Prospective Students
If you are interested on doing research with me in Brazil, read the appropriate sections below, divided by undergraduate and graduate students.
If you are interested on doing research with me in Brazil, read the appropriate sections below, divided by undergraduate and graduate students.
A note to prospective students at any level: I do not respond to mass mailings (unless you have made an outstanding AI software that convinces me yours is not a mass email). If you will not take the time to read a couple of pages in my website and the abstract of a couple of my papers to discover if I am a good fit for your areas of interest in research, then I will not spend more than the 2 seconds it takes to delete your email (and mark you as a spammer). Note that this does not mean I will not advise work outside my specific areas of interest within AI---should you want me to advise you, explaining to me why you are excited about a different area is an excellent start. Finally, I do not take part-time PhD students. I will take part-time MSc students, but not PhDs because I reckon the commitment and amount of free time required to think of innovative research ideas is non-trivial.
I have curated all MSc and PhD manuscripts I advised in Github.
Before you start doing any kind of research, you need to be able to write about it in decent English. Understandably, for most of prospective students in Brazil, English is not the first language, so there are two obstacles to conveying research ideas: mastering the language, and writing about research in a style that is concise, direct, and unambiguous. For the first problem, there is not much I can do. However, for the second one, there is a slew of resources available on the web, which I have condensed in a site covering writing style.
Michael Luck's lecture on How to finish a graduate programme and have a successful defence.
Jason Eisner's advice on how to write a PhD thesis.
Brazilian impact rating for conferences and journals from PPGCC/PUCRS
Google Scholar impact rating for AI venues: Google Scholar
As the name implies, this checkpoint consists of a report that outlines what you will be doing in your second year of MSc. This deliverable is a small survey of what you have studied in your first year, followed by a project describing the activities and expected timeline of their conclusion in the subsequent year, key elements in this project are:
This looks like a regular paper, and most of its content should look like one. However, you should be careful about the actual objective of this check point, which is to assess how your MSc/PhD is going in relation to your Study and Research Project.
Key elements in this report include:
This is the primary deliverable of your MSc (and not a shiny implementation that only you understand). The dissertation is a self-contained contribution to science, you should include not only results of your implementation (if your work is on the practical side) and the proofs of your results (if your work on the theoretical side), but also a solid background on the theory behind your contribution, situating it within the specific area of your work. Thus, the dissertation should be a self-contained document that allows a specialist in your key subject area (e.g. if you are my student, this means a specialist in Artificial Intelligence) to read the document without necessarily consulting outside material. Key elements in the dissertation include:
If you are currently under my advice as a scientific apprentice scholar, here are the key events you need to mindful of (and aware of the deadlines):
For suggestions of potential final year projects, I have set up a small Github repository with ideas for final year projects.
These are the checkpoints for the students finishing their graduation at PUCRS. I have prepared checklists for each deliverable in the final year project courses.
This is basically a plan for what you aim to study in your final year project. What you need to have in this document:
The text you deliver at the end of TC1 is a much expanded version of your plan, but here, you must have a clearly defined problem you will solve, and concrete plans of how you will spend the following semester solving it.
The text you deliver at the end of the final project II must show that you are able to apply what you learned throughout your undergrad course to solve a concrete problem. You must convince the reader that you are capable of digging deeper into a CS subject and then use this to solve a problem.