology (2002–2003) and CityU (2003–2006) Research Interests: Survival analysis methodologies, including frailty models, cure models, and quantile regression for censored data. She develops robust statistical approaches for clustered/longitudinal data, addressing missingness and overdispersion. Applications span biomedical research, epidemiology, and quality management. Grants & Collaborations: Her grants include work on robotic-assisted stroke rehabilitation (2021) and LNG cold energy utilization systems (2017–2019). She collaborates with clinical teams on trials involving upper limb neurorehabilitation technologies. Labs & Teams: Leads statistical method development for multi-center clinical trials, particularly in biostatistics and survival analysis frameworks. Active in NTU’s School of Physical & Mathematical Sciences research initiatives.
Michal Engelman is a Professor of Sociology at the University of Wisconsin–Madison and serves as the Director of the Center for Demography of Health & Aging (CDHA) and the Wisconsin Longitudinal Study (WLS). She is also the Director of a doctoral/postdoctoral training program in Population, Life Course, and Aging. Engelman holds a PhD in Population & Health and MHS in Biostatistics from Johns Hopkins, along with an AB in History from Harvard. Her research bridges sociology and public health, focusing on social determinants of health and longevity, particularly how socioeconomic status, race/ethnicity, nativity, and geography shape health inequities. Current projects include NIH-funded studies on epigenetic aging and neighborhood disadvantage (REWARD) and early/midlife exposures influencing cognitive health in later life (ILIAD). Engelman’s academic affiliations include the Center for Demography and Ecology and CDHA. She teaches courses such as Sociology of Aging, Population Problems, and Population Economics. Her work addresses critical topics like mortality disparities, immigrant health, and the interplay between social context and health outcomes.
Naim U. Rashid, PhD, is an Associate Professor with tenure in the Department of Biostatistics at the UNC Gillings School of Global Public Health and holds a joint appointment as Research Associate Professor at the Lineberger Comprehensive Cancer Center. He serves as Associate Director of the Lineberger Biostatistics Shared Resource and co-directs the Biostatistics Cores of the UNC Pancreatic and Breast Cancer SPOREs. His work bridges statistical methodology development with collaborative cancer research, focusing on translating genomic discoveries into clinical applications. Dr. Rashid's research spans precision medicine, genomics, statistical computing, and machine learning with specific applications to pancreatic and breast cancers. His lab develops novel statistical methods for high-throughput genomic data analysis, cancer subtyping, missing data problems in deep learning, and clinical trial design. Recent work includes developing an AI tool that recommends optimal clinical trials to pancreatic cancer patients, funded by a $311,000 Department of Defense grant in 2024. His methodological contributions focus on improving replicability in gene signature selection and clinical prediction, with emphasis on addressing racial disparities in cancer outcomes. His publication record shows consistent output in top statistical and medical journals, with recent work focusing on high-dimensional statistics, missing data methods, and cancer genomics. His research demonstrates a clear trajectory from methodological innovation to clinical implementation, particularly in pancreatic cancer where his PurIST classifier has gained recognition. The work increasingly incorporates machine learning approaches while maintaining strong statistical foundations. Delta Omega Faculty Award (2021, UNC Chapel Hill) IBM and R.J. Reynolds Junior Faculty Development Award (2017, UNC Chapel Hill) Barry H. Margolin Dissertation Award (2013, UNC Chapel Hill) Training Grant recipient (2006-2011, Genomics and Cancer) Dr. Rashid actively mentors graduate students and serves as trial statistician on multiple cancer clinical trials. He teaches BIOS 735, a doctoral-level course on statistical computing, and is involved with the Translational Breast Cancer Research Consortium Statistical Working Group. His lab collaborates extensively with clinicians at UNC Lineberger and beyond, with recent work including the PROCLAIM Study examining mHealth apps to improve diverse recruitment in pancreatic cancer trials. The Rashid Lab focuses on developing computational tools that directly impact clinical decision-making while addressing methodological challenges in genomic data analysis.
Laura Balzer, PhD, MPhil is an Associate Professor of Biostatistics at the University of California, Berkeley . Her research focuses on methodological and applied work in causal inference , machine learning , and messy real-world data , particularly in the context of HIV prevention and global health in East Africa. PhD – Biostatistics, University of California, Berkeley (2015) MPhil – Computational Biology, University of Cambridge (2009) BS – Applied Mathematics, University of Vermont (2008) Dr. Balzer specializes in the design and analysis of cluster randomized and pragmatic trials , addressing challenges like differential measurement , complex dependence , and missing data . Her work integrates epidemiologic methods with machine learning to enhance rigor in real-world studies. Recent publications emphasize community-based HIV interventions , dynamic choice models , and causal inference frameworks for global health applications in Kenya and Uganda. Her methodological contributions include Two-Stage TMLE for handling sub-sampling and non-independent units , while applied studies examine HIV-tuberculosis interactions , hypertension care models , and social network effects on health outcomes. Dr. Balzer’s role as a Primary Statistician for East African studies underscores her commitment to translating academic advances into public health impact .
