Accelerating the discovery of therapies for aging

An opportunity to dramatically reduce disease

By targeting the biological processes of aging itself, we aim to uncover new therapies for the diseases of old age — from cardiovascular to neurodegenerative disease — and increase our healthy lifespans.

Accelerating research with machine learning

There's promising science behind delaying aging — we're building new tools to realize its potential faster.

Aging can be delayed...

Several lines of evidence have recently bolstered the belief that by understanding and treating the damage that accumulates in our bodies as we age, we will be able to slow or repair this damage with dramatic benefits for our health and lifespan.

The most studied lines of evidence for this claim are focused on the fields of senescence, genetic manipulation, heterochronic parabiosis, caloric restriction, and even the use of existing drugs like Metformin. We can reproducibly use such techniques to significantly increase the lifespan of animals while delaying the onset of multiple diseases — and this evidence has been built up by world-class scientists at UCSF, Stanford, and more of the world's best labs.

...and AI can help find therapies faster

We've built a machine learning platform to accelerate experimentation for discovering therapies for aging and its related diseases.

While the science of aging research is full of promising results, it still takes far too long to run experiments. This significantly slows down the search for therapies that could one day reduce cardiovascular disease, neurodegenerative diseases, arthritis, and more.

Spring Discovery's machine learning-based experimentation platform solves this. We're applying a novel computational approach to one of the most important problems in the world: battling aging and disease.


Bringing together aging biology and machine learning experts



Founding engineer and VP Engineering at Khan Academy. Early engineer and VP Engineering at Fog Creek Software.

Elabd, PhD

Lead Scientist

Director of Cell Biology at BioRestorative Therapies. Postdoc at Berkeley focused on skeletal muscle aging. Ph.D. from University Nice Sophia Antipolis.

Cousin, PhD

Lead Scientist

Product Team Leader at Turing Pharma. Postdoc at Dr. Conboy's lab in Berkeley working on aging. Ph.D. from University Nice Sophia Antipolis.


ML Eng Lead

Senior engineer and technical lead at Khan Academy. Senior engineer at Google.
Member of rescue team that helped fix


ML Engineering

Senior engineer and technical lead at Khan Academy.
Engineering lead and member of the early team at Cedar, a healthcare technology startup.

Lauren Nicolaisen

ML Engineering

Senior data scientist, engineer, and manager at Khan Academy and Chan Zuckerberg Initiative. Ph.D. in Physics and Pop. Genetics at Harvard.

Donne, PhD

Chief of Staff

Head of Ops at Age1. PhD in developmental and stem cell biology, UCSF. Stem cell specialist in Dr. Susan Fisher's lab.


World leaders in aging research, drug discovery, and tech



Cofounder of Color Genomics and Mixer Labs. Serial angel and entrepreneur.

Chang, PhD

Scientific Advisor

Head of Chang Lab and Professor of Dermatology at Stanford. Scientific cofounder of Epinomics.

Villeda, PhD

Scientific Advisor

Head of Villeda Lab for aging research at UCSF. PhD from Stanford with focus on reversing aging in the brain.

Kamb, PhD


Senior Vice President of Research, Head of Neuroscience Discovery at Amgen. President/CSO A2 Biotherapeutics.

Young, PhD


Vice President, Arcus Bio. Previously Executive Director and head of first automated screening lab at Amgen.



President of Y Combinator.
Co-chair of OpenAI.


Spring Discovery Brings Machine Learning to Longevity Sector
Spring is Diving into the Longevity Fight With a Fresh $18 Million


We are a team of scientists and engineers working closely together to increase healthy lifespan and dramatically reduce disease.

We value building a diverse, inclusive environment and welcome all applicants regardless of gender, sexual orientation, ethnicity, race, education, age, or other personal characteristics.

See open positions

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