Friday, November 15, 2013

Prof. Yves Atchade (Michigan/Columbia): November 20th

Title: Bayesian inference for doubly intractable distributions.

Abstract: The talk will review some of the recent developments in computational statistics to deal with statistical models with intractable likelihoods (viz. intractable normalizing constants). I will describe in particular our recent methodology that exploits the exact-approximate MCMC framework combined with a Russian roulette trick.

Sunday, November 10, 2013

Prof. Dana Pe'er: November 13th

Title: Revealing tumor heterogeneity between and within tumors

Abstract: Systematic characterization of cancer genomes has revealed a staggering complexity and heterogeneity of aberrations among individuals.  More recently appreciated that intra-tumor heterogeneity is of critical importance, each tumor harboring sub-populations that vary in clinically important phenotypes such as drug sensitivity. A major challenge involves the development of analysis methods to integrate the flood of high-throughput data on tumors towards a past of personalized care. We will elaborate on two  computational approaches on this path:  (1) Integration of genetic and genomic data to identify genetic determinants of cancer.  (2) Single cell analysis of signaling based on mass cytometry, a novel technology that can accurately measure more than forty signaling molecules simultaneously single cells.

Sunday, November 3, 2013

Prof. Wei Ji Ma (NYU): November 6th

Abstract: My lab just arrived at NYU ( We do human psychophysics, behavioral modeling, and neural modeling. Today, I will be telling three short stories that are still in development:

1) Do humans aspire to optimality or favor simple heuristics in their decision-making? Both notions are prominent in different domains, but it is rare that they can be pitted directly against each other. We do so in a simple, new visual search task.

2) Confidence ratings are widely used in psychophysics, but rarely fitted. In a working memory task in which stimulus estimates and confidence ratings were collected, we tested different mappings from precision to confidence. It seems this mapping is logarithmic.

3) Using forward models of fMRI activity, we are trying to not just decode the stimulus but also uncertainty. A big problem is how to estimate the covariance matrix. I will discuss where we are currently stuck.