compscian

@Rigenate Just curious, why not just change research groups? Are there really no one in your current school who does the type of work you like, or is there some other program in your current school which fits well with your interests? I know of many students who internally changed programs e.g. from Physics to EE, Math to CS, Math to Economics etc. In most of these cases, the research interests of student changed, they talked to an adviser who matches their interest (and about half the times, they will be outside their current department), and started working on a fresh project.

I am OK with you PMing me. For the more general questions, I can take them here so that others benefit too. For more specific or personal questions, we can try PM on gradcafe. Some statistics departments are joint with IEOR ones -- they are particularly good choices (e.g. Georgia Tech, Princeton etc). However, note that OR or data science programs will not help you towards the ultimate goal of PhD in NLP, for which you have to be in CS, EE (esp. for speech, sequence prediction, or dialogue systems), or Linguistics (semantics, language understanding). You'll run into the same problem of department mismatch after your MS, and your chances might be even lower than now (bar for PhD >> bar for MS). If you are particularly interested in NLP, and also want to develop a parallel skill set in NLP, I'd actually suggest a computational linguistics MS program. For example UWashington has an excellent program. Otherwise, try and apply for "data-science" programs. Operations Research won't look good on your resume since it's very far from NLP.

Hi @Tanul I'm an ME who transitioned into ML, and can probably share some insights. When you say ML program, which department are you talking about? If you want to join a CS/EE or Stats department, you will get degrees in that discipline. Hence they will naturally expect familiarity and pre-requisities (eg. if you join CS/EE/Stats you might have to take operating systems / communications / measure theory). Do you have a background in these areas; and are you interested in doing these courses for sake of degree? In absence of above, your best bets are "data-science" type programs at places like NYU. Alternatively, you can look into operations research or information management programs and take a good number of electives in Stats and CS. Online courses are not useful by themselves for a number of reasons -- see this post by a professor http://qr.ae/1JprKQ Kaggle competitions are useful on your resume if you actually accomplish something impressive. Participation means nothing since anyone can do that. To get good results in Kaggle, you need a good understanding of ML, and it's not easy. On the other hand, if you get impressive results, it will certainly add a lot of value, but it will be hard. My suggestions: Retake GRE, maintain a good online presence with your codes on github, apply to data science, business analytics, or info management type programs. Honestly, there isn't much you can do to improve your profile at the stage. One long shot is to join some professor at IIT or IISc as a research assistant and work for a year. However, convincing them to take you is not easy -- but strong interest in the form of MOOCs, Kaggle, and github repos might help. You can then use the professor's recommendation letter and apply for Fall 2017. It's a circuitous path, but is likely your best option if you want to get into CS or Statistics departments.

@marmle I don't know about all the univs, but I can comment about the CS/EE and Statistics departments of UW, Penn, Columbia, and Rice. At UW, nearly all the admitted students with the ML emphasis in CSE, EE, and Stats departments had at least one published paper or in the pipeline. Similar was the case with Columbia. Penn and Rice are also great places -- I didn't visit these two, but was admitted. I was told that admission was competitive here as well, and professors looked for one really distinguishing factor. My understanding is that grades don't really matter much (my undergrad was in MechE with Applied Math minor) -- there would be 100s of applicants with really high grades anyway. Publications and research experience count a lot more. ML and related areas are insanely competitive at the moment, so I would recommend you apply to a few extra backup places. PS: Operations Research in places like Princeton and GATech are also possibilities for ML/Stats related work, and are a little less competitive. Best wishes!

I'm more familiar with the EE (or EECS if I can say) side of things as opposed to pure CS areas. My reading is pretty similar to yours. In decreasing order of competition. Machine learning and related areas like computer vision, NLP, speech, and general data science are the most competitive. From my experience, these also provide the most lucrative post PhD outcomes -- especially in the industry. The signals & systems side of EE -- optimization, info theory, comms, control, DSP, compressed sensing. This attracts the mathematically minded folks who want to work on theoretical questions and stay in academia. Some of the more practical ones might jump ship to (1) above; and separation within these areas in (2) is wafer thin. Industry lags behind theory by at least a decade or two, and hence jobs meaningful of your research is hard. As you said, it's make it or break it -- professor or bust (or jump ship to data science band wagon). IoT, embedded systems -- possibly the next big thing. Computer Arch, High performance computing, HCI After this, I think (5), (6), (7), (8) in your list is quite accurate.

@TakeruK and @rising_star Thought I should update you. I talked to the professor, and he is supportive of me moving. However, the department would be unable to process my offer for this Fall due to administrative issues. After considering all possibilities, I am sticking with my original plan. I will be working with this professor remotely -- he has made the same arrangement for a few of his current students. I'll have a co-adviser in this university (there are 2 others who do related work). He has also agreed to the possibility that if I really want to work with him, and if the project in the first year works out well, I can transfer to the other program.

