Answering an unanswered research question is a struggle. You do not know what the solution is, what it might look like, or if it even exists.

In Chapter 1 of Not My Thesis, Tal Einav talks about his particular version of this struggle, when building mathematical models to predict the unpredictable behavior of antibodies binding viruses or of enzymes binding DNA. Building a model that predicts complex data is easy; rather, the challenge is finding a model that uses a few simple ideas to (mostly) predict complex data. He explains how he unexpectedly finds the struggle to be the most productive part of discovery and how that informs his approach to teaching (https://magazine.caltech.edu/post/socaltech-tal-einav).

You can find his paper on RNA polymerase here (https://www.pnas.org/content/116/27/13340.short) and HIV (https://www.sciencedirect.com/science/article/pii/S2405471219303151) here. He is currently a post-doctoral scholar at the Fred Hutchinson Cancer Center in Seattle.

You can contact us by emailing notmythesis@gmail.com. Music for this episode was provided by Blue Dot Sessions, and our logo is by Usha Lingappa. Find more Caltech Letters content at https://caltechletters.org/podcasts/.