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Gustavo Lacerda  +1 9I7 655 87O7 |
I am a PhD student in Statistics at Columbia University.
CV (as of 9 September 2009 or later) Projects
academic history
Columbia University 2010-201?, PhD in StatisticsUniversity of British Columbia 2008-2010, MSc in Computer Science
Carnegie Mellon University 2006-2008, programmer for HCII, researcher at Machine Learning Department
Universiteit van Amsterdam 2003-2005, MSc in Logic at ILLC
Bucknell University 1997-2001, B.S. in Mathematics and Computer Science
why so many places, so many degrees?
conferences and summer schools
IPAMGSS 2007 ICML/UAI 2008 SFI Summer School 2009NIPS 2008, 2009 CogSci 2008, 2009.
some things I like
argument mapping, bikes, bluegrass, Emacs, functional programming, infoviz, Kiva, musical instruments, open data, wikis.tips for Stats folks
For help with math, go to mathoverflow.Start using IRC. Visit the #R channel on FreeNode and get your R questions answered. Emacs users can use IRC by doing "M-x erc".
Use my R code snippets (coming soon)
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Underneath this page, this website is run on a wiki, so...
Note: I take full responsibility for the content on this page and any other pages that don't have an "edit" link, as they are only editable by me. -- Gustavo Lacerda
papers (see all publications)
Identification of gene modules using a generative model for relational data (PDF, slides) - UBC Master's thesis (2010), supervised by Jennifer Bryan.Discovering Cyclic Causal Models by ICA (UAI2008) (paper, video lecture with slides) extends LiNGAM to discover cyclic models; The non-Gaussian model leads to a finer level of identifiability than what can be achieved in the Gaussian case (e.g. by Richardson's CCD), and allows us to relax the faithfulness assumption. We prove theorems about identifiability, specifically about when a unique model can be identified.
(draft) Upper-Bounding Proof Length with the Busy Beaver (2008) (PDF) - This note presents a Chaitin-esque result. I derive an (uncomputable) upper bound on the length of the shortest proof of any given statement, as a function of the length of the statement; and briefly discuss implications. Mathematically trivial, but original (to the best of my knowledge). Could possibly be useful if we ever have good estimates of BB for n large enough to encode an interesting question.
see all papers
tutorials
- Independent Component Analysis (ICA) (slides) Introduces ICA, and tackles some very common misconceptions, 30 minutes.- Introduction to Kolmogorov Complexity (with Liliana Salvador) (slides), 45 minutes.
- Introduction to Machine Learning and Bayesian inference (slides), 45 minutes.
This website is permanently under
construction. You may notice that behind this frontpage is a MediaWiki site. Someday I'd like to have indexing.
For now, keyword searches will have to do. RIP Xanadu
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