Colloquium Series: Alan Amin, "From Overlooked Data to Drug Design: Learning from Nature's Experiments"

Thursday, March 12, 2026
11:00 AM 12:30 PM 11:00 12:30

Bahen Centre for Information Technology, Room 3200 40 Saint George Street Toronto, ON, M5S 2E4 Canada (map)

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Speaker:

Alan Amin

Talk Title:

From Overlooked Data to Drug Design: Learning from Nature's Experiments

Date and Location:

Thursday, March 12, 2026

Bahen Centre for Information Technology, BA 3200

This lecture is open to the public. No registration is required, but space is limited.

The grad roundtable that follows the talk is open only to current University of Toronto Department of Computer Science graduate students.

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 challenge isn't the lack of data; it is that off-the-shelf machine learning models trained with traditional pipelines are insufficient for these problems. 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.

About Alan Amin:

Alan Amin is a Faculty Fellow at NYU’s Courant Institute with a Ph.D. in Systems Biology from Harvard. He develops machine learning methods for designing biological sequences, with research spanning generative models, accelerated linear algebra, and applications to disease and therapeutics. His work has been highlighted at top venues such as ICLR and ICML, and he has collaborated with industry partners including BigHat Bio and Jura Bio.. He completed his undergraduate studies at the University of Toronto, specializing in Biochemistry with a Major in Mathematics, and took advanced coursework in measure theory, operator theory, and abstract algebra.