Zach Lipton

And like that was the community.

But that was that was kind of my idea.

It was like machine learning was my way in to have an impact in health care.

And I ended up backing up very far from like the kind of impact I wanted to have into the theory of machine learning.

neural network methodology, deep learning was exploding at the time.

I got to be like kind of a core member of the community and build a whole career as an academic in machine learning.

But I always kept that thread in healthcare.

And it was always kind of like working backwards from that personal experience as being a patient, wanting the

the guanine system to be better.

So I was like sitting on two sides.

I had one office in the medical school and one in the school of computer science.

I was funded by a grant from the NIH that came through the medical school.

My advisor was a professor of computer science and machine learning.

And I spent a lot of that early, you know, this like big epochal moment was 2012.

There was a paper published out of, by Jeff Hinton's lab at University of Toronto that was a breakthrough result using neural networks for image classification.

very quickly folks started catching on and other folks were able to figure out how to train neural networks for, for, for various image problems.

And they started taking off and there was this, there was still this general feeling in the field that like neural networks were a backwater and kind of, um,

you know, a not so rigorous area that was like more appealing because of storytelling than because of practicality.

And suddenly there was this area inside computer vision, suddenly neural networks were really working.

And I think it really helped that, you know, I'd been a musician, so I actually came into PhD with almost no prior commitments about methodology and also no skills that I had already invested in such that like I was...