Training Workshops
IFoDS 2026 offers workshops on Friday, July 3, 2026
TBD
Outline
TBD
Instructor
Dr.
Jian Huang is a Chair Professor of Data Science and
Analytics in the Department of Applied Mathematics at The Hong
Kong Polytechnic University. He obtained his Ph.D. degree in
Statistics from the University of Washington in Seattle. His
current research interests include deep generative models and
inference, statistical inference in deep learning, deep neural
network approximation theory, representation learning, and
statistical analysis leveraging pretrained large models. He has
published widely in the fields of Statistics, Biostatistics,
Machine Learning, Bioinformatics and Econometrics. He was
designated a highly cited researcher in the field of Mathematics
from 2015 to 2019 by the Web of Science group at Clarivate and
included in the list of top 2% of the world's most cited
scientists by Elsevier BV and Stanford University (2019-2024).
He serves on the editorial boards of the Journal of the American
Statistical Association and Journal of the Royal Statistical
Society (Series B). Professor Huang is a fellow of the American
Statistical Association and a fellow of the Institute of
Mathematical Statistics.
Prerequisites
TBD
Training Materials
TBD
Academic Writing in Statistics and Data Science
Outline
Targeting graduate students and early-career researchers in statistics/data science, this short course introduces a principled workflow for academic writing with the full lifecycle from idea organization to polished manuscript. The course begins with the structure of statistical papers, emphasizing strong topic sentences, coherent paragraph development, and disciplined use of citations. It then addresses common writing pitfalls specific to quantitative research, including clarity in model description, interpretation of results, and alignment between methods and conclusions. A central component of the course focuses on LaTeX typesetting, where participants will learn best practices for structuring documents, managing references, formatting equations and tables, and maintaining consistency across sections. The course further integrates reproducible writing tools, including Quarto and Git-based workflows, to connect narrative, code, and results in a unified framework. Practical examples drawn from real manuscripts will be used throughout to illustrate both effective and ineffective practices. By the end of the session, participants will have a concrete template and workflow for producing clear, reproducible, and publication-ready research papers.
Instructor
Dr. Jun Yan
is a Professor in the Department of Statistics at the University
of Connecticut and a Research Fellow at the Center for
Population Health at UConn Health. He earned his Ph.D. in
Statistics from the University of Wisconsin–Madison in 2003.
Prior to joining UConn in 2007, he spent four years at the
University of Iowa. Dr. Yan’s methodological research spans
networks, spatial extremes, measurement error, survival
analysis, clustered data analysis, and statistical computing,
often motivated by cross-disciplinary collaborations. His
applied work focuses on environmental sciences, public health,
and sports, with notable contributions to statistical methods
for the detection and attribution of climate change. Committed
to open science, he and his collaborators have developed and
maintain a suite of open-source R packages. Since 2020, he has
served as Editor of the Journal of Data Science. He is a Fellow
of both the American Statistical Association and the Institute
of Mathematical Statistics.
Prerequisites
Interest in improving writing skills. Experience would be a plus.