Michael P. Verdicchio

Sometime during ROCKY ’08 I’ll be presenting the current status of the Causality work I’ve taken over from Xin Zhang.  It’s in Aspen, CO and all the details are on the Causal Networks page.

Information has been added to the pages for my three main research projects: Cellular Context Mining, Causality, and Aging.  Feel free to check them out and comment.

My group’s manuscript, “Context-Specific Gene Regulations in Cancer Gene Expression Data,” was accepted to the 2009 Pacific Symposium on Biocomputing (link).  I’ve posted the manuscript on the Research page.

I recently included a section on d-Separation in my most recent talk on causality, but I wanted to give it its own post. Before defining it formally, a brief history is given here from Richard Scheines’s page at CMU (http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html).

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This semester we’ve covered a number of topics in Sungwook Yoon’s Statistical Relational Learning reading group. My turn came a couple of weeks ago and I presented Bayesian Logic Programming. It is essentially a methodology which combines the structural conveniences of Bayesian networks and the theorem proving aspects of logic programming. The chapter in the Getoor/Taskar text was not especially liked in the group as it lacked, among a number of things, specific examples of its use and advantages; in other words, we didn’t know WHY we should use such a framework, only that it was an interesting hybrid of two seemingly separate methodologies. Nonetheless, my slides are below.

Slides (ppt)

In the last Computational Systems Biology lab group seminar I presented the topic of causality. It was essentially a survey of the first two chapters of Judea Pearl’s book, Causality. The slides for the talk can be found at the link below.

Causality Seminar Slides (ppt)

Here is the most recent talk in my Bayes nets study. With it I will have wrapped up what I want to cover from Heckerman’s tutorial.  It concerns causality and then a gentle introduction to dynamic Bayes nets.
(ppt)

In my Statistical Relational Learning reading group I have a few slides to chirp in with regarding joint probabilities in Bayesian Networks.  The first slide show has a brief synopsis of what a Bayes net is, and then has a simple probability factorization example.  The second set of slides just lists some different application areas where Bayes nets are in use.  It focuses on biologically related areas, and then summarizes one paper.  Another student is giving the bulk of the talk.

Bayesian Network Example (ppt)

Bayesian Networks in Use (ppt)