Huidong Chen

Huidong Chen

Machine Learning Enthusiast

About Me

A passionate machine learning enthusiast constantly exploring the cutting-edge advancements in artificial intelligence, deep learning models, and their real-world applications.

Interests
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Generative AI
  • Neural Networks
  • GNN, LLM, CNN
Education

  • PhD in Computer Science, 2012~2018

    Tongji University, Shanghai, China

  • Visiting PhD student in Computational Biology, 2016~2017

    Massachusetts General Hospital & Harvard Medical School, Boston, USA

  • Joint-training PhD student in Biostatistics and Computational Biology, 2015~2017

    Dana-Farber Cancer Institute & Harvard T.H. Chan School of Public Health, Boston, USA

  • BSc in Computer Science, 2008~2012

    Tongji University, Shanghai, China

Skills

python
R
Artificial Intelligence
Machine Learning
Deep Learning
Visualization

Experience

 
 
 
 
 
Staff Data Scientist
Valo Health
Mar 2024 – Present Massachusetts, USA
 
 
 
 
 
Senior Data Scientist
Morphic Therapeutic
Jul 2021 – Mar 2024 Massachusetts, USA
 
 
 
 
 
Postdoctoral Fellow
Massachusetts General Hospital & Harvard Medical School​
Sep 2018 – Jul 2021 Massachusetts, USA
 
 
 
 
 
Postdoctoral Fellow
Broad Institute Of MIT And Harvard
Feb 2019 – Jul 2021 Massachusetts, USA
 
 
 
 
 
Research Assistant
Massachusetts General Hospital & Harvard Medical School​
Oct 2016 – Aug 2018 Massachusetts, USA
 
 
 
 
 
Research Scholar
Dana-Farber Cancer Institute​ & Harvard T.H. Chan School of Public Health
Sep 2015 – Sep 2017 Massachusetts, USA
 
 
 
 
 
Graduate researcher
Tongji University​
Sep 2012 – Sep 2015 Shanghai, China
 
 
 
 
 
Purchasing Intern
Faurecia
Oct 2011 – Nov 2011 Shanghai, China

Software

*
All Python R Docker Dash JavaScript
SIMBA
SIMBA ( SIngle-cell eMBedding Along with features) is a versatile single-cell embedding method that co-embeds cells and features into the same latent space. https://simba-bio.readthedocs.io
May 29, 2023
SIMBA
STREAM
STREAM (Single-cell Trajectories Reconstruction, Exploration And Mapping) is an comprehensive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.
Jan 4, 2022
STREAM
singlecellVR
A user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware http://www.singlecellvr.com.
Oct 31, 2021
singlecellVR
scvr
scvr enables the conversion of standard outputs from commonly-used single-cell analysis tools to singlecellVR-compatible objects.
Mar 25, 2021
scATAC benchmarking
A benchmarking framework that was applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms.
Nov 30, 2020
scATAC benchmarking
STREAM website
An interactive website for STREAM http://stream.pinellolab.org
Oct 23, 2020
STREAM ATACseq
A STREAM companion package for single cell ATAC-seq preprocessing
Dec 11, 2019
GiniClust
GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data.
Jun 18, 2018
GiniClust
MEGATRON (coming soon)
MEGA TRajectories of clONes.
Jan 1, 0001
STREAM2 (coming soon)
Fast, scalable, and interactive trajectory analysis of single-cell omics data.
Jan 1, 0001

Featured Publications

Huidong Chen, Jayoung Ryu, Michael E Vinyard, Adam Lerer, Luca Pinello
May 2023 Nature Methods
SIMBA: SIngle-cell eMBedding Along with features
SIMBA: SIngle-cell eMBedding Along with features

SIMBA is a method to embed cells along with their defining features such as gene expression, transcription factor binding sequences and chromatin accessibility peaks into the same latent space. The joint embedding of cells and features allows SIMBA to perform various types of single cell tasks, including but not limited to single-modal analysis (e.g. scRNA-seq and scATAC-seq analysis), multimodal analysis, batch correction, and multi-omic integration.

Recent Publications

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Huidong Chen, Jayoung Ryu, Michael E Vinyard, Adam Lerer, Luca Pinello (2023). SIMBA: SIngle-cell eMBedding Along with features. Nature Methods.

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Haichuan Hu, Zofia Piotrowska, Patricia J. Hare, Huidong Chen, Hillary E. Mulvey, Aislinn Mayfield, Sundus Noeen, Krystina Kattermann, Max Greenberg, August Williams, Amanda K. Riley, Jarad J. Wilson, Ying-Qing Mao, Ruo-Pan Huang, Mandeep K. Banwait, Jeffrey Ho, Giovanna S. Crowther, Lida P. Hariri, Rebecca S. Heist, David P. Kodack, Luca Pinello, Alice T. Shaw, Mari Mino-Kenudson, Aaron N. Hata, Lecia V. Sequist, Cyril H. Benes, Matthew J. Niederst, Jeffrey A. Engelman (2021). Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. Cancer Cell.

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David F. Stein, Huidong Chen, Michael E. Vinyard, Qian Qin, Rebecca D. Combs, Qian Zhang, Luca Pinello (2021). singlecellVR: Interactive Visualization of Single-Cell Data in Virtual Reality. Front. genet.

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Lingfei Wang, Qian Zhang, Qian Qin, Nikolaos Trasanidis, Michael Vinyard, Huidong Chen, Luca Pinello (2021). Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate. Curr. Opin. Syst. Biol..

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Carman Man-Chung Li, Hana Shapiro, Christina Tsiobikas, Laura M. Selfors, Huidong Chen, Jennifer Rosenbluth, Luca Pinello, Kaitlin Moore, Kushali P. Gupta, G. Kenneth Gray, Yaara Oren, Michael J. Steinbaugh, Jennifer L. Guerriero, Aviv Regev, Joan S. Brugge (2020). Aging-associated alterations in mammary epithelia and stroma revealed by single-cell RNA sequencing. Cell Rep..

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Popular Topics

Aging Batch Corrction Benchmarking Cancer-Associated Fibroblasts Clustering Dash Dimensionality Reduction Docker Epigenome Lineage Tracing Multiomics Principal Graph Pseudotime Python R scATAC-seq scRNA-seq Single-cell Trajectory Inference Visualization

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