About
I am a biologist and AI researcher working to decode the regulatory language of the human genome. My work combines large-scale genomics with deep learning to model the mechanisms of gene regulation, understand disease biology, discover novel drug targets, and design therapeutic DNA molecules.
Currently, I work as a Principal AI Scientist at Genentech (Roche), where I lead efforts to build foundation models and generative models for DNA/RNA, and to create scalable software that enables deep learning across genomics research. Previously, I was a Senior Data Scientist at Insitro, Senior Scientist in Deep Learning and Genomics at NVIDIA Research, and a Postdoctoral Researcher in the Departments of Genetics and Pathology at Stanford University.
- Industry Resume (September 2024)
- Publications and Patents
- Peer Review
- Public speaking
- Contact Me
- Follow Me
Industry Resume
Publications and Patents
Peer Review
I actively participate in peer review in the machine learning + genomics field, including for Nature Genetics, Nature Methods, and NeurIPS. Please reach out to me via the email below.
Public speaking
I frequently give academic and industry talks on the topic of machine learning in genomics. A few selected recent talks:
2025
- Upcoming: Decoding sequence determinants of gene expression in diverse cellular and disease states, Annual Meeting of the American Society of Human Genetics (ASHG), Boston, MA, 2025.
- Decoding sequence determinants of gene expression in diverse cellular and disease states, Biology of Genomes, Cold Spring Harbor Laboratory, NY, 2025.
- AI-Guided Design of Nucleic Acids for Therapeutic Applications, Society for Lab Automation and Screening (SLAS) 2025 Conference, San Diego, CA, 2025
2024
- gReLU: A Comprehensive Python Framework for DNA Sequence Modeling and Design, Machine Learning in Computational Biology (MLCB), Seattle, WA, 2024
- Decima: Decoding sequence determinants of gene expression in diverse cellular and disease states, Machine Learning in Computational Biology (MLCB), Seattle, WA, 2024
- “regLM: Designing realistic regulatory DNA with autoregressive language models”, 28th Annual International Conference on Research in Computational Molecular Biology (RECOMB), Boston, MA, 2024
Older
- Machine Learning Tools to Analyze Gene Expression and Regulation, National Cancer Institute, Bethesda, MD
- Accelerated Computing and Deep Learning for Single-cell Genomics, GPU Technology Conference (GTC)
- Deep learning Based Enhancement of Epigenomics Data with AtacWorks, MIT Department of Computer Science
- Accelerate and scale genomic analysis with open source analytics, Google
- GPU-Accelerated Single-Cell Genomics Analysis with RAPIDS, Chan Zuckerberg Institute (CZI)
- Machine Learning in Bioinformatics, DNANexus
Contact Me
Email: avantikalal02 AT gmail DOT com
Follow Me
