Tyler Lum
Building intelligent robots with learning-based perception and control
Hi, my name is Tyler Lum and I am a 2nd year Computer Science PhD student at Stanford University studying artificial intelligence and robotics. I am advised by Professor Karen Liu in The Movement Lab (TML) and Professor Jeannette Bohg in the Interactive Perception and Robot Learning (IPRL) Lab, and I am supported by an NSERC Postgraduate Scholarship (PGS-D).
I am broadly interested in building robots that can move and act in dynamic real-world environments in an elegant and efficient manner. My research focuses on developing the most effective ways to integrate learning-based perception and control, and studying the inductive biases we can exploit to improve the efficiency and reliability of these systems. I hope to uncover the underlying principles that enable intelligent agents to reason through uncertainty, continuously learn from their environments, and adapt to new challenges. My long-term goal is to create robots that have some form of common sense reasoning, which will make them reliable enough to create tremendous value for people in their everyday lives.
Before my PhD, I studied Engineering Physics at the University of British Columbia (UBC), where I graduated as a Wesbrook scholar - one of UBC's most prestigious designations given to the top 20 overall senior students. I was advised by Professor Michiel van de Panne studying reinforcement learning and motion planning for quadruped robots. I also worked with Professor Purang Abolmaesumi on deep learning for medical image analysis.
