Jin-Hong Du

Jin-Hong Du

Assistant Professor at the University of Hong Kong

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Pages

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    <a href="https://jinhongdu-lab.github.io/" rel="permalink">Welcome to Jin-Hong's Homepage</a>
  
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<p class="archive__item-excerpt" itemprop="description"><p>About me</p>


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    <a href="https://jinhongdu-lab.github.io/talks/" rel="permalink">Talks and presentations</a>
  
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    <a href="https://jinhongdu-lab.github.io/AGENTS/" rel="permalink">Repository Instructions</a>
  
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    <a href="https://jinhongdu-lab.github.io/markdown_generator/" rel="permalink">Jupyter notebook markdown generator</a>
  
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Posts

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    <a href="https://jinhongdu-lab.github.io/posts/2024/10/blog-job-interview/" rel="permalink">Academic Job Interview

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2024-10-13T00:00:00-07:00">October 13, 2024</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><h1 id="screening-interview-questions">Screening interview questions</h1>


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    <a href="https://jinhongdu-lab.github.io/posts/2024/04/blog-python/" rel="permalink">Python Handbook

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2024-04-30T00:00:00-07:00">April 30, 2024</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><h1 id="conda">Conda</h1>


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    <a href="https://jinhongdu-lab.github.io/posts/2022/04/blog-inequality/" rel="permalink">Norm Inequality

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2022-04-16T00:00:00-07:00">April 16, 2022</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes some useful inequalities between different norms.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2022/04/blog-divergence/" rel="permalink">Divergence Measures

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2022-04-16T00:00:00-07:00">April 16, 2022</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes useful results on divergence measurere.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2022/03/blog-RKHS/" rel="permalink">Reproducing Kernel Hilbert Spaces

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2022-03-22T00:00:00-07:00">March 22, 2022</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes reproducing kernel Hilbert spaces and mainly follows the lecture slides by <a href="http://www.stats.ox.ac.uk/~sejdinov/teaching/atml14/Theory_slides1_2014.pdf">D. Sejdinovic, A. Gretton</a>.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-differential-theory/" rel="permalink">Differential Theory

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-21T00:00:00-08:00">December 21, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes differential theory on convex functions, including directional derivatives, subgradients, and Legendre transformation.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-duality/" rel="permalink">Duality

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-20T00:00:00-08:00">December 20, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes duality.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-convex-set-and-function/" rel="permalink">Convex Sets and Convex Functions

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-17T00:00:00-08:00">December 17, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes convex sets and convex functions, and their properties.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-nonparametric/" rel="permalink">Nonparametric Regression

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-07T00:00:00-08:00">December 07, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes nonparametric regression.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-causal-inference/" rel="permalink">Causal Inference

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-07T00:00:00-08:00">December 07, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes causal inference.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-multiple-testing/" rel="permalink">Multiple Testing

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-06T00:00:00-08:00">December 06, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes multiple testing.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-test/" rel="permalink">Hypothesis Testing

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-12-05T00:00:00-08:00">December 05, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes hypothesis testing, confidence interval, bootstrap, and Bayesian inference.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-asymptotics-MLE/" rel="permalink">Asymptotics of MLE

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-10-29T00:00:00-07:00">October 29, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes asymptotic properties of MLEs.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-uniform-laws/" rel="permalink">Uniform Laws

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  4 minute read


     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-10-28T00:00:00-07:00">October 28, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes empirical process theory and statistical functional estimations.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-point-estimation/" rel="permalink">Point Estimation

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  4 minute read


     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-10-26T00:00:00-07:00">October 26, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes point estimation methods and some inportant quantities.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/10/blog-statistics/" rel="permalink">Statistical Model And Statistics

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-10-25T00:00:00-07:00">October 25, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes statistical models, statistics and its properties.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/09/blog-concentration-bound/" rel="permalink">Finite-Sample Concentration Bounds

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-09-10T00:00:00-07:00">September 10, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes some useful finite-sample concentration bounds.</p>


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    <a href="https://jinhongdu-lab.github.io/posts/2021/08/blog-post-parallel/" rel="permalink">Parallelism in Python

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-08-20T00:00:00-07:00">August 20, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>This post summarizes some useful resourses for parallelism in Python.</p>

research

talks

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    <a href="https://jinhongdu-lab.github.io/talks/2021-04-26-VITAE" rel="permalink">Model-Based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2021-04-26T00:00:00-07:00">April 26, 2021</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><h1 id="background">Background</h1>

In the analysis of single-cell RNA sequencing (scRNA-seq) data, trajectory inference or pseudotime analysis methods are proposed to infer the cell developmental lineages and order cells on their pseudotime within these lineages. As scRNA-seq datasets continue to expand, there is a strong need to integrate different datasets and perform trajectory inference jointly to align cells experiencing similar dynamic changes but are from different labs or under various conditions. By a joint cell trajectory inference of our own data with other publicly available scRNA-seq datasets, we aim to obtain a complete picture of the neurogenesis in the mouse neocortex and expand our understanding of the progenitor heterogeneity.

Results

We develop a new quantitative framework to perform a joint cell lineage analysis, which uses a statistical hierarchical mixture model for the trajectory structure on the cells' low-dimensional embedding. Our method outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. We also apply it to jointly analyze two single-cell RNA sequencing datasets on the mouse neocortex, with a conditional variational auto-encoder and adjusting for batch and cell-cycle effects. The inferred developmental trajectory enables us to study the progenitor cell types and identify the subtle development of IPCs. After correcting post-estimation bias, we have detected around 1000 genes whose co-expression patterns with Eomes change significantly across embryonic days.

Conclusions

(1) Our method successfully merges cells from different datasets and learns a shared trajectory of neurogenesis, keeping biologically meaningful differences between the two datasets. (2) We identify progenitor cell types based on integrated data and can further divide the IPCs into post-mitotic and mitotic IPCs. (3) We are able to identify temporally expressed transcription regulators in cortical progenitors.

<h2 class="archive__item-title" itemprop="headline">
  
    <a href="https://jinhongdu-lab.github.io/talks/2023-07-28-gcv" rel="permalink">Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2023-07-28T00:00:00-07:00">July 28, 2023</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p><a href="https://icml.cc/virtual/2023/oral/25518">More information here</a></p>


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    <a href="https://jinhongdu-lab.github.io/talks/2023-08-06-ecv" rel="permalink">Extrapolated cross-validation for randomized ensembles

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2023-08-06T00:00:00-07:00">August 06, 2023</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p>Ensemble methods such as bagging and random forests are ubiquitous in various Þelds, from Þnance to genomics. Despite their prevalence, the question of the e_cient tuning of ensemble parameters has received relatively little attention. This paper introduces a cross-validation method, ECV (Extrapolated Cross-Validation), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields _-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and K-fold crossvalidation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique.</p>


<h2 class="archive__item-title" itemprop="headline">
  
    <a href="https://jinhongdu-lab.github.io/talks/2023-11-20-GCATE" rel="permalink">Simultaneous inference for generalized linear models with unmeasured confounders

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     <p class="page__date"><strong><i class="fa fa-fw fa-calendar" aria-hidden="true"></i> Published:</strong> <time datetime="2023-11-20T00:00:00-08:00">November 20, 2023</time></p>
    


<p class="archive__item-excerpt" itemprop="description"><p><a href="https://ims.nus.edu.sg/events/ims_forum2023/">More information here</a></p>

teaching

<h2 class="archive__item-title" itemprop="headline">
  
    <a href="https://jinhongdu-lab.github.io/teaching/data-8020-advanced-causal-inference/" rel="permalink">DATA 8020: Advanced Causal Inference

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      <p> Research postgraduate course, <i>HKU IDS P603 Seminar Room, Graduate House</i>, 2026 </p>
    


<p class="archive__item-excerpt" itemprop="description"><p>A research postgraduate course on modern causal inference from statistical, machine learning, and computational perspectives.</p>