@TakeruK and @rising_star Thanks for your comments. I am planning to wait till we talk (Saturday) before contacting the university (on Monday). I hope it won't be too late. If this conference were not around, I am sure we would have talked sooner. What really bugs me is that this professor was very interested in recruiting me, and I'd attribute his presence as the factor that swayed my decision towards UW. I'm just praying he doesn't leave me stranded. We had a good rapport, and in fact even started laying down the foundations for my project through reading assignments and implementations of published results. But I can sympathize with his decision - he got his PhD from this univ, went out and made a name for himself, and is returning back to his alma mater. At times like this, you can't help but feel that world works in mysterious ways to screw you over backwards

@fuzzylogician @TakeruK Thank you so much for the response. The professor is currently attending a premiere conference and wants to talk this Saturday. I'll bring up these issues when we talk. There are a few issues I am very anxious about. Firstly, if I contact the new school, and even if the current one doesn't know about it, I will personally know that the new place is a better fit for me. This will definitely affect my performance if I am not allowed to accept again and have to go with the old school. The new school is actually more reputed overall (but same in my field). I went with the lower ranked school specifically for a better research fit. Now, this school isn't the best fit for me, and I have also let go of a stronger department (esp with the move of this prof). Also, I'm an international student, and have completed a good chunk of the visa process. If I switch places now, it might lead to a bureaucratic mess or very long processing time. The stress is going to kill me.. But this is less of an issue compared to the above. I feel devastated and hesitant to discuss this with anyone. My parents have a number of health issues already, and I'm really scared of what might happen to them if I break this suddenly. This just puts a lot more pressure on me to somehow make it work I understand that you can't make accurate predictions. But, have you seen situations like these before? Will universities make offers open again under circumstances like these? I'm really anxious and have already asked for a week of leave. This might just be the worst week of my life yet!

Guys, I am freaking out. I just heard from my potential adviser that he is moving to another university. The irony is that I was actually admitted to the university he is moving to, but I decided to accept UW's offer because I wanted to work with him. My understanding is that he got the offer and accepted it after April 15th. Can I write to the new university asking them if they can give me the offer again, and explain that I will accept and come because of my adviser moving there. Would they hold it against me for rejecting their offer earlier? Also, if I do this, and I don't get a positive response, I'll feel miserable going to the other university. They will obviously know that I tried to move, and am not very happy here. I am very interested in working with this professor - he was very enthusiastic while recruiting me. I have also been doing some work with him (reading assignments and some exercises) from May, and he has been giving me constant feedback every two weeks or so. I'm really torn as to how to proceed. As a city and department in general, I like both the places. The only differentiating factor was my adviser. I'll feel gutted if I don't get back the offer

I'll add a different perspective: it doesn't really matter what your MS is in. Any good PhD program worth their salt will value relevant research experience more than coursework. If your ultimate goal is a PhD, I'd suggest you pick a program that is very light on requirements and will give credits for research work, even if thesis option is unavailable. Personally, I did an undergrad in MechE/AMath and wanted to do a PhD in ML/data science. I had little coursework apart from the standard math sequence: CS (zero outside ML/AI); EE (only optimization and signal processing; zero in circuits); Statistics (no dept in my univ). I spent a year with an EECS professor doing research and published a paper, and didn't have trouble getting admitted to all three departments above. Coursework doesn't matter.

In that case, I think you have covered nearly all the departments/universities Also, MIT and Princeton don't have a "statistics" department yet, but they have an Operations Research program which is basically statistics with an increased emphasis on optimization. They might interest you too. Best wishes!

If you are interested in "machine learning", then you don't need to limit yourself to statistics or biostatistics departments. You can apply to CS or EE programs too. For example, UT-Austin (not on your list) has a strong machine learning program in both CS (applied flavor) and EE (theory flavor). Also, I would actually apply to TTIC instead of UChicago if you are more into ML as opposed to traditional statistical estimation. UMass also has a very strong ML program (stats and math in same dept + CS is strong).

From your profile, you will have better chances at statistics or computational linguistics programs as opposed to CS, which requires competent knowledge across theory, systems, and ML; and not just ML. You can of course sit in a stats program and have an adviser in CS (your best option IMO). For a good list of schools, I'll refer you to Jordan Boyd-Graber's pretty comprehensive list: http://qr.ae/8YoNwX My personal picks: UW, WUSTL, Stanford, Columbia, CMU are the strongest for ML/NLP overall. Regina Barzilay at MIT does some of the most interesting work. JHU, TTIC and UMass (Andrew McCallum) are easier to get into, but also have great ML/NLP research groups.

This may no longer be relevant for this year, but I'm going to post it anyway for future students. There is a fundamental difference between factor and criteria: Factor: a circumstance, fact, or influence that contributes to a result. (read: attribute) Criteria: a principle or standard by which something may be judged or decided. In other words, factors help you decide between options: to say one option is better than the other. Criteria is the evaluation metric itself. For example, if you want to drive from point A to point B, there are a number of factors that will influence your ride. They can include weather, traffic, condition of roads etc. Criteria is what you want to optimize: for some people it will be travel time, for others it can be smoothness of ride, fuel consumption etc. Once you fix the criteria, you can then use various factors to reason which option/route is better. Salary, employment opportunities, prestige of program etc are all factors. What you must optimize on are return of investment, and some measure of happiness for which fit with program and city is a good proxy.

I have been very consistent. The point of my first post was to enumerate various considerations and how to reason about them. If you notice, I never recommended one option over the other, but just gave a list of things to consider. Secondly, the norm being refereed to by us all here is essentially field-specific norms i.e. generalizations of various patterns that are specific to the field like salary, funding opportunities, employment goals, employment hubs (cities) etc. You can call this whatever you want, but I am essentially referring to this. Finally, criteria remain the same across all fields because goals for professional masters programs remain the same across all fields - to land the (dream) job of interest and ear a lot! Essentially, there are only two main criteria - return on investment and fit with the program/city. Both of them depend on all the above factors and more (eg climate). At the core, only two questions are important: (a) will you be happy at the program/city and can it help you land the job you desire; (b) is the cost of attending the program worth the benefits. Factors in the second para can help answer these questions, and the amount of importance given to different factors are subjective/personal.