TA Spotlight: Vahe Hagopian
Vahe Hagopian is a TA for CS 7642: Reinforcement Learning. Keep reading to learn more about Vahe!
What is your academic and/or professional background? If you're currently working, what do you do?
I started off as an electrical engineer, working on the first generation of gigabit ethernet transceivers that now reside in most computer motherboards and routers. I then made an abrupt transition into poker, where I played professionally (mostly online) and first discovered the power of machine learning and its ability to solve formerly intractable problems. Since graduating from OMSCS, I've been doing research in reinforcement learning.
Why do you TA for OMSCS?
The best experiences I had during my OMSCS career involved the feeling of community that existed in classes where TAs took an active role. This is the closest thing we have to an on-campus feel, and I enjoy trying to recreate that atmosphere in the classes I TA.
What is your advice for current OMSCS students?
Many of my favorite courses in OMSCS had a gap between what was required and what was actually provided as material to learn from. My advice is to fully explore all that a course has to offer, because the gains per effort expended can be superlinear. As an example, in CS 6601: Artificial Intelligence, the optional extra credit portions attached to each assignment were actually the best parts of the assignments. You really miss out if you don't do them!
Why did you choose to pursue computer science?
I believe that humans will need the assistance of machine intelligence to solve important, and even existential, problems. Being a part of the process of developing that capability is exciting.
What is your favorite hobby?
About a year ago I started playing the piano again after having been away from it since childhood. Now it's a huge part of my life and I spend pretty much every day practicing and trying to improve. I also co-host a machine learning podcast with two other OMSCS alumni called "Argmax" where we dissect interesting machine learning research papers. You can find us at argmax.fm, and for our most recent episodes on YouTube at the channel @argmaxfm.