Information for Students (and Prospective Students)

This page contains information for prospective students, tips and guidelines for (my) students, and listing of my current and past students. 

Prospective students:

• I now have open positions for grad students for Spring and Fall 2025. I recommend contacting me by email (with CV and any other supporting infromation, including a link to a webpage), before or in addition to applying to UConn. UConn students esp. encouraged, you can also use Nexus to setup time to meet and discuss. 

• I am selective in taking new grad students. Most of my students are UConn graduates, who did well in my courses here in UConn. I may also take students from other universities but only from well-known universities and with recommendations from well known researchers, and preferably with some publications. The reason for this policy is that I need to manage my time well - `testing' candidates is time consuming - and since I prefer to limit the number of students I take so that I will have sufficient time to work with each of my students. I'm quite stressed in time as is.  And I may simply not have funding for new students, especially with current cuts in NSF budgets. 

• I am not looking to hire a post-doc, sorry.

• I often hire research assistants - undergrads and sometimes grad students from UConn. This is mostly for help in programming of our applied projects. I may also hire non-students if they have necessary skills, typically, for full-time employment for half a year or more. This is based on needs, and after validating the relevant qualifications (e.g., relevant programming skills).  But, again, this may depend on available funding. 

• Candidates can  contact me by email. Please send CV, grades, recommendations, and explanation of your availability and preferred areas.  

Tips and guidelines:

• 1. Search and read - a lot... use keywords and previous articles to find new works. In particular use Google Scholar, Citeseerx, and ACM Portal. Use Citeseer and Scholar to find citations, crucial for searching an area. Once you identify important author, look for other publications in their homepage, in Scholar and in DBLP. Scholar is esp. useful for finding which papers cited a given paper (click on `cited by'); very useful to identify new papers relevant to your research. Many articles are available online. UConn provides online access to many sources.  When all fails, ask authors by email - most researchers will help you (also with questions). My students are welcome to consult with me on such emails. 

  2. Citations are important and could be surprisingly painful... but LaTeX makes life easier with BiBTeX citations, available from DBLP or ACM Portal. Scholar also provides BibTex citations, which have improved a lot so can often be good enough. The Collection of Computer Science Bibliographies used to be a great resource but seems broken :( ... but please first see if the citation is already in my collection, so we don't get multiple citations to the same paper.   

  3. Improve your (English) writing style. Two (of the many) great online sources: Lynch's guide to grammar and style, and the very concise Strunk's `The Elements of Style` (this online version is 1st edition; I use 3rd edition, by Strunk and White - you can loan it). 

  4. Use the right tools! Write using LaTeX, mainly via overleaf.com (I have a pro account with few benfits so ask me to open our projects). I use, and recommend, the Algorithmix package for writing algorithms and protocols, and TikZ package for figures and graphs. Three cute tools (sites): Detextify (find LaTeX macros for symbols by scribbling them), EqnEditor (online equation editor) and MathPix (similar to Detextify, but allowing `cut and paste'). At least Detextify and MathPix also have applet versions. Both Google's Gemini and ChatGPT can generate an initial draft of the Tikz code for diagrams. 

     1. One of LaTeX great features is the ability to define macros, bibliography (bib) files and support different packages. I share a special overleaf project called `common' which contains stuff we share between projects like bib, macros and packages. Please use it. In particular it contains bibtex entries for RFCs and for many papers in 'my' areas; add to it if needed but use it rather than keeping your own separate file, to ensure consistency and avoid double citations for same paper. 

     2. Use a dedicated macros files for concepts, notations, names and variables/parameters you define and use. This allows you to change them when necessary, which often happens during work, and ensure consistency along the paper(s). It's much easier to change notations when consistently using macros; yes, you can do a `global change' of a name, but we often use short variable names (e.g., i, t, ...) - changing these can be a pain, and changing over multiple files is a pain too. See more notations rules in the table below.  Notice also my Murphy Macros rules: if you leave some variable/notation without a macro, then you will change the notations again and again, until you define a macro!

  5. Networking is also important; in fact, one of the great benefit of both research in general and cybersecurity specifically are the great connections you will make. One small but useful tip: I recommend to use LinkedIn, it's a handy tool to maintain (also) your less-frequent connections, as well as to contact people when needed. I welcome connection requests from my students, of course. 

  6. Now to some rules, beginning with Murphy's research rules:

     1. The deadline rule: the probability of problems increases as the time till the deadline decreases.

     2. The backup rule: files get deleted if there is no backup. 

     3. The related works rule: related work tends to appear only in the reviews rejecting your paper (or, if lucky, when you thought work is ready for submission). Hunt it vehemently earlier!

  7. Tooth-brushing research rule. When working on research, there are often aspects which are murky, and there is a natural tendency to avoid them - kind of like a sensitive gum that one is tempted to skip over when brushing your teeth. But dentists tell me that it is especially important to brush these delicate areas. Similarly in research: pay attention to the murky, `painful' areas, for there lurk serious issues. 

     1. Igal's rule: claims marked as `trivial' are often actually hard to prove or incorrect. (Ask about professor story.)  

  8. Notations rules: (most important rule: don't take these, or anything, too seriously)