Julia Kiseleva, PhD
Product vision and incremental experimentation.
Genome-scale AI for biology
Programming biology with genomic intelligence.
We build ultra-long context genomic foundation models to predict disease risk, explain heritability, and design actionable edits from whole-genome and multi-omics data.
Mission
Explain the genome. Predict disease. Design the next intervention.
Genomic Intelligence is building the AI layer for biology: models that learn reusable genomic primitives, reason over long-range regulatory logic, and turn abundant DNA data into decisions for predictive health, drug design, and ag-bio.
Foundation models
Ultra-long context architectures pretrained on whole genomes to capture regulatory logic across megabases.
Predictive health
Variant effect, expression, and disease-risk prediction grounded in multi-omics, calibrated to clinical use.
Actionable design
From hypothesis to edit — close the loop between in-silico prediction and wet-lab validation faster.
Team
Interdisciplinary specialists in AI, biology, and software engineering.
Benjamin Fishman, PhD
Wet-lab validation and genomic model science.
Mikhail Burtsev, PhD
Ultra-long context models and memory architectures.
Alex Boldakov
Data platforms, infrastructure, and scale.
Advisory Board
Eric Horvitz, MD, PhD
AI-for-health strategy and ecosystem leadership (Microsoft CSO; founder of Stanford AI100; former AAAI President).
Interested in Genomic Intelligence?
Investors, partners, and researchers — let's talk about programming biology together.