Tasha Kim, MS, Computational & Mathematical Engineering '25

I used to imagine the things I wanted to do in terms of x, then y, then z. But now, I think more about the value I want to bring to the world, and I believe that value comes naturally when you’re pursuing what you’re interested in and giving that idea life.

I was a math major at Brown University during the pandemic, and during that period when we couldn’t go out much, I spent a lot of time watching live streams of programmers. That led to me developing my own open-source software and, later, doing some software development for the federal government during my senior year. 

After graduation, I landed a job involving both software and hardware development for Google, where I also had the opportunity to talk to people from Google Deep Mind, including Google fellow and Stanford alum Claire Cui. Many of them encouraged me to pursue a degree here at Stanford so I could contribute in a more significant way to research. I decided to apply to ICME, in part because the department is largely a blend of math and computation and a natural fit with the work I’d done at Brown, but also because ICME provides the opportunity to earn an MS degree through the Honors Cooperative Program (HCP), which makes it possible to pursue a master’s degree while you’re working.  

After starting my degree work, I decided I wanted to dedicate myself to that task exclusively and join Stanford ICME as a full time MS student, so I left Google, telling my team and managers that I’d finish my degree, learn what I needed to know, and come back to contribute to the field in a stronger way. I had some trepidation making that decision, but I’d become increasingly interested in AI and robotics after talking with the people doing that work at Deep Mind, and I had confidence that prioritizing my time at ICME aligned with the things I want to do long-term.

At ICME I first worked with my wonderful advisor Mykel Kochenderfer in the Stanford Intelligent Systems Laboratory (SISL), where I focused on collision avoidance algorithms for robots, which is about understanding how robots can be designed to harmoniously walk around and interact with humans in public settings. I also worked with advisors Fei-Fei Li and Jiajun Wu at the Stanford Vision and Learning Lab (SVL) on a large multi-lab project looking at brain-robot interaction, which involves translating biological signals - like the brainwaves of a human being - into a form that a robot can use to execute a task.

I’ll be continuing some of that work this fall when I start my PhD at the Oxford Robotics Institute at Oxford University, where I’ll focus on brain-robot interfacing and human-robot interaction. As technology advances over the next century and beyond, I’m interested in helping find the best and safest ways for humans to leverage machines in ways that augment our lives and work with us, rather than against us.

Challenges in human-machine cooperation are going to come along, but if we start by making difficult tasks a little bit easier for us, perhaps that will evolve into something much larger. I believe in the synergy and collective intelligence of humans. As long as we’re able to check each other, talk to each other, and cultivate values that are important to us as human beings, I think we’ll be fine. All we can do is work on the next step forward.