Dawnbreaker

A couple years back, I ended up visiting both programs. I was particularly unhappy with Berkeley IEOR, for the following reasons: 1. Berkeley is vert strong in Stats, EECS, and Math. All the star faculty are in those departments and not IEOR. If you want to do theory, you'll be competing with students in CS theory, which will be a very hard fight. Similarly Statistics and Math departments have great students doing optimization and probability theory. Thus, there is absolutely no reason for someone to choose IEOR over Stats, CS, or Math; and thus all students there seemed to have an inferiority complex. 2. As for the applications, again IEOR doesn't seem to be doing anything very impressive. The west coast scene is mostly driven by the software industry, and focus primarily on CS and Statistics. Bottom line, the IEOR dept of Berkeley is heavily overshadowed by the other departments there. I am not aware of any students in IEOR who work with outside faculty, and if they do, chances are that they will switch programs. Given these factors, I would advise against Berkeley IEOR, especially given that you have an offer from Columbia which arguably has a stronger IEOR department, and would likely provide better moral support.

Range of opportunities with a PhD is much much more than range of opportunities with an MS. If you really want to do the exciting work that is hyped up in big data, you need to be the head of a team with 3-4 people working under you (the MS in DS type people). For that you need a PhD. I perceive MS in data science as too short sighted. An MS in data science over specializes on the type of skills that are currently in demand in industry. If the demands or methods change, much of the DS curriculum is obsolete. An MS in CS or Statistics on the other hand provides the necessary foundations, in addition to skills of value in industry. When applying for a PhD program, I am pretty certain that strong departments would prefer an MS in CS or MS in Statistics. If you go the MS in DS route, you might be forced to do a PhD in data science with some umbrella program as your home unit (similar to Stanford where ICME is not a department). This is far from ideal if you decide to go the academia route or even top industrial labs. I perceive interdisciplinary and data science programs as weaker compared to an established department. Given a choice between MS in CS, MS in Statistics, and MS in DS: nearly everyone will choose the former two. Hence, your cohort is going to be weaker. I'll pick UCLA for all the above reasons. In addition, I'd take at least 3-4 CS courses (data structures & algorithms, software development, graph theory, and an applied ML course like vision or speech processing). It'd be ideal if you can get an MS in CS along the way too!

@myh Hi.. among these 4 (and in general too), Berkeley and Stanford have the strongest programs. Based on my interaction with students from both programs, I have observed that Stanford's program tends to be more slanted towards CS. If you observe, quite a few Stanford MS&E professors have PhD in CS. I felt that many students at Stanford were unhappy because they perceive themselves as being second fiddle to CS - they try to do work that appeals to a CS audience, but obviously they cannot be as good as a CS-proper program. Berkeley on the other hand is a bit more theoretically inclined. They primarily stick to optimization, but also foray into "newer" and applied areas like data mining, network flows, power grids, robotics etc. I feel that these projects are oriented towards both general OR/CS as well as domain specific audience. I think they try to see how an OR perspective adds to a problem in data mining or robotics, as opposed to doing low quality work in data mining or robotics. So you'll see a lot more collaboration, and multiple PI names on the paper. I think this is a good middle ground to retain the theoretical rigor of OR, but at the same time get exposure to newer and "hotter" research areas. It is true that Berkeley on the whole, and IEOR in particular had a funding crunch some years (3-4) back. However, they have recovered gracefully. I believe they have given fellowship offers to all students this year (a college told me), and 100% of students have got full funding for the past 3 years or so. They even got a part of new building (CITRIS) and renovated their main building. You shouldn't be worried about funding, but do make sure with the program manager. I am only familiar with Peter Frazier at Cornell who does interesting work. However, both Berkeley and Stanford can offer more in nearly every metric than what Cornell can offer you. I'd recommend that you choose one of these two. If you want to do more applied work at interface of CS, Stanford is likely a better option. If you want to do more mathematically grounded work, have a shot at faculty positions, but also do some applied projects from time to time, Berkeley is the best option.

