

research scrapyard — hammers seeking nails

2012STORM imaging with ISTA report slidesmixture of Kalman Filters Demo: Mixture of Gaussians EM Demo: Mixture of Kalman Filters EM 2011regularization / filtering / clustering with pairwise fusion penalties, report slidesthoroughly scooped by T. Hocking, A. Joulin, F. Bach and J.P. Vert. Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties, ICML 2011. [pdf] 
I am a PhD student in Statistics at Columbia University, working with John Paisley. 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. Data of interest: spike trains, microscope images, networks, potentially anything 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 academic historyColumbia University 2010, PhD in StatisticsUniversity of British Columbia 20082010, MSc in Computer Science Carnegie Mellon University 20062008, programmer for HCII, researcher at Machine Learning Department Universiteit van Amsterdam 20032005, MSc in Logic at ILLC Bucknell University 19972001, B.S. in Mathematics and Computer Science why so many places, so many degrees? conferences and summer schoolsIPAMGSS 2007 ICML/UAI 2008 SFI Summer School 2009NIPS 2008, 2009 CogSci 2008, 2009. 
papers all publications Google ScholarIdentification 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 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. We prove theorems about identifiability, specifically about when a unique model can be identified. (draft) UpperBounding Proof Length with the Busy Beaver (2008) (PDF)  This note presents a Chaitinesque 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 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 
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 Programming tools: emacs, automatic memoization Writing: knitr, pandoc, LyX, ShareLatex Online notebook: MediaWiki Data collection: BeautifulSoup Reference management: Zotero misc toolsBeeminder, Boomerang, Dropbox, 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 