Gunjan Baid

I'm a researcher based in San Francisco, CA. My interests include applications of machine learning to problems in health and genomics. Previously, I was at Google Research where I worked on genomics and multimodal machine learning. I completed my Masters and undergraduate studies at UC Berkeley, where I was a part of the RISELab.

At Google, I developed DeepConsensus for error correction in PacBio sequencing data and worked on DeepVariant for small variant calling. My work in multimodal ML includes text-to-image generation with Parti and Multisearch for Google Lens.

Email / Google Scholar / Twitter / GitHub

Research

	DeepConsensus: improving accuracy of genomic sequencing Nature Biotechnology, 2022 Paper / Blog Post / GitHub DeepConsensus uses a Transformer to reduce errors in PacBio Circular Consensus Sequencing (CCS) data by 59%. This method is now deployed on PacBio Revio sequencers. Awarded Best Scientific Breakthrough (one project across Alphabet).
	Scaling autoregressive models for content-rich text-to-image generation TMLR, 2022 Paper / Website / Blog Post / GitHub Parti (Pathways Autoregressive Text-to-Image) is an autoregressive text-to-image generation model that achieves high-fidelity photorealistic image generation and supports content-rich synthesis involving complex compositions and world knowledge.
	PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions Cell Genomics, 2022 Paper / Challenge Results / Blog Post This challenge assessed variant calling pipelines on reference datasets, with a focus on difficult-to-map regions, segmental duplications, and the Major Histocompatibility Complex (MHC). DeepVariant recevied best overall accuracy in 3 out of 4 sequencing instrument categories, and we released an improved version of the submitted model as DeepVariant v1.0.
	Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing Nature Biotechnology, 2022 Paper / Blog Post This pipeline performs distributed whole-genome Nanopore sequencing, near real-time base calling and alignment, accelerated variant calling, and custom variant prioritization. We received a Guiness World Record for fastest DNA sequencing technique.
	Ultrarapid nanopore genome sequencing in a critical care setting New England Journal of Medicine, 2022 Paper Whole-genome sequencing was performed for twelve patients in critical care using an accelerated pipeline. The shortest time from arrival of the blood sample to the initial diagnosis was 7 hours 18 minutes. A pathogenic or likely pathogenic variant was identified in five out of 12 patients.
	Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads Nature Methods, 2021 Paper / GitHub PEPPER-Margin-DeepVariant is a haplotype-aware variant calling pipeline for Oxford Nanopore data that produces state-of-the-art results.
	DeepTrio: variant calling in families using deep learning bioRxiv, 2021 Paper / Blog Post We extend DeepVariant to to consider the genetic variants in mother-father-child trios. Coexamination of available parental data improves the accuracy of variant calls for the child. The DeepTrio model was released as part of DeepVariant v1.1.
	An extensive sequence dataset of gold-standard samples for benchmarking and development bioRxiv, 2020 Paper / Dataset We provide a benchmark dataset of WGS and WES samples from HG001-7 and NA12878/9 from a variety of sequencers. We characterize properties of the data and present variant calling results on these samples.

Design and source code from Jon Barron's website.