When the noted AI expert and ex-Stanford star Daphne Koller was recruited to Google’s Calico Labs in 2016, it fit into an upbeat narrative about how Arthur Levinson was building an amazing team of top professionals to pioneer anti-aging research. A few days ago, though, Koller abruptly left the company and that narrative is getting some fresh scrutiny in R&D circles.
“I have decided to leave Calico to pursue other professional opportunities,” Koller said in an emailed statement to Bloomberg, which first reported the departure. “I very much enjoyed my time at Calico, and have the greatest respect for the Calico team and their important and aspirational mission.”
This is the second big defection for Calico in recent months. Top scientist Hal Barron jumped to helm the R&D group at GlaxoSmithKline. But Barron was wooed away with a top job and a turnaround mission, with a multimillion-dollar annual pay package and his own office in the Bay Area, where GSK has little presence.
Koller had only recently attracted attention for her discussion of new mouse data they were accumulating in the lab, part of a pricey effort to develop new therapies that could make longer lives healthier. That was a departure for Calico, which has kept its head down low since Levinson was brought in by Google’s Larry Page to build the company.
More recently, there were some rumors circulating in the AI world that Koller had grown somewhat disenchanted with Calico.
That certainly wasn’t part of her public profile, though. Here’s a comment from Koller’s LinkedIn page.
Calico allowed me to return to my passion of applying machine learning to improve human health. As Calico’s first Chief Computing Officer, I built an amazing team working on new computational methods for analyzing biological data sets to help better understand the process of aging and developing interventions that enable people to live longer, healthier lives.
The use of new artificial intelligence tech in drug discovery is fast becoming one of the biggest trends in drug R&D as the majors start to develop ambitious internal efforts to find a faster and more efficient way to identify potential breakthroughs. But it still has a long way to go before it can prove itself to the industry.
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