Dr. Maria Bolsinova is an Assistant Professor at Tilburg University's Department of Methodology and Statistics within the Tilburg School of Social and Behavioral Sciences. Her work focuses on psychometrics, healthcare technology, and methodological advancements in educational and clinical assessments. She holds a PhD and has contributed to over 40 research outputs since 2013. Research interests include experience sampling, patient-friendly measurement designs, differential item functioning, and extreme response style correction. Her projects often involve collaborations with organizations like the OECD and Amplify Education Inc., focusing on adaptive learning systems and PISA studies. She developed a personalized missingness design to reduce patient burden in longitudinal studies, currently being implemented in the m-Path app. Consulting roles: Curriculum Associates, LLC (2024–2024); OECD (2023–present); Amplify Education Inc. (2023) Contributions to UN Sustainable Development Goals through methodological advancements in healthcare and education Dataset: 'Assessing Life Satisfaction in Everyday Life' (2023)
Rohan Alexander is an Assistant Professor jointly appointed in the Faculty of Information and the Department of Statistical Sciences at the University of Toronto. His research focuses on improving the trustworthiness of data science through rigorous workflows, including reproducibility and bias mitigation. He co-founded The Data Workshop, a platform for data science best practices, and authored Telling Stories With Data , a book emphasizing reproducible methods. Rohan holds a PhD in Economics from the Australian National University, specializing in economic history. Rohan’s academic roles include Assistant Director of CANSSI Ontario and Senior Fellow at Massey College. His teaching includes courses like Experimental Design for Data Science and Worlds Become Data . He actively contributes to interdisciplinary initiatives like the Schwartz Reisman Institute for Technology and Society. His research interests span quantitative social science, Bayesian methods, text analysis, and computational reproducibility. He emphasizes code transparency and testing in data science projects. Rohan’s work addresses challenges in data measurement, such as systematic missingness, and explores AI’s societal impacts through collaborative projects with academia and industry. Rohan is affiliated with the Data Sciences Institute and leads initiatives on reproducibility. His current supervision includes PhD student Ciara Zogheib. His institutional roles extend to strategic projects like the Climate Positive Energy initiative and School of Cities.
Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.
Prof. Zeynep Işıl Kalaylıoğlu is a Professor of Statistics at the Department of Statistics, Middle East Technical University (METU), Ankara, within the Faculty of Arts and Sciences. She holds a Ph.D. in Statistics from North Carolina State University (2002) and has held roles including Associate Professor (2014–2022), Assistant Professor (2009–2014), and researcher at the National Cancer Institute (2002–2007). She has served in administrative roles such as Assistant to the Department Head and member of METU’s Wind Energy Research Center and Faculty Board. Ph.D.: Statistics, North Carolina State University, 2002 M.S.: Statistics with Computational Engineering minor, North Carolina State University, 1999 B.S.: Statistics with Computer Engineering minor, Middle East Technical University, 1995 Her research focuses on Bayesian statistical methodologies for environmental and health phenomena. Key areas include spatial/temporal modeling of atmospheric and ecological variables, prediction of migratory patterns (e.g., wind direction, bird movements), and risk modeling for breast cancer using mammogram data. She develops flexible models for circular and extremal data, emphasizing applications in ecology, epidemiology, and environmental science. Her recent publications address topics like goodness-of-fit tests for statistical distributions, predictive model selection for circular data, and Bayesian approaches for missing covariates in generalized linear models. Current projects include mobile apps for olive harvest optimization and frost预警 systems, reflecting her commitment to applied statistical solutions. Labs/Teams: She leads the BayeZian Research Group, collaborating with METU’s Ecosystem Research Center on interdisciplinary projects. Her teaching expertise spans mathematical statistics, Bayesian theory, and statistical inference at undergraduate and graduate levels.
Elizabeth Schifano is an Associate Professor in the Department of Statistics at the University of Connecticut. She focuses on biostatistics, statistical genomics, and high-dimensional data analysis with applications in medical research and genetic studies. Her work addresses complex challenges in missing data imputation, longitudinal studies, and variable selection in high-dimensional genomic datasets. Research Interests: Development of Bayesian methods for nonignorable missing data in clinical trials Statistical methodologies for analyzing SNP sets in genetic association studies Online updating algorithms for streaming big data Survival analysis with monotone partial likelihood Publications reflect her expertise in statistical genomics and advanced computational methods. She collaborates with researchers in public health and biomedical sciences to advance methodological innovations in data analysis. Office: AUST 317 | Contact: (860) 486-6143
