About Jeff Hammerbacher
Jeff Hammerbacher, cofounder of Cloudera and an assistant professor at the Icahn School of Medicine at Mount Sinai, has focused his recent work on applying data science to biomedical research, particularly cancer immunotherapy. In a 2020 talk, he described a relation extraction project on biomedical literature aimed at understanding T cell function and differentiation, noting that immune checkpoint blockade is forecast to generate over $100 billion in sales by 2024 and that over 2,000 clinical trials for such therapies are active. He has emphasized the importance of open science, stating that his lab’s pipeline, epiD, is open source under an Apache 2.0 license and that all development occurs publicly on GitHub to allow others to reperform analyses.
Hammerbacher has also spoken about the challenges of translating high-throughput web experimentation to healthcare, arguing that the field needs to conceive of healthcare delivery as a high-frequency, low-cost touchpoint with patients to enable rapid learning. He has criticized the tendency of institutions to outsource data infrastructure to large tech companies, calling such use cases “just marketing” from a Silicon Valley perspective. In earlier talks, he discussed the ethical implications of data collection, stating that “the best minds of my generation are thinking about how to make people click ads” and that decisions about what to measure involve “implicit political, moral, and ethical choices.”
Source: AI-verified profile updated from Jeff Hammerbacher's recent appearances.
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Transcript (1 segments)
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Jeff Hammerbacher0:10
I linked a providing of care and the theory of which is the best care to provide. You have to remain close to this thing which you create, and the thing that you created is understanding of predictions. So if you are generating predictions in terms of sending them out into the world and not participating in their deployment and evaluation, then you're going to do much worse. So the more we can do, you know, there's actually a very nice talk in my world from a guy named Brett Victor who designs development environments for programmers, and he talks about creators need to be close to the things that they create. So it's the same notion: when you build something, you have to watch it perform, and then you'll learn from how it performs to iterate and change what you've built. The closer that you can hue to the representation of reality of what's actually happening in the process. My experience on Wall Street, I worked there briefly, was that we began to sort of the models began to slowly drift away from the actual physical practice of trading. I could be working on a stochastic differential equation for two weeks, and I implement some numerical solution to it, and wander down to the trader and talk with him about, hey, how are you going to use this thing? It's a well, you know, it's this market that just started three months ago, and there's seven traders in it, and the guy from Goldman knows the guy from Lehman from school, so they often trade together. I was just thinking, gosh, all this information, the mental model that this guy has in his head for what's actually happening is not present anywhere in my model. So the closer you can stay to the actual act that's being performed in this universe of atoms, the tighter digital representation to the realm of atoms, I think the better off you're going to be from creating these really dangerous mismatches between theory and reality.
