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In this digital age, large-scale data offer many new opportunities, holding great promises for researchers and decision-makers to understand important variations among sub-populations, explore associations between features and rare outcomes (e.g., rare diseases or extreme events), and make optimal personalized recommendations in areas of immediate practical relevance such as precision medicine and social programs. There exist formidable computational and statistical challenges in the analysis of heterogenous data. Some of the key barriers include scalability to data size and dimensionality, deep exploration of heterogeneity and structures in the data, need for robustness and replicability, and the ability to make sense of incomplete observations (e.g., due to censoring). The proposed workshop will serve as a platform for bringing some of the leading scholars in statistics and data science to exchange new research ideas and train the next-generation data scientists in the analysis of heterogeneous data. The workshop will convene interdisciplinary researchers to discuss the forefront of heterogeneous data analysis and identify emerging areas for future research, emphasizing both methodology and applications.

The workshop will feature keynote speakers, invited talks, poster session and career panel for junior researchers. Graduate students, postdocs, and participants from under-represented groups are particularly welcome.

More details are forthcoming.

Program Committee:

Xuming He, Washington University in St. Louis

Kengo Kato, Cornell University

Roger Koenker, University College London

Snigdha Panigrahi, University of Michigan

Lan Wang, University of Miami

Qi Zheng, University of Louisville

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