SynBioBeta Speaker
Avantika Lal
Genentech
Principal ML Scientist II
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 understand the regulatory syntax of the human genome, reveal the molecular mechanisms underlying complex diseases, and design new therapeutics.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Avantika
This Year
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AIxBIO
The Data Reality Check: Human-First Biology for AI Models
Why do so many in silico models fail when moved to the lab or clinic? Too often, they’re trained on incomplete, non-human, or non-representative datasets. This session tackles the “data gap” head-on: from interoperability bottlenecks and the black box problem to the limits of current virtual cell simulations (~50 million perturbations vs. the billions biology demands). Panelists will explore how to create “human-first” datasets that reflect real biology, unlock mechanistic interoperability, and close the discovery–development divide. The goal: build AI tools that can directly identify viable drug candidates instead of stalling in silico.
Purchase Pass
Featuring
Krish Ramadurai
AIX Ventures
Partner
TechBio investor backing AI-designed drugs and breakthroughs.
Julie O'Shaughnessy
Vivodyne
COO
Operational scale-up leader building a predictive human-tissue platform.
Nima Alidoust
Tahoe
CEO & Co-founder
Built Tahoe-100M: 100M single-cell dataset powering virtual cell models.
Avantika Lal
Genentech
Principal ML Scientist II
Building DNA foundation models that design regulatory sequences.
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AIxBIO
The Data Reality Check: Human-First Biology for AI Models
Why do so many in silico models fail when moved to the lab or clinic? Too often, they’re trained on incomplete, non-human, or non-representative datasets. This session tackles the “data gap” head-on: from interoperability bottlenecks and the black box problem to the limits of current virtual cell simulations (~50 million perturbations vs. the billions biology demands). Panelists will explore how to create “human-first” datasets that reflect real biology, unlock mechanistic interoperability, and close the discovery–development divide. The goal: build AI tools that can directly identify viable drug candidates instead of stalling in silico.
Purchase Pass
Featuring
Krish Ramadurai
AIX Ventures
Partner
TechBio investor backing AI-designed drugs and breakthroughs.
Julie O'Shaughnessy
Vivodyne
COO
Operational scale-up leader building a predictive human-tissue platform.
Nima Alidoust
Tahoe
CEO & Co-founder
Built Tahoe-100M: 100M single-cell dataset powering virtual cell models.
Avantika Lal
Genentech
Principal ML Scientist II
Building DNA foundation models that design regulatory sequences.
Session lineup still growing
Purchase Pass
Featuring
Speaker Coming Soon
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Human Health
From Cells to Patients: Solving the Scale Mismatch in Virtual Biology
Drug discovery often measures biology at the cell level while interventions work at the tissue, organ, or whole-patient scale. This mismatch can make accurate cell-level predictions irrelevant in the clinic. This session dives into strategies to bridge that gap: multiscale modeling that nests single-cell dynamics within organ-level simulations, spatial transcriptomics that preserve context, and surrogate models that translate cell-level outputs into clinical biomarkers. Speakers will ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the real complexity of patients?
Purchase Pass
Featuring
Speaker Coming Soon
