Xiaoqiong Hu, Simon Fraser University

Research

Latest funding

Statistical Modelling and Inference with Unconventional Data
NSERC · 2023 · Principal investigator
$66,000
Mathematics for Public Health (MfPH)
NSERC · 2020 · Co-investigator
$3,000,000
Statistical Modelling and Inference with Complex Data
NSERC · 2016 · Principal investigator
$240,000

Statistical Modelling and Inference with Unconventional Data

Principal investigatorNSERC2023Discovery Grants Program - Individual

Principal investigators: Hu, Xiaoqiong

Keywords: clustered/correlated data; estimating function; fusion analysis; imperfect data; longitudinal/spatio-temporal analysis; non/semiparametric inference; online volunteer-based survey; stochastic process data/functional data; time-varying effect/covariate; truncation vs censoring

$66,000

Mathematics for Public Health (MfPH)

Co-investigatorNSERC2020Emerging Infectious Diseases Modelling Initiative (EIDM)

Principal investigators: Murty, Vijayakumar

$3,000,000

Statistical Modelling and Inference with Complex Data

Principal investigatorNSERC2016Discovery Grants Program - Individual

Principal investigators: Hu, Xiaoqiong

$240,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

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Mention their NSERC grant from 2023

Subject: Prospective PhD student: Clustered/Correlated Data Dear Professor Hu, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. Your research on clustered/correlated data connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Statistical Modelling and Inference with Unconventional Data" (2023), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]