Employee spotlight: Gaurav Kandoi
What is your role at Invaio?
As a Senior Scientist in Computational Biology, my role sits at the intersection of biological insight and computational innovation. I design and apply algorithms, machine learning models, and data-driven frameworks to understand complex biological systems and help guide the discovery and optimization of our Biologicals By Design™ solutions. My core focus is turning diverse datasets into actionable insights that accelerate scientific decisions and strengthen our product pipelines.
How can machine learning, artificial intelligence, and data science help us predict the most promising biological candidates and interactions before we move to lab validation?
Machine learning and AI allow us to sift through enormous biological search spaces far faster than traditional methods. Instead of testing thousands of candidates experimentally, we can train models to predict which biologicals, interactions, or modes of action are most likely to succeed. By integrating -omics, structural, and phenotypic data, these models uncover subtle patterns that humans may miss, enabling us to focus lab resources on the most promising—and most biologically meaningful—options.
In what ways can computational models accelerate and optimize our design cycles for biological innovation?
Computational modeling enables us to run “virtual experiments” at a fraction of the time and cost. Whether we’re testing design strategies, optimizing bioactive delivery, or predicting performance across conditions, models allow rapid iteration and scenario testing. This shortens our design–build–test–learn cycles and helps us converge on optimized biological solutions more quickly, with data-backed confidence.
How does bringing together biology and machine learning help us understand living systems in new ways?
Living systems are incredibly complex — shaped by networks, feedback loops, and adaptive behavior. Machine learning helps us detect nonlinear relationships, emergent properties, and system-level dynamics that are difficult to analyze using traditional methods. By uniting wet-lab science with computational approaches, we can move from descriptive biology (“what happens?”) to predictive biology (“what will happen and why?”), resulting in deeper mechanistic understanding and more innovative solutions.
How can data-driven discovery speed up our ability to create sustainable products that are better for people and the planet?
Data-driven discovery helps us identify biological pathways and molecules that are both highly effective and inherently sustainable. By modeling environmental impact, stability, and biological efficacy early in development, we can steer our innovation toward solutions that minimize chemical inputs, reduce environmental load, and leverage the natural potential of biological systems. This makes sustainability not an afterthought, but a built-in design principle.
How does a data-driven, computational approach transform the way we discover, optimize, and deploy our Biologicals By Design solutions?
Computational workflows make our discovery more targeted, our optimization more efficient, and our deployment more predictive. They enable us to:
- Anticipate performance under diverse field conditions
- Personalize biological solutions to specific crops, pests, or microbiomes
- Rapidly adapt designs based on real-world data feedback
This integrated approach ensures that our Biologicals By Design are not only scientifically robust but also scalable, reliable, and tuned to the needs of growers and ecosystems.
What new possibilities open up when we combine the creativity of biology with the power of computation?
At this intersection, we unlock the ability to engineer biology with intention. Computation broadens our search space while biology provides the raw creative potential of evolution. Together, they enable us to imagine solutions once considered impossible—from precision microbial interactions to next-generation bioactives and delivery systems. It’s a future where innovation is limited not by trial and error, but by how boldly we design.
Gaurav Kandoi is a Senior Scientist in Computational Biology at Invaio, where he specializes in building predictive models and computational frameworks that accelerate biological innovation. With a background in machine learning, bioinformatics, and systems biology, Gaurav is passionate about using data-driven tools to decode complex biological systems and transform them into practical solutions for sustainable agriculture. His work bridges multiple disciplines to help design biological products that are effective, environmentally aligned, and rooted in deep scientific understanding.