Latest news from the BEEHIVE!
Dr. Li-Fang Cheng successfully defends her extraordinary thesis: "Large-scale multi-output Gaussian processes for clinical decision support." Exciting machine learning, brilliant data analysis