What has your career path been so far, and how did you come to your current position?
During my undergrad studies in mechanical engineering, I was not a fan of thermodynamics. All I liked was making robots. It felt like you could code and bring scrap material to life. That feeling was very cool to me. I enjoyed studying about the kinematics and dynamics of robots. I wanted the robots to do something animals can do, like walk, jump, and move stuff. This inspired me to pursue a master's in control systems, where I learned about making controllers for these robots. Still, as control engineers, we were taught to develop very specific controllers for very specific situations a robot may find itself in. When the environment was unpredictable, the controller was usually a question mark. I kept wondering how to animals do it, how they are so adaptable, and how we can make a robot do the same? Then I was offered this PhD project, which deals with modeling natural intelligence. This was the next logical piece of my puzzle, and I picked it up. Ironically, I still have to study thermodynamics because of the application of its 2nd law in designing neural networks. But now I enjoy it.
What is your favorite book and why?
Right now, my favorite book is "The Joy of x" by Steven Strogatz because it does this wonderful job of turning this giant monster called "Mathematics" into someone you can cuddle with. This outlook is really important in research. During meetings, conferences, discussions, and subjects, experts use terms and concepts that can be overwhelming, fueling imposter syndrome. But in that moment, it is important to know that every complex idea, given some time and reading, can be turned into a few simple ones and grasped easily.
What is the most important advice you want to share with Donders PhD candidates?
Something straight out of my simulations, which I expect my artificial agents to do be adaptable while optimizing the objective. More often than not, things go out of script in a PhD. Being adaptable is the key.