Zelda Mariet, Co-Founder and Principal Research Scientist at Bioptimus, joins me to continue our series of conversations on the vast possibilities and diverse applications of foundation models. Today’s discussion focuses on how foundation models are transforming biology. Zelda shares insights into Bioptimus’ work and why it’s so critical in this field. She breaks down the three core components involved in building these models and explains what sets their histopathology model apart from the many others being published today. They also explore the methodology for properly benchmarking the quality and performance of foundation models, Bioptimus’ strategy for commercializing its technology, and much more. To learn more about Bioptimus, their plans beyond pathology, and the impact they hope to make in the next three to five years, tune in now.

Key Points:

• Who is Zelda Mariet and what led her to create Bioptimus.
• What Bioptimus does and why it’s so important.
• Why their first model announced was for pathology.
• Zelda breaks down three core components that go into building a foundation model.
• How their histopathology foundation model is different from the number of other models published at this point.
• Their methodology behind properly benchmarking how well their foundation model performs.
• Different challenges they’ve encountered on their foundation model journey.
• How they plan to commercialize their technology at Bioptimus.
• Thoughts on whether open source is part of their long-term strategy for the model, and why.
• Developing a product roadmap for a foundation model.
• She shares some information regarding their next step, beyond pathology, at Bioptimus.
• The importance of understanding what kind of structure you want to capture in your data.
• Where she sees the impact of Bioptimus in the next three to five years.

Quotes:

“Working on biological data became a little bit of a fascination of mine because I was so instinctively annoyed at how hard it was to do.” — Zelda Mariet

“Bioptimus is building foundation models for biology. Foundation models are essentially machine learning models that take an extremely long time to train [and] are trained over an incredible amount of data.” — Zelda Mariet

“There are two things that are well-known about foundation models, they’re hungry in terms of data and they’re hungry in terms of compute.” — Zelda Mariet

“On the philosophical side, science is something that progresses as a community, and as much as we have, what I would say is a frankly amazing team at Bioptimus, we don’t have a monopoly on people who understand the problems we’re trying to solve. And having our model be accessible is one way to gain access into the broader community to get insight and to help people who want to use our models, get insight into maybe where we’re not doing as well that we need to improve.” — Zelda Mariet

Links:

Zelda Mariet on LinkedIn

Zelda Mariet

Bioptimus

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