Celebrating Our Second Unlearniversary

Today marks Unlearn's second birthday - what we fondly call our unlearniversary.

As we celebrate two years as a company, we are thrilled to see how much we've grown and accomplished. We made our first home in a humble 90-square-foot coworking office in downtown San Francisco. The initial team consisted of four friends: co-founders Charles, Jon, and Aaron plus Graham, our first hire, COO, and "business guy."  

Two years later, we've outgrown two coworking offices and decamped to our own real office a block away from Union Square. The office came fully furnished (thanks, Zagster!), but we've also added two new prized possessions, a Portuguese phone booth and a Swiss Nespresso machine.

Our team has also doubled in size over the past year, with the addition of a senior computational biologist, communications specialist, chief commercial officer, and machine learning scientist. As Unlearn grows, we are excited for the opportunity to build a team that reflects the diversity of the patients and scientists we serve and the challenges we hope to solve together. But for us, one of the best parts of growing is having more people at the lunch table - our favorite place to share thoughts about everything under the sun.

Lastly, we reached some key milestones this year. We published several papers, most recently on our Alzheimer's disease model. Our Alzheimer's disease paper attracted some media attention. And to top it all, we recently obtained new funding to continue helping patients and scientists develop new treatments for complex diseases.

We are grateful for the people who have helped us - investors, advisers, and our families and friends - and we are excited to see Unlearn grow even more this coming year.

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Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins


Bayesian prognostic covariate adjustment


Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 2020)

We showed that digital twins could reduce the number of control subjects required in the analysis to achieve equivalent results to an analysis of the actual subjects.
Our novel method - Bayesian prognostic covariate adjustment - is a Bayesian analysis that draws on the strengths of the prognostic model approach.
Here, we have demonstrated that a particular type of generative model (i.e., CRBMs) can be used to accurately model disease progression for patients with MCI or AD.