robot_control

Bumping thread. D-date near. Appreciate any comments and advice. Thanks!

Hi, I have been fortunate to get two wonderful offers: Berkeley and UW. I would appreciate any comments about them and advice on where to go. I visited both places, and liked both universities. Both cities seem to be huge tech hubs (Berkeley more so than Seattle, but the latter is great too), have good weather, and look gorgeous. I can see myself doing good work at either place and also living comfortably. For many people, Berkeley would almost look like an obvious choice, but surprisingly I find myself leaning towards UW. I have summarized my reasoning below. Am I shooting myself in the foot? Berkeley has many great researchers in my area, but they are not as accessible as I had imagined. Most top POIs are oversubscribed with 15+ grad students and postdocs. Hence, it would be hard to get face time with them, or I have to compromise and work with junior faculty. On the other hand, UW CSE is expanding at a good pace with great funding. Many top faculty were hired over the past 2-3 years and are in the process of expanding their labs. Sergey Levine joined recently, and the general consensus is that he was the brain behind most of the robotics work at Berkeley over the past 2-3 years. The situation boils down to sub-optimal POI support (either face time or researcher) at a more prestigious place vs ability to work freely with anyone I want at a (slightly?) less prestigious place. Can anyone offer advice on how to navigate this landscape? Is the difference between Berkeley and UW very significant, that too in ML/AI/robotics? I believe both are top 5 schools. Thanks!

Got into the EE program at UW today; still waiting to hear from CS. I'd much prefer the latter. @emmm What's your research area? Computational biology?

@ev a. Sounds very similar to my own story However, I knew from my sophomore year that I wanted to do robotics. Only later did I figure out that CS builds the brain for the robot whereas EE is more like the central nervous system. I associate mostly with the ICRA and IROS communities (and maybe CVPR) which do have decent representation from EE people. So I don't actively abhor EE, but given a choice will obviously pick CS. I however agree that much of EE undergrad curriculum is outdated and irrelevant for anyone who doesn't want to work in that particular space; while CS provides more *general* skills which are widely applicable. Best wishes for your apps

@ev a. If you don't mind, can you elaborate on why you want to sever ties with EE. Saying that CS is better suited is one thing, but saying you wish to sever ties with EE seems to suggest you had a bad experience. If you don't mind, can you share the reasons - I am curious. Also, are you by any chance a student at Caltech?

@ev a. You seem to have very peculiar reasons specific to you, not sure if anyone here can provide a good feedback. If you felt your calling for deep learning early enough, and lost interest in EE, you should have switched streams. At the very least, must have taken enough courses to cover for lack of exposure. If you feel anxious due to your lack of CS courses alone, it is understandable, but no point in fretting over it now. You'll get to know authoritatively in another 1-2 months. On the other hand, if you are anxious because ML/AI is very competitive at the moment - this is no secret. To be frank, I think you should have applied to another 3-4 programs. I regret not doing so myself. The deep learning professor who interviewed me said that applications to his lab nearly quadrupled from last year. Even if I do happen to get in, I may not get to work on the area of my choice due to fierce competition among admitted students. I guess everyone is anxious due to the situation being fuzzy and messy.

I assume you are interested in an MS and not PhD. If that is the case, why didn't you just apply for MS in EE? ML has very weak connection to CS compared to other areas. Much of ML is based on optimization and statistics which are integral to EE too. You could have just taken the appropriate ML electives and got away with an EE degree IMHO. This however changes if you want to do a PhD, in which case you should apply to the department of potential advisers.

Had an interaction/interview with PoI at UWashington yesterday (over phone). He said first batch of admits would be sent by this Friday (notified at the dept. level), and another batch would be done sometime around Feb 2nd week. Really nervous right now! He told me that he liked my application, and our interests match. But he didn't say outright "admit". Don't know how much to read into the conversation, but it is safe to assume that I will not get any sleep this week.

I've applied to their IDSS program, for transportation systems specification. I hope to work on either autonomous vehicles or intelligent transportation (smart cities project). I haven't heard back from them yet; have you?

@Yav Friendly Are you applying for PhD this year, or are you continuing at ETH? If you are applying, where to, if you don't mind me asking.

@Micecroscopy Thanks for the comments. I think it would be more appropriate to ask the inverse question - how relevant, conventional, or advantageous would a neuroscience based degree be for motor control, vision etc. in the context or robotics. How the CNS program is viewed in the engineering and CS communities. What drew me to the CNS program was that it isn't a conventional neuroscience program - majority of its faculty and affiliates are not biologists. I felt that it combined neural networks based methods for vision and control which is an exciting area. As you said, I am really hyped up for the interview, and would like to make it click. I'm still waiting for a few other programs though, which might be better fits - particularly ETH, CMU (RI), and UWashington. Best wishes for your applications!

Oh! This is funny. ETH is probably my top choice. I have applied to Prof. Raffaello D'Andrea and Prof. Andreas Krause there. Incidentally Prof. D'Andrea got his PhD from Caltech under Prof. Murray and Prof. Krause was a professor at Caltech till a few years back, before moving to ETH. I can't help but wish I was born 5 years sooner, that would have been a golden time!

@Yav Friendly Thanks for the info. Prof. Doyle is an absolute legend in control theory, and his robust control paper "State-space solutions to standard H 2 and H∞ control problems" (popularly known as DGKF paper) arguably marks the birth of modern control theory. But I don't think he is affiliated with the CNS program. Caltech also have another legend, Prof. Richard Murray who pioneered Networked Control Systems. However, both of them seem to work on systems and synthetic biology at the moment, as opposed to AI based robotic systems, which is a shame. For bio-enthused students, I think Caltech, UCSD, and ETH-Z are probably the top places. I will have an interview with Prof. Burdick who is their only robotics type control theorist (adviser of Jorge Cham, PhD comics!), and I plan on asking him about possible collaborations with other control theorists, machine learners, and vision experts. Keeping my fingers crossed Thanks again for your comments.

Haha, that seems decades away though. As an aside, I am absolutely convinced that control theory is one of the most awesome topics in the world. It has fertile connections to AI (reinforcement learning), synthetic biology (feedback circuits), and even economics! Systems and synthetic biology is certainly an exciting direction, with most control theorists (including my adviser) readying their guns to take aim. Though I am not directly interested in it (you can only choose one area, ), I am very excited for it, and hoping to keep myself up to date with the developments. In case it gets super hot, I'll probably jump ship Best wishes for your grad school applications!

Thank you @optogent and @Yav Friendly for your comments. I wouldn't say my field of interest is computational neuroscience, but it is certainly related and draws from it. I am more interested in the robotics end of things. How artificial neural networks can be used to make robots intelligent. For this, I am interested in mainly two parts: perception (mostly vision) and control. I think both have good connections with neuroscience. Till now, computer vision has not followed the path of biological vision, but my understanding is that the landscape is changing dramatically. Ideas from biological vision (particularly sampling) are taking over out in the wild type perception tasks useful for dynamic decision making. Control on the other hand is much more intimately tied to neuroscience for quite some time - particularly motor control and TD learning. However, I don't think I will be happy with studying "how does the brain perform a task". Rather, I would be happy to take inspiration from neuroscience, but I am more interested in "how to make a robot do a task", which may or may not imitate how the human brain works. I think I need to make it clear to the profesors. As you said, Caltech is a small place, and I can see myself working with only 2-3 professors. Though I am very happy with the interview invite, I am not 100% convinced if CNS is the ideal program for me. I really hope I click with the POIs though, since Caltech is Caltech!