About
I am a researcher working at the cross-section of machine learning and genomics. The goal of my research is to use machine learning methods, particularly deep learning, to identify patterns in human genomic data that reveal the molecular mechanisms underlying complex diseases and result in new treatments.
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.
My areas of expertise include functional genomics, next-generation sequencing, single-cell and spatial genomics, cancer genomics, machine learning and deep learning.
- Industry Resume (October 2022)
- Publications and Patents
- Peer Review
- Grant Review
- Public speaking
- Contact Me
- Follow Me
Industry Resume
Publications and Patents
Peer Review
I actively participate in peer review in the areas of computational biology and machine learning. I have performed 76 reviews of 55 papers for leading journals and conferences, including Nucleic Acids Research, Communications Biology, PLOS Computational Biology and Scientific Reports. Please reach out to me via the email below for peer review.
Grant Review
I actively participate in grant review for funding bodies in the areas of computational biology and machine learning. I have reviewed recently for the Chan Zuckerberg Initiative. 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:
2022
- Optimizing Drug Target ID Through AI (podcast with Biorad.io)
- Data Science in the Era of Digital Biology, San Francisco State University
- Machine Learning for Genomics, University of Miami
2021
- 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
2020
- Accelerating Single-cell Genomics with Machine Learning and Deep Learning, Indian Institute of Technology (IIT) Delhi
- Real Time, GPU-Accelerated Analysis and Visualization in the Life Sciences (Invited keynote), Ken Kennedy Institute Virtual Data Science Conference, Rice University
Contact Me
Email: avantikalal02 AT gmail DOT com
Follow Me
