Daphne Koller
Founder and CEO, insitro. On 2 shows, 3 appearances, plus 1 compilation re-air not counted. The Shows tab opens the full record on each.
founderexecutiveacademicscientistauthor@DaphneKoller ↗LinkedIn ↗ai.stanford.edu/~koller ↗Wikipedia ↗
Daphne Koller is the founder and CEO of insitro, a biotechnology company applying machine learning to drug discovery and development. A former Stanford University computer science professor, she is also the co-founder of Coursera and co-author of the textbook Probabilistic Graphical Models.
2shows
3appearances
46statements
5resolved
4supported
0contradicted
80%fully supported
23said about them ↓ Everything Daphne Koller said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Koller: General artificial intelligence is not right around the corner
“I think one of the big risks that we run as a machine learning community is the incredible amount of hyperbole that's going on right now, where it's like, we're gonna have general intelligence right around the corner. We're not. Ok, we really aren't.”
Koller: AI requires synthesizing deep learning with causal and interpretable modeling
“What I think we're starting to see right now is a the pendulum starting to swing back in the sense that there is a greater understanding that you really need a bit of both. You need that hugely powerful pattern recognition that we get from deep learning, but y…”
Koller: Insitro will deliver AI-discovered medicines to patients by 2030
“The hope is that by the end of the Of this decade, we will have built this process, we will have run through it a number of times, we will have delivered some medicines to patients in our first tranche of indications, but then we will have learned enough from …”
Koller: AI requires synthesizing deep learning with causal and interpretable models
“What I think we're starting to see right now is a the pendulum starting to swing back in the sense that there is a greater understanding that you really need a bit of both. You need that hugely powerful pattern recognition that we get from deep learning, but y…”
Koller: Tech workers entering biotech often disrespect biological challenges and create friction
“There's a lot of tech people who come in To life sciences, and it's like, we have that cell verbal. We are the smartest. We're machine learning. We're going to solve everything. And they don't respect the challenges of the other discipline. They sometimes don'…”
Koller: Biological ML requires exploiting domain structure due to dataset limits
“We're still not in the large, large data regime where, you know, blind architectures that don't exploit structure of the problem can just work out of the box. So you really have to understand your problem domain and figure out how to exploit the structure that…”
Koller: insitro operates a unique data factory generating biological data on spec
“So one of the things that we have at Insitro that is truly unique is we have a data factory. We have put together the tools that have been developed by people who are taking pluripotent stem cells, which are cells from you or me or anyone in this audience and …”
Koller: Insitro runs genome-wide CRISPR screens in two weeks
“When they're all in a pool, you eliminate all of those artifacts, and all of a sudden you have the ability to measure a genome-wide CRISPR screen, basically, so, 20,000 genes in the genome, all modifying the same cellular background in the same dish with a dif…”
Koller: Human technician variance is a primary signal in biological ML
“When you do biological experiments, one of the strongest signals when you apply machine learning to it is what was the technician who actually did the experiments? You could read that very clearly off the cells because they behave a little bit differently.”
Daphne Koller: AI and quantitative biology are merging into 'digital biology'
“I think this time that we're living is the time when those last two disciplines are actually going to merge, and they're giving us an era of what I think of as digital biology, which is the ability to measure biology at unprecedented stability and scale, inter…”
Koller: Most drug failures stem from wrong target selection, not trial design
“It's a place where most programs fail is because we're just not modulating the right thing. It's the wrong target in the wrong indication or the wrong patient population.”
Koller: Insitro aims to replace untranslatable animal models using human cellular models
“Which ultimately what we're looking to do is to replace the sort of untranslatable animal models with something that is much more driven from human biology.”
Koller: Broad patient targeting to maximize revenue causes drug trial failures
“That is one of the big things that causes drugs to fail is that you are trying to apply it more broadly. If I'm being cynical, sometimes it's supposed to maximize the revenues that you can get from a drug versus trying to figure out exactly in which patients i…”
Koller: Computational methods cannot compress slow biological disease progression timelines
“And so ultimately there's only so much that you can speed up biology in certain cases because biology takes as long as it takes.”
Koller: Engineers look for patterns; scientists look for outliers
“When you show an engineer or computer scientist a bunch of dots
Usually the natural inclination is to try and find the pattern, the thing that explains as many of the points as you can, because that is the thing around which you will engineer your system.
If y…”
Koller: Healthcare tech adoption depends on workflow integration, not ML complexity
“It's not really about the machine learning inside the box. It's about how do you get it so that the physician doesn't even have to think about how to use your system. It just happens naturally.”
Koller: Tech founders in healthcare need domain co-founders or industry experience
“You really need to either spend serious time in either a hospital or a company, an existing company that actually has that as a market, or you get a co-founder who's had that.”
Koller: Theranos never published peer-reviewed papers or disclosed raw data
“We all know Theranos, you know, that's an extreme example, but, ah, the fact that they never had a peer-reviewed publication, they never presented their data in any way, they kept even potential customers from looking at the raw data, I mean, those are all rea…”
Koller: AUC-ROC curves rarely measure real-world performance
“The area under the ROC curve is rarely the thing that you actually care about. That was devised for radars back in the fifties, ok?”
Koller: Biological datasets became large enough for meaningful ML around 2016
“What brought me back to this field back in 2016 post Coursera was the realization that we can now finally, for the first time, measure biology at scale, both at the cellular level, sometimes at subcellular level, and at the organism level via ways of quantitat…”
Koller: ML lagged in life sciences due to lack of cross-disciplinary talent
“It wasn't having much of an impact in the life sciences, and I believe one of the main reasons for that is because there's so very few people who actually have the language of both disciplines and are able to bring them together.”
Koller: Human radiologists see only a small percentage of MRI data
“In MRI data, your radiologist doesn't see more than like a small percentage of what's there in your radiology images”
The other half of the tape: Daphne Koller's own voice is left out of every number here. Other people bring the name up 22 times in 7 episodes across the shows. 1 statement on the record names them. every mention, with the transcript →
Who brings them up most Sarah Guo 11Lukas Biewald 5Beyang Liu 2Martin Casado 1Kim Branson 1Balaji Srinivasan 1
Statements about Daphne Koller, by other people (1)
Biewald: Bayesian Networks Never Worked Well For Many Applications
“Daphne was actually really obsessed at the time with a thing called base nets, which you don't hear about too much anymore, because I don't think they ever really you know, worked for many applications. I hope I'm not offending anyone, but that's my understand…”
Every mention by year
tap a year for its mentions
No Priors 19
the a16z Podcast 3
2025 1 mention in 1 episode
2024 4 mentions in 3 episodes 1 per episode
One line per show, most statements first. The link opens Daphne's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| a16zLEDGER | Founder and CEO, insitro | 2 | 25 | 0% 0/1 | full record on the a16z Podcast → |
| NO PRIORSLEDGER | Founder and CEO, insitro | 1 +1 | 21 | 100% 4/4 | full record on No Priors → |