I don't think this is accurate, and it doesn't even agree with your last line about Aaron Clauset's findings! Let me clarify, I don't believe in rankings as a strict indicator (rank 6 not necessarily better than rank 7). However, there are definite tiers. Implicitly, you are creating a tier yourself with something like "top 10". Why did you come up with this magical number 10, and not something else like 15 or 20? IMHO, it's pretty clear that top 10 is not homogeneous by any means. For example, in EE; MIT, Stan, UCB, Caltech, and (possibly) UIUC clearly break from the rest. Head to head, MIT will definitely win much more students than Michigan for example. The causation is not high rankings, but rather the understanding that MIT has a higher standard of research, which in turn reinforce the rankings. To say that difference in rankings is negligible is too much of a stretch. In CS, the difference between the top 4 or 5 (MIT, Stan, UCB, CMU, UW) and the rest of top 10 is even more stark. No one with an offer from one of these programs is going to choose something else, unless there are other factors at play like two-body. They also produce much more faculty. I however agree with you on funding. I wouldn't do a PhD without guaranteed funding. But, I am pretty sure a top school like Berkeley wouldn't admit anyone without funding. I think the OP is just waiting for confirmation on fellowship, which if he doesn't get, should get a TA/RA offer (the same happened with me). With regards to NRC rankings, they are outdated.

@bandinterwebs Also look at people from Haas, some people there also seem to do relevant work.. Anyway, to answer the opening question, I personally think the ranking difference in this case is quite significant. I'd highly recommend Berkeley. Best wishes for your decisions and grad school experience

Hi @bandinterwebs.. As my signature suggests, I'll be joining MechE. However, my research interests are much more on the optimization, control, and statistics end of things. I'm actually trying to see if I can get one of Profs. Goldberg or Lavaei as my adviser or co-adviser. I got admitted for Fall 2015, and deferred admission. So had a ton of time to do groundwork on various programs. I am not 100% sure about production and logistics. How fixed are your interests? I mean, Berkeley did admit you, so there must be some research interest overlap! Check out Rhonda Righter and Lee Flemming. They seem to be doing related work. Btw, have they already approved your fellowship, or are you on some sort of wait-list? I assume you'll get TA/RA if you don't get the fellowship. Cost of living is certainly a concern, but definitely manageable with a little compromise and wise spending.

@bandinterwebs To be honest, Berkeley and Virginia Tech IMHO is a no contest. Berkeley is far superior in terms of reputation - both in academia and industry. It's also a great time for optimization and data science in general, and specifically in the Bay Area. The only reason to not choose Berkeley is if your research is very focused on particular areas like Supply Chains or Production/Manufacturing. However, as said before analytics, data science, and related problems are all the rage these days. I'm personally very familiar with the work of many OR faculty at Berkeley. Specifically, Javad Lavaei is a superstar in applying convex relaxation methods for distributed problems. Atamturk, Hochbaum, and El Ghaoui are also world leaders in various aspects of optimization and statistical decision theory. Ken Goldberg is of course a very famous Roboticist - with very close ties (multiple PhD students) to EECS and MechE programs. He uses learning and optimization for motion planning, robotic surgery etc - very interesting work. It's also worth noting that OR is very close to both the Statistics and EECS departments. Most end up getting an additional MS (with thesis) in either one of them depending on their inclinations. (OR+Statistics) and (OR+CS) are mouthwatering combinations for many job searches. So if you want to go the Industry route, Berkeley will definitely offer more - due to both program structure and location. I think summer internships are encouraged, and most do at least 2-3 summers over their 4-5 year stay in the program. It's also really strong for academia - they placed a recent grad at MIT. However, the only concern might be the high cost of living and financial situation of IEOR dept and UC Berkeley in general. I believe some 3-4 years ago, IEOR had a funding crunch. I think they have recovered well, but you should check with students and professors. How much are they paying you, and have they guaranteed full funding? PS: I realize that this sounds like a sales pitch, and honestly Berkeley doesn't need one. However, I am joining there and plan to work closely with many OR professors. Most of the above info are based on my own research and interaction with people there. As I said before, Berkeley and VTech is a no-contest. Berkeley and Stanford are the best places for new-age OR (intersection of optimization, statistics, and computing).

Hi @bandinterwebs.. In these cases, I think it would help a lot if you can identify the universities by name, instead of hiding them. That way, you'll get much better and directed responses. For example, my suggestions will be very different depending on what A is. If A is one of Berkeley or Stanford, I'd highly recommend that you go there. Though the programs are certainly theoretical in nature, the location, possibilities of summer internships, and overall culture will provide the necessary applied "flavor" you might need. On the other hand, I personally think that GATech and Northwestern are not as good as rankings suggest - they end up doing very theoretical work based on last century knowledge. I'd definitely pick a lower ranked program like UIUC or Cornell over them. PS: Note that I have a bias in my research interests towards statistical decision theory, stochastic control, and convex optimization. All of these are at the interface of IEOR, EECS, and Statistics.

