About
I am a researcher working at the cross-section of machine learning and genomics. The goal of my research is to use deep learning models to identify biologically meaningful patterns in the human genome, reveal the molecular mechanisms underlying complex diseases, and develop new therapeutics.
Currently, I work as a Principal AI Scientist at Genentech (Roche). 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
