Seven current and former UC Berkeley engineers named top innovators under 35
A UC Berkeley postdoctoral fellow hoping to develop wearable sweat sensors for better health monitoring and a young assistant professor who helped pioneer “deep learning” to create more dextrous robots are among this year’s top innovators under 35, a list compiled each year by MIT Technology Review.
Wei Gao, 31, a postdoc in the lab of Ali Javey, and Sergey Levine, 29, a newly arrived assistant professor of electrical engineering and computer sciences and a former postdoc with the department’s Pieter Abbeel, were among 35 innovators announced today.
The two were joined by former UC Berkeley bioengineering postdoc and 2013 Ph.D. Kelly Gardner, 31, 2010 materials science Ph.D. Christine Ho, 33, and 2005 bioengineering graduate Heather Bowerman, 31, who were touted as “people who hope to turn innovations into disruptive businesses.”
Gardner worked with bioengineering professor Amy Herr before co-founding a startup, Zephyrus Biosciences, to market a kit that analyzes the proteins inside single cells. Gardner, who spun out her company after developing the technology at QB3’s Startup in a Box and the Skydeck incubator and through the Bakar Fellows program, is the firm’s CEO.
Ho launched a startup, Imprint Energy, in 2012 to commercialize thin, flexible, printable batteries that she developed at UC Berkeley while working in the laboratory of James Evans in the Department of Materials Science and Engineering. Thanks to help from the College of Engineering’s Sutardja Center for Entrepreneurship and Technology and the Haas Energy Institute’s Cleantech to Market, she was able to spin off the technology and hopes to have a product on the market by 2018.
Bowerman is the CEO of a Skydeck-incubated startup, Dot Laboratories, that is developing a cheap and easy way to test female sex hormone levels and track them online.
The honors this year went to “exceptionally talented technologists whose work has great potential to transform the world,” according toMIT Technology Review‘s announcement.
Wearable sweat sensors
All four have gotten kudos in recent years for their innovative technologies.
Levine’s interest in “deep learning” began during his graduate student years at Stanford, and in 2014 he came to UC Berkeley as a postdoc to work with Abbeel, who had gained fame by teaching robots to fold towels. He hopes that by linking learning with continuous feedback and control, robots might begin to match human proficiency even at basic sensorimotor skills like grasping, and in so doing make it possible for robots to intelligently and reliably handle the complexities of the real world.
In Abbeel’s lab he worked on robotic manipulation of toys, and one of his current projects is to improve motor control of robotic hands, allowing the robot to observe its own tasks and engineer its behavior to perform the tasks correctly. He is also interested in using deep learning to train autonomous drones and vehicles.
Gao published a major paper with Javey on the wearable sweat sensor in January that received global attention.
“It has been a dream since my childhood that if I can develop some wearable device that can help to monitor our health and give us early warning, it could lead to better treatment or ways to improve our health,” Gao said. “If we could know what is going on and what is going wrong with our body, that would be of great interest.”
UC Berkeley turned out to be the ideal place for Gao to start to realize that dream.
“I came here [to the Department of Electrical Engineering and Computer Sciences] because it combines my background of electrochemistry and materials with device development,” he said. “This allowed me to develop a fully integrated system for sweat analysis.”
A native of Xuzhou in China’s Jiangsu province, Gao obtained his master’s degree from Tsinghua University in Beijing and came to the U.S. in 2009 to get his Ph.D. from UC San Diego in chemical engineering.
In a paper published in January in Nature, Gao, Javey and postdoc Sam Emaminejad described a sweatband they built that combines multiple sensors with electronic processors and a Bluetooth transmitter on a flexible printed circuit board. The electronics wirelessly transmits data about what’s in a wearer’s sweat to a cellphone running an app.
In the device, two sensors interact with sweat components, including glucose and lactate, causing a detectable change in their electrical current. It also contains two sensors that change their voltage in response to sodium or potassium. A temperature sensor is used to measure skin temperature and calibrate the response of other sensors.
Since then, Gao and his colleagues have successfully detected heavy metals, pH – that is, how acidic the sweat is – as well as the electrolytes calcium and chloride.
“Currently we are working on a large-scale population-based study to determine the correlation between the sweat composition and different health conditions,” he said. “Hopefully, we can actually extract more useful information about our health, both from a physiological and a clinical point of view.”
Gao wears his smart wristband nearly every day as he rides an exercise bike in his lab, part of the lab’s efforts to understand what sweat measurements mean and how they can be used to improve people’s health. He and his colleagues are working with exercise physiologists and clinicians and on large-scale studies to look for correlations.
This year’s honorees will be featured online at MIT Technology Review starting today, and in the September/October print magazine, which hits newsstands worldwide on Aug. 29. They will appear in person at the upcoming EmTech MIT conference Oct. 18–20 in Cambridge, Massachusetts.