Gustavo Lacerda
CV publications blog twitter links
I left Google in 2019, and started consulting in the area of forecasting.

Areas of interest: machine learning, graphical models, signal processing, information theory, compressed sensing, convex optimization, interpretability; 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; Non-Parametric Bayes.

Neat ideas: geometry of exponential families, automatic optimization, manifold learning, information bottleneck method, probabilistic programming languages

"Follow Occam street, but do not stop!" -here


- Introduction to Kolmogorov Complexity (with Liliana Salvador) (slides), 45 minutes.

- Introduction to Machine Learning and Bayesian inference (slides), 45 minutes.

video demos

slice sampling

general-purpose code

R: R-helpers
Julia: B-Splines

papers Google Scholar

S. Carré, F. Gabriel, C. Hongler, G. Lacerda, G. Capano - Smart Proofs via Smart Contracts: Succinct and Informative Mathematical Derivations via Decentralized Markets

G. 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 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.

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 (UAI-2008)
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 - Upper-Bounding 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 simulated-student 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. 467-476). 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. 107-116). 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 91-98, 2004.

getting help

Q&A sites for Math: mathoverflow, math.stackexchange
Q&A sites for Machine Learning / Stats: CrossValidated, MetaOptimize
For R, visit the #R channel on FreeNode (IRC). Emacs users can use IRC by doing "M-x erc".

If your problem is computationally intensive, consider learning distributed programming (GPU or cluster).

work tools

Ergonomics: standing desk, high chair, white boards

Programming languages: Julia, R, Matlab, Python, bash

Programming tools: emacs, automatic memoization

Writing: pandoc, LyX, ShareLatex, LyX, Obsidian

Data collection: BeautifulSoup, curl

Reference management: Zotero

misc tools

Beeminder, Boomerang, Dropbox, HelloSign, Google Calendar (social features, add events with one click), TotalFinder, SSHFS

language tools

"a unique translation tool combining an editorial dictionary and a search engine"

Google Translate tooltip

learn a new language / help translate documents; very clever crowdsourcing.

some things I like

argument mapping, bikes, bluegrass, contact improvisation, DreamWidth, functional programming, GiveWell, infoviz, musical instruments, open data, Quantified Self

food 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"


Andrew Gelman - Statistical Modeling, Causal Inference, and Social Science

Cosma Shalizi - Three-Toed Sloth

Cathy O' Neil - mathbabe

Peter Gray - Freedom to Learn

neat toys

Algodoo: 2D physics engine

BeepBox: chiptune editor

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