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AMS Special Seminar Series | Alan Amin

January 22 @ 1:30 pm - 2:30 pm

Location: Shaffer 3

When: January 22nd at 1:30 p.m.

Title: Machine Learning for Nature’s Overlooked Experiments

Abstract: Large-scale efforts have amassed vast biological sequence data, enabling machine learning models to learn conserved motifs and advance protein engineering. Yet in many high-impact domains—shrinking proteins for therapeutic delivery, engineering antibodies for diverse targets, incorporating priors into underpowered genetic studies—practitioners still resort to mining homologues, starting from scratch, or using simple linear models. The barrier is not data scarcity but that models trained on canonical evolutionary signals fail when the relevant information lies elsewhere. My work demonstrates that large-scale biological data can be leveraged for these problems given the appropriate statistical and computational framework: learning how nature navigates combinatorial deletion space to build smaller proteins, how our immune systems optimize antibodies, and how flexible models can scale to massive genetic datasets without overfitting.

Bio: Alan is a faculty fellow at the Courant Institute at New York University, working with the Wilson lab. In the summer of 2023, he was a postdoc at Jura bio. He completed my PhD in the Harvard Systems Biology program supervised by Debora Marks in 2023. He graduated with a BS in Biochemistry and Mathematics from the University of Toronto in 2019.

Zoom link: https://wse.zoom.us/j/92755277282?pwd=iULpLaFnWAcWl6tQYUbeyZaN3zwBzn.1

Details

Venue

  • Shaffer 3
  • 3400 N Charles Street
    Baltimore, MD 21218 United States     + Google Map