It might be helpful to mention your background, and what you already know. Much of OR is centered around the following, so it might help to brush up on these topics. More directed feedback is possible if you provide further info. Optimization - start with LP, duality etc. Also study convex optimization, numerical optimization (BFGS, conjugate gradient etc.). If time permits, get some exposure to integer programming using gomory cuts and other methods. Probability theory - basics of probability and statistics starting from common distributions and moment calculations. Bayes rule and its applications are also important. You should also study stochastic processess like markov process, martingales, and Gaussian process models (Bayesian framework) if time permits. Data structures and algorithms - do not under estimate the utility of algorithm design, complexity analysis, and programming in general. Some advanced topics like NP-completeness, approximation, and randomized algorithms are helpful too.

@deborah_caf Congrats! I'd say getting an admission in Dec for international students is indeed surprising and certainty early. Last year, my earliest admit was NU which was around Jan 10th. Wish you good luck for the other applications too (Where else have you apped? and which IIT are you from?)

This is an extremely open ended question, and honestly I am not sure what to write. At the outset, I have a BS in systems and chemical engineering, and focused on optimization, data mining, and control (some process control, some networked control for smart cities). I think it is definitely possible to switch to a "more AI' field. (AI is a spectrum and not a single field) What I have observed is that AI is extremely broad. You can almost attach an "intelligent" prefix to anything which improves previous capabilities/results since no one really knows what intelligence is. That being said, one way to narrow down on interest is to look at AI and AI-related journals/conferences and identify some 2-3 venues which have publish most of the type of work you want to do. For me, the choices were ICRA/IROS, AAMAS, and CDC. For someone interested in vision, it could be CVPR, ECCV, NIPS etc. You can pick research communities like this, see which type of people publish regularly in such venues and apply to them. For example, AAMAS is mostly CS, ICRA/IROS is both CS and EE, CDC is mostly EE. So if these conferences interest you, a natural choice will be either CS or EE. Also, I think it might be a tad easier to get into EE programs than CS due to your background. If you are interested in hardcore CS areas of robotics/AI like vision, knowledge representation etc. the path is likely to be more difficult. On the other hand, if control, filtering, SLAM type of topics in robotics are your pick, you can get away with EE for which you stand a better chance.

I accepted the offer from Caltech. However, due to some personal commitments that required me to stay in my home country (International student), I had to defer my offer to Fall 2016. I have been working as a research assistant in my undergrad university though, and also doing courses in consultation with Caltech professors. I think it has worked out great though - I can take courses and learn the material without any exam/grade pressure and do the research I like, with sufficient amount of free time. My research interest is at the interface of optimal control, machine learning, and economics. Applications are in energy markets, online auctions, clinical trials etc.

@ArgonI understand. Hope it works out to your satisfaction this year. Incidentally, what you said mirrors my impression from last year. My interests too didn't gel well with MIT and hence I applied to their masters program as a safety option. Funny how it turned out. Which type of computational work are you interested in - algorithm development for optimization, inference, control etc; or computation geared towards numerical simulation of ODE/PDE/stochastics? For the former, Wisconsin is clearly the winner in your list. For the latter, it's a good toss up between Minnesota, Berkeley, and maybe UIUC I guess. Congrats and best wishes

Hi. If anyone has questions on Systems Engineering, in ChemE, EE, or ME, I can help with my 2 cents. Best wishes to all applicants. @Argon If you are comfortable with discussing, what happened at MIT? Faculty left, interested group is full, funding issues? Any idea where you are headed (UCB or Madison is a good option I'd guess).

- Assume no research This is a big negative. Top 5 PhD program across all fields expect good research if not publications. Also, good recommendation letters without doing research with them almost never happens. So heads up for that. -Strong interest in "mathy" fields within EE and CS, such as information theory, machine learning, data science, control systems. For these interests, I would rather apply to EE programs, or some sort of interdisciplinary EE and CS programs (like applied math). Info theory and control are nonexistent in CS. You can still do ML sitting in EE departments (in most cases directly, in a few cases indirectly through signal processing). Also from admissions point of view, EE is slightly easier than CS (statistically, there seem to be too many CS students for too few positions). So why not just do it in EE? You also have option of other "mathy" fields like communications, compressed sensing etc.

