students Hal Daumé III
about me cv + bio publications research teaching students software photos calendar contact me links		If you are at any stage in the application process, please read the information on this page. If you want to get a sense of the kind of problems students who work with me work on, check out my list of current students. Alternatively, you can look for advice on applying to grad school, deciding on which grad school to attend, or what classes to take. If you are already a student here, you can also check out a list of Open Research Problems that you might find interesting to work on! (This page is only accessible from within the Utah domain. If you are a current student but cannot access it, please contact me.) Current Students I'm happy to be the current advisor for: Arvind Agarwal (PhD) Scott Alfeld (BS) Amit Goyal (PhD) Piyush Rai (PhD) I am additionally on the committees of: Venkat Anand (PhD, Computer Science; Advisor: Sneha Kasera) Josh Cates (PhD, Computer Science; Advisor: Ross Whitaker) Jeff Ferraro (PhD, Biomedical Informatics; Advisor: John Hurdle) Devyani Ghosh (MS, Computer Science; Advisor: John Carter) Avishek Saha (PhD, Computer Science; Advisor: Suresh Venkatasubramanian) Shuying Shen (PhD, Biomedical Informatics) Zhou Yang (MS, Information Technology; Advisor: Olivia Sheng) Applying For Admission I get lots of emails from potential students. I don't mind these emails at all, but just because I don't reply doesn't mean I haven't read it! However, since I'm routinely asked roughly the same questions, it is more convenient for me to answer them here. Q: Will you be accepting new students for the year 20XX? A: Almost certainly yes. Now that I have a handful of students, I want to maintain a steady state (perhaps expanding a bit). This means that I plan on taking one or two new students every year. (The precise number depends on the quality of the applicants as well as how much money I have!) Q: Is there anything special I should do when I apply? A: Yes! If you want to work with me, be sure you list me as the first or second choice professor to work with and list NLP/ML as your area of choice. If in doubt, email me: after you've completed your application, I can check to ensure you show up in the system. Q: How will I get paid? A: The current funding model works like this. Most incoming PhD students are funded by the department for the first year (typically under a TAship). After that, you'll need to find an advisor and get fuding through an RAship. If you're good, this has never been a problem. We're unlikely to admit you if we don't think that you'll be able to get an RAship after the first year. If you're really worried about this (which is sensible!) you can either email me (perhaps after getting admitted or talk to some current students). Q: What is your research group like? A: We don't really have formal "research groups" here. However, there are actually a lot of people here who work in neighboring areas to me. Ellen Riloff is a pure NLP person who mostly does information extraction and sentiment analysis; Suresh Venkatasubramanian is an algorithms/theory guy who does a lot in high dimensional geometry and data mining (very relevant to ML); Juliana Freire does web mining and databases; Tom Fletcher and Alun Thomas do computational statistics; Ross Whitaker does statistical image processing; Andrea Bild does biology and Lewis Frey does computational biology; and there aremore. In addition to the faculty, we each have a bunch of students who interact regularly through research seminars, reading groups, etc. I find it to be a fairly collaborative environment. Q: What sort of problems are you working on? A: My main interests are in statistical machine learning with applications to language and biology. If you fit in any of these categories, we're a decent match. If you fit in any two of these categories, we're a good match. I'm particularly interested in models that express prior (human) knowledge about a problem in a reasonable way, and models that work with structured data. My research page has more information (though it's a bit outdated); my publications page is more up to date but less coherent; and, of course, you can always look at what my students are working on, or what I'm blogging about. But I'm quite flexible and am interested in a wide variety of areas (even things like applying machine learning to less standard problems, like systems and graphics). Deciding Where To Go Coming soon... What Classes To Take Depending on (a) what your interests are and (b) what your background is, you'll have to tailor this list a bit. Note also that some of these classes are only offered once in a while. I expect that students working with me have command of the material covered in the following classes (note: you needn't actually take them; you'd just better know the material): CS 6350: Machine Learning CS 6150: Algorithms CS 7020: Research Proposals Additionally, if you are interested in language, I expect you to take Ellen's NLP class, my ANLP class and any course from the linguistics department that you wish. If you're more on the machine learning/statistics side, I expect you to take at least two classes from the math department or the statistics department. In addition to that list, you might find the following courses of interest: CS 6300: Artificial Intelligence CS 6320: Computer Vision CS 6530: Database Systems CS 6340: Natural Language Processing CS 6960: Nonparametric Methods CS 6964: Applications of NLP CS 7640: Image Processing BMI 6105: Statistics for Biomedical Informatics BMI 6950: Bioinformatics ECE 5510: Random Processes in Engineering ECE 6530: Digital Signal Processing ECE 6540: Estimation Theory ECE 7520: Information Theory MATH 5040: Stochastic Processes I MATH 5050: Stochastic Processes II MATH 5080: Statistical Inference I MATH 5090: Statistical Inference II MATH 6040: Probability MATH 6070: Mathematical Statistics MATH 6880: Optimization LING 6020: Introduction to Syntax LING 6030: Semantics LING 6060: Language and the Brain STAT 7593: Computational Statistics (not the CS course with the same name) WRTG 7060: Scientific Writing I would also strongly recommend taking the Topics in Machine Learning (CS 7941) and/or the Topics in Algorithms (CS 7936) seminars offered every semester. I would probably recommend taking 2-3 "real" classes per semester for your first year, plus 1-2 seminars. Most of the courses listed above require a fair amount of effort, so do not take 3 of them light heartedly. There's nothing wrong with putting one off for one or two semesters in favor of having time to work on research. If you're interested in statistics, see also the stats-related courses offered in various departments here.
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last updated on eight november, two thousand nine; contact