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Jeannette Bohg

Jeannette Bohg

Assistant Professor
Computer Science
Story originally published on Jan 2019
One evening, last summer, I sat by a small fire along with about 20 high school girls from around the world.

The teenagers were participating in Stanford AI4All, a three-week residential program aimed at exposing young girls to the field of artificial intelligence, and we had gathered at Lake Lagunita to bond.

They were glowing from the inside they were so excited. As the group talked, one of the girls spoke candidly about harboring doubts. She said she worries that the dearth of women in computer science means doors are opening for her simply because of her gender – and not her talents.

I was empathetic. I’m a first-generation university graduate who grew up in communist East Germany. I’ve felt the same way and I have been, directly and indirectly, underestimated. I remember telling the girls that the contributions that women are making in science are just incredible and that girls and women deserve every opportunity they get.

Experiences like this are part of the reason why I’m passionate about mentoring young women in science and engineering. I’m developing courses in robotics and AI for low-income and underrepresented youths. I’m also organizing a networking workshop for female roboticists at academic conferences, and am working to secure funds that ensure that Stanford students can attend. Half of the 12 student researchers I’m now working with are women.

In my lab, we are working at the intersection of advanced robotics, machine learning and computer vision. My goal is to teach robots to see and move with the high degree of sophistication and spontaneity needed to work in a number of challenging environments, including homes, offices, hospitals, disaster zones and even underwater. This requires programming robots to pick up and manipulate objects that may be bulky, heavy or supple, like sand or fluids.

The challenges are exceedingly complex. Today, robots can be programmed to grab hold of and interact with specific items that are rigid. But they can’t work with objects that they’ve never seen before and under conditions that are in flux.

Computers can beat the world’s greatest human chess and go players, yet we haven’t figured out how to build robots that grasp and manipulate objects instantaneously. It’s such a great riddle. We don’t yet understand the fundamental principles that allow us, as humans, to move with such ease.

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