Which university in the US are we talking here? Since you have multiple papers during your stay there, it's almost certain that you will get an admission offer from that university, and comparable ones. Apply here and make this your safety admit. I am not an expert in electrochemistry, so I am unable to judge the relative quality of your research work. However, superficially, I'll say you have a very good shot at most universities you mentioned. It's kind of like a twilight zone where your application might warrant a serious consideration at most if not all universities (including the top 3-4), however it's not strong enough to automatically guarantee you an admit. Which IIT are you from? Is it one of the top 5? Who are your recommenders, and what is your relationship with them? Also, just a thought. If you still have the option of switching to dual degree, I would encourage you to go that route. If you stay for an additional year and do well in projects and courses, it's quite possible to boost up the CGPA to 9.1 or 9.2. With that CGPA and your publications (which at least superficially, seem very impressive) you have a very good shot at getting into the top programs like MIT, Caltech, Stanford, UCB, Minnesota etc. In my opinion one year of additional preparation is worth it if you are getting into one of those universities. Only you can answer this question, but would you be happier joining a place like Michigan or Austin this year, or would you be happier to spend an additional year at IIT and end up at Caltech or Stanford? Think about this question and make an appropriate choice. Best wishes

What is your area of interest? Chemical Engineering is too broad. Some of the universities you have listed are easy to get into for some areas of ChemE, while very difficult for other areas. The first author journal paper will help a lot, not so sure about the second author one. How many authors were there in each paper, and where were the papers written (IIT, IISc, or some university in USA)? It would be beneficial if you can list the journal names. GPA is always measured relative to the university at hand. For example an 8.7 GPA from an IIT is not a big red flag (I had a similar GPA, albeit closer to 9, but still managed to get into all the top places). On the other hand, an 8.7 from an NIT or lower is kind of a negative point. Instead of "a top university in India", it might be better to explicitly mention the university name. "Top" means different things for different people, for example some professors I spoke to in the US seem to have the view that only IITs, IISc, and ICT do anything worthwhile as far as ChemE is concerned. Not having an MS is a total non issue. More than half the PhDs in US come directly out of B.Tech. Also, a word of caution. There is nothing like an "integrated" MS/PhD program in the US. It is simply a PhD program, and you pick up an MS "on the way". Given the lack of info, I cannot really assess chances of admission. However, superficially I'd say everything except Yale and Purdue are ambitious, while these two are possible. With more information, this could of course change for the better

I am doing my BS in Chemical Engineering and switched over to control systems. I understand your predicament - I had to justify to just about everyone I spoke as to why I am switching over. I did a minor in Systems Engineering which certainly helped I guess. I would suggest one course of action, which I had as a backup plan: work with an electrical engineering department for 1 year as a project assistant, get a conference paper or two, and then apply to either ME or EE programs (from my understanding, the nature of control systems work in the two are somewhat different but distinctions are greatly blurred).

Hello guys, It turns out that I have got into all my dream programs except MIT (their loss!). This is absolutely wonderful, since I never expected to have this scenario when I filled out the apps (I would have happily taken any one of these offers). I have narrowed down my choices to the following: Stanford, Caltech, UCB. My situation is a bit peculiar, so I'll try to put down relevant points here. I am a very non-traditional applicant (Bachelors in Chemical Engg, but focus on advanced process control and signal processing) who wanted to branch out into cyber-physical systems and the theoretical side of statistical signal processing (as opposed to applications). I want to work in academia after PhD. My thoughts on the program (in the order in which I got the acceptances): Stanford --------------------- Gave me an offer comparatively early. I applied for MS (huge mistake!) but was able to find a professor kind enough to take me as an RA. The problem is the MS is non-thesis, and I have to pass an insanely difficult quals and take an insane number of courses (course requirements last well into the 3rd year for the ICME PhD program) in addition to doing good research work (I can't let this kind prof down). Pros: excellent campus, brand value, and quality of life. Cons: MS offer, no "guaranteed funding" though I am almost certain of being funded. I really hit it off with a prof at Stanford, so will be very hard to let him know won't be coming Caltech --------------------- Was initially put on the wait-list and was accepted off the waitlist. This program is insanely difficult to get into (class size is 4, # applicants is 400+), so I wouldn't count it against them for waitlisting a non-traditional applicant. Pros: Very diverse group - Control, CS, Math are in one department. So I can pretty much work and take courses on whatever I want. Got Fellowship, and I have heard Caltech is pretty liberal with funding. Great track record for academic placements (which is what I want to do). Spoke to two professors, both of whom seemed cool to work with. Another thing I have in mind, but not sure if it is possible, is to also get involved with the EE and applied math department in UCLA. They seem to have some giants working in signal processing and allied areas like compressed sensing. Would be nice if I can spend a couple of quarters in their lab. Cons: Same as the pros, extremely small program. Might be hard to find good friends. Brand value may be slightly lesser than Stanford or UCB (is this true?). Some guys have told me that Caltech is in general a very depressing place and has poor social life. But oddly enough, it comes from students who either didn't get into, or didn't apply to Caltech. UC Berkeley ---------------------- Again was placed in the waitlist, and got off it in the last moment. Pros: Brand value, have lots of friends who are going there (but in EECS as opposed to ME). Cons: I applied to the ME department, which in hindsight might not have been the best option. My interests are not directly the focus of the program, but I can get to work with some profs who have joint appointment with EECS. Have to do other ME courses about which I have absolutely no clue, so passing quals will be very challenging! Currently on fellowship, but finding an appropriate PhD adviser acceptable to ME department might be difficult. I am almost set on Caltech, but would be great if someone else can give me a perspective too. I have not visited either of the schools since I got the offers pretty late, and its too late now anyways! The only negative I find with Caltech is some people describe it as a depressing and boring place - if this were not true, I will have absolutely no regrets about choosing Caltech. Otherwise, I may have to rethink. Thanks in advance.

I don't know much about reaction engineering, but I think CMU is quite strong in catalysis. You might want to consider that option too. Among the three, I would say CMU is the best option - both in terms of overall reputation, and the place (which is also very important IMO, Pittsburgh is a great city). Though I don't have any concrete record of this, CMU's placement record is next to none. If you are interested in industrial job, I don't think there are very many programs better than CMU.

What is your area of interest/preference? As doomination alluded, TAMU picked up not just one, but 3 GIANTS in the field of Process Systems Engineering. (Floudas, Pistikoupolos, Kravaris). As such, I would say TAMU is the 3rd best Systems Engineering program in the world, only next to CMU and Wisconsin-Madison. If you are interested in Systems, then TAMU is the easy choice, and its miles ahead. Also, Systems directly translates into industry appointments (you are the guy who'll be making corporations tons of money) mostly in areas like process optimization, scheduling, advanced process control, fault detection and isolation, control loop performance monitoring etc. I am not aware of other areas in ChemE, but as far as PSE is concerned the choice is obvious (TAMU). I would say related areas will also feed off the strength of PSE (energy technology, catalysis etc.).

For process systems engineering, TAMU wins hands down. The only better places for PSE are CMU and UW-Madison. TAMU will make a comfortable 3rd, especially with some real giants joining this year, who were formerly full professors at Princeton and Imperial College London. I am not a fan of bio, so I can't comment on that, but comparing just PSE, TAMU wins hands down and I am pretty sure you can get the best of jobs in energy sector. As you said, PSE is more translatable to other fields as well. I know PSE people who went into supply chain management, transportation system management (including aviation industry - due to knowledge in planning and scheduling), data scientists - including places like Facebook, Amazon etc. I am obviously biased here because I am not a fan of bio at all, and IMO bio and PSE don't really go well together (systems biology is the only common point, but its more systems than biology so there is that). The fields you are interested in are pretty much orthogonal, so make an informed choice by deciding which area you like the most. Once that is figured out, the university choice becomes obvious.

I think Columbia has a very good program, but Northwestern might have the best overall living experience (from my limited experience, I like the Chicago area a lot). I am also trying to choose between Columbia, Northwestern, and UIUC, so do share your thoughts. At the moment, I am leaning towards UIUC, but that is more a result of my research interests (optimal control, game theory) and their strong ECE program. IMHO, Columbia and Northwestern are equally good programs. GATech might be even better depending on research interests.

Yes, I believe what you are saying is true. I was informally told that they expect a class size of 4-5 students for CDS and 9-10 students for CMS, but its fairly easy to switch between the two. I would expect that they give out around 1.5 times these number of offers in many windows, and are likely to interview may be twice these number of people. I only hope those who are not taking Caltech's offer tell them soon, and with a bit of luck, I guess the chance of being accepted after interview is close to 75%. Any idea how many turned up for the in-person interview?

Any updates? I had a short interview a couple days back, and I am absolutely clueless about how it went. Also saw a CDS admit post on the board, so I guess they have made their decisions