Wesley is pursuing a master’s degree in Electrical Engineering and Computer Science at Berkeley. He originally entered Berkeley as an Industrial Engineering / Operations Research major, and quickly discovered and made the transition to EECS, from which he graduated in three years. Under Professor Ken Goldberg in the Automation Sciences Lab, he conducted research in applications of machine learning to robotics. In particular, his work focused on leveraging human intuition and demonstrations to improve robot learning. His work earned him coauthorship on ”A Gambler’s DAgger: Reducing Cost in Large Scale Online Learning From Demonstration Learning Through Hierarchical Supervisors” and “SHIV: Reducing Supervisor Burden using Support Vectors for Efficient Learning from Demonstrations in High Dimensional State Spaces”, respectively accepted by the IEEE CASE Automation Science Conference and IEEE ICRA Robotics Conference in 2016. Furthermore, he spent three months as a software engineering intern for the advertisement targeting team at Amazon, where he designed and implemented query reformulation methods for improving coverage and relevance of advertisements based on customer searches. Wesley is also greatly involved in the teaching community. He has served three semesters as a student instructor for the upper division algorithms and artificial intelligence classes. He contributed also as a tutoring committee member for Eta Kappa Nu, the EECS engineering society. Outside of academics, he is one of the founding members of the Mahjong club at Berkeley.