About

Xiaohua Huang, Ph.D., is an Associate Professor in the Department of Bioengineering at the University of California, San Diego (UCSD), and is also affiliated with the Materials Science and Engineering Program and the Institute of Engineering in Medicine at UCSD. As the Principal Investigator of the Huang Lab, Dr. Huang leads research efforts focused on advancing technologies in genomics, proteomics, microfluidics, single-cell transcriptomics, and single-molecule sequencing. The lab's work integrates expertise in micro and nanofabrication, computational modeling, and deep machine learning to develop innovative tools and methods for molecular analysis at the single-cell and single-molecule levels. Dr. Huang's research group includes postdoctoral researchers and graduate students working on projects such as microfluidic processors for genome sequencing, single-molecule DNA and protein sequencing, and computational approaches to biological data analysis. The lab's interdisciplinary approach combines bioengineering, chemistry, physics, and nanotechnology to push the boundaries of molecular and cellular analysis technologies.

Research topics

• Optoelectronics
• Chemistry
• Composite material
• Biotechnology
• Nanotechnology
• Biology
• Chemical engineering
• Genetics
• Botany
• Materials science

Selected publications

• High-Data-Density, High-Decoding-Speed, and High-Decoding-Accuracy DNA Data Ink for Digital Preservation

  ACS Nano · 2026-01-21

  article

  DNA-based storage offers exceptional information density, durability, and energy efficiency compared to conventional digital media, yet practical implementation faces challenges including high synthesis costs, sequencing errors, and slow access speeds. Here, we present an integrated DNA storage system with optimized encoding and processing strategies to address practical implementation issues. Our approach achieves 9.78 bits/nt net information density with a flexible index allocation system handling data volume from 0.37 KB to 2.79 × 1022 YB. The decoding process delivers 360× faster throughput than traditional methods, processing 4.57 million reads (1.63 GB) in 34.5 s and demonstrating perfect data retrieval from down-sampled (×5.33) sequencing in 2.47 s. Our error correction system combines inner Reed-Solomon with outer XOR code, ensuring reliable recovery with large reading sequences (92,626) and low copy number data (×0.52). The streamlined NGS preparation workflow reduces processing time from ∼4.5 to ∼2 h while decreasing per-sample costs from ∼$60 to ∼$0.50. The system demonstrates versatility through high-fidelity DNA data storage ink and implementations ranging from physical stamps to VR platforms. This technology establishes a foundation for practical DNA data storage solutions applicable to cultural heritage preservation, autonomous vehicle data management, and matrix-based machine learning applications.

  PublisherDOI

• Additional file 12 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 12: Table S12. The correlation coefficient between all the genes and modules in the coexpression network.

  PublisherDOI

• Additional file 5 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 5: Table S5. The identifed DEGs between consecutive stages (25DAF vs 20DAF).

  PublisherDOI

• Additional file 13 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 13: Table S13. qRT-PCR primers used in this study.

  PublisherDOI

• Additional file 1 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 1: Table S1. The expression level of all the annotated genes in S. grosvenorii genome.

  PublisherDOI

• Additional file 10 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 10: Table S10. GO functional enrichment of DEGs (25DAF vs 20DAF).

  PublisherDOI

• Additional file 6 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 6: Table S6. The identifed DEGs between consecutive stages (30DAF vs 25DAF).

  PublisherDOI

• Additional file 8 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 8: Table S8. GO functional enrichment of DEGs (15DAF vs 10DAF).

  PublisherDOI

• Additional file 7 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 7: Table S7. GO functional enrichment of DEGs (10DAF vs 5DAF).

  PublisherDOI

• Additional file 4 of Genome-scale transcriptomic insights into the gene co-expression network of seed abortion in triploid Siraitia grosvenorii

  Figshare · 2022-01-01

  datasetOpen access

  Additional file 4: Table S4. The identifed DEGs between consecutive stages (20DAF vs 15DAF).

  PublisherDOI

Recent grants

• NIH Grant R21HG004130

  NIH · $501k · 2009

• CAREER: Engineering and Integrated Microfluidic System for High Throughput Genomic Analyses

  NSF · $400k · 2006–2011

• NIH Grant R21HG003587

  NIH · $731k · 2008

• NIH Grant R01HG007836

  NIH · $3.6M · 2019

• NIH Grant R21HG005096

  NIH · $1.1M · 2013

Frequent coauthors

• Juan Xu

  Chaohu Hospital of Anhui Medical University

  52 shared

• Haifeng Yan

  University of Chinese Academy of Sciences

  38 shared

• Xiyang Huang

  Guangxi Institute of Botany

  38 shared

• Rongchang Wei

  Guangxi Academy of Agricultural Science

  38 shared

• Zuliang Luo

  Institute of Medicinal Plant Development

  31 shared

• Nan Cui

  First Hospital of China Medical University

  25 shared

• Xiaojun Ma

  Guangzhou University of Chinese Medicine

  25 shared

• Xiaojun Ma

  Ministry of Education of the People's Republic of China

  20 shared

Labs

• Huang Lab @ UCSD BioengineeringPI

Education

• Ph.D., Biophysical Chemistry

  Stanford University

• Other, Genetics and Pathology

  Yale University School of Medicine

• Other

  Harvard Medical School

Awards & honors

• NSF CAREER Award