

Areas of interest: machine learning, graphical models, signal processing, information theory, compressed sensing, convex optimization; dynamical systems, functional data analysis / spatial statistics / shape analysis Models of interest: Independent Component/Subspace Analysis, Mixture of Kalman Filters; network models; topic models; differential equation models; NonParametric Bayes; structured data. Neat ideas: geometry of exponential families, nary relational learning, automatic optimization, manifold learning, information bottleneck method, lowrank matrix completion, unfolding flower models, probabilistic programming languages tutorials Introduction to Kolmogorov Complexity (with Liliana Salvador) (slides), 45 minutes. Introduction to Machine Learning and Bayesian inference (slides), 45 minutes. video demosslice samplinggeneralpurpose codeR: RhelpersJulia: BSplines 
papers all publications Google ScholarG. Lacerda  Identification of gene modules using a generative model for relational data  UBC Master's thesis (2010), supervised by Jennifer Bryan.G. Lacerda, P. Spirtes, J. Ramsey, P.O. Hoyer  Discovering Cyclic Causal Models by ICA (UAI2008), video lecture with slides) extends LiNGAM to discover cyclic models; The nonGaussian 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. P. O. Hoyer, A. Hyvärinen, R. Scheines, P. Spirtes, J. Ramsey, G. Lacerda, and S. Shimizu  Causal discovery of linear acyclic models with arbitrary distributions Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI2008) How to intelligently combine LiNGAM with methods based on conditional independence tests. This is useful when it may be the case that more than one, but not all error terms are Gaussian. G. Lacerda  UpperBounding Proof Length with the Busy Beaver (2008)  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 apparently original. N. Matsuda, W. Cohen, J. Sewall, G. Lacerda, and K. R. Koedinger (2008)  Why tutored problem solving may be better than example study: Theoretical implications from a simulatedstudent study. In Proceedings of the International Conference on Intelligent Tutoring Systems. N. Matsuda, W. Cohen, J. Sewall, G. Lacerda, and K. R. Koedinger (2007)  Predicting students performance with SimStudent that learns cognitive skills from observation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Proceedings of the international conference on Artificial Intelligence in Education (pp. 467476). Amsterdam, Netherlands: IOS Press. N. Matsuda, W. Cohen, J. Sewall, G. Lacerda, and K. R. Koedinger  Evaluating a Simulated Student using Real Students Data for Training and Testing, In C. Conati, K. McCoy & G. Paliouras (Eds.), Proceedings of the international conference on User Modeling (LNAI 4511) (pp. 107116). Berlin, Heidelberg: Springer. S. F. Adafre, W. R. van Hage, J. Kamps, G. Lacerda, and M. de Rijke  The University of Amsterdam at CLEF 2004, In: C. Peters and F. Borri, editors, Working Notes for the CLEF 2004 Workshop, pages 9198, 2004. 
getting helpQ&A sites for Math: mathoverflow, math.stackexchangeQ&A sites for Machine Learning / Stats: CrossValidated, MetaOptimize For R, visit the #R channel on FreeNode (IRC). Emacs users can use IRC by doing "Mx erc". If your problem is computationally intensive, consider learning distributed programming (GPU or cluster). work toolsErgonomics: standing desk, high chair, white boardsProgramming languages: Julia, R, Matlab, Python, bash Programming tools: emacs, automatic memoization Writing: knitr, pandoc, LyX, ShareLatex Online notebook: MediaWiki Data collection: BeautifulSoup, curl Reference management: Zotero misc toolsBeeminder, Boomerang, Dropbox, HelloSign, Google Calendar (social features, add events with one click), TotalFinder, SSHFSlanguage toolslinguee"a unique translation tool combining an editorial dictionary and a search engine" Google Translate tooltip Duolingo learn a new language / help translate documents; very clever crowdsourcing. some things I likeargument mapping, bikes, bluegrass, contact improvisation, DreamWidth, functional programming, GiveWell, infoviz, musical instruments, open data, Quantified Selffood for thought"You and Your Research", by Richard Hamming"Why People Are Irrational about Politics", by Michael Huemer "Why I defend scoundrels", by Yvain Paul Graham: "How to do Philosophy", "Why nerds are unpopular" LessWrong: Applause Lights "Illusion of Transparency: Why No One Understands You" Ribbonfarm: "A Big Little Idea Called Legibility" Ben Goldacre: "The Information Architecture of Medicine is Broken" blogsAndrew Gelman  Statistical Modeling, Causal Inference, and Social ScienceCosma Shalizi  ThreeToed Sloth Cathy O' Neil  mathbabe Peter Gray  Freedom to Learn neat toysAlgodoo: 2D physics engineBeepBox: chiptune editor 