SynBioBeta Speaker
Nima Alidoust
Tahoe Therapeutics
CEO & Co-founder
Nima Alidoust is the Co-Founder & CEO of Tahoe Therapeutics, a biotech company using large-scale single-cell data and AI to build virtual cell models for precision drug discovery. Under his leadership, Tahoe produced Tahoe-100M — a groundbreaking dataset of 100 million single-cell datapoints — and is now scaling toward a billion-cell dataset to train next-gen biological foundation models.
SynBioBeta 2026 Tickets are Live
Confirmed Speakers
Sessions Featuring
Nima
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.
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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 Therapeutics
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.
Get a Ticket
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 Therapeutics
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
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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?
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Featuring
Speaker Coming Soon
