I am looking for someone with strong mathematical, statistical AND computational skills. Contact me via email for further details - please include a CV and a personal reference (advisor or employer) with your email.
My research centers on the development of new statistical methodology for network modeling, clustering and model selection, with applications to high-dimensional biological data. I am particularly interested in integrating techniques from information theory into new tools for statistical model selection and high-dimensional data exploration. Efforts in this area include Simultaneous model selection via rate-distortion which allows for the identification of genes and gene clusters associated with interpretable models derived from the experimental design. This work has been extended to data integration, involving mRNA expression, protein metabolic data and pathway information (work with former student Alexandra Jauhiainen ).
Data integration and joint modeling is a rich source for research problems. These types of problems are central components in several joint projects now underway in collaboration with Sven Nelander's group. Our group aims to formulate integrated models for mRNA, microRNA, DNA copy number, methylation and mutation in human cancer.
In addition to projects stemming from systems biology problem, my students and I are also investigating statistical clustering methodology, particularly how subsets of features play in role in the formation of clusters of observations. These projects constitute continuations of my research into mixture modeling, data depth and missing value imputation (see publication list).
My research is often motivated by my collaborative projects. The best thing about being a statistician is that you get the opportunity to work with people from other disciplines.
I work closely with the Nelander lab on problems pertaining to network modeling of cancer, data integration in cancer genomics, and the identification of therapeutic targets.
I work with Gunnar Steineck's group, investigating long-term effects of chemotherapy. I derive computational phenotypes from factor analysis of patient self-assessment data.
Together with scientists at the Center for Brain Repair and Rehabilitation (CBR), I explore the therapeutic impact of music.
The power of R is that current research is often available as packages almost immediately upon the publication of the methodology in a journal. This leads to a fluidity of ideas and implementations and, more importantly, is a key component of reproducible research.
Here is a short lecture about the integration of R and LaTeX (R-Sweave) for dynamic report writing. I am so sold on this idea that I am implementing most of my lecture notes in R-sweave. My students can thus reproduce lecture notes and coding demos at home.
I cycle teaching applied statistics couses (Linear models, Applied multivariate analysis) and method courses (Statistical inference principles and Survival analysis).
I also teach PhD courses, sometimes jointly with upper division masters programs (Sparse modeling, Empirical Bayes, Bootstrap methods). Here are some links to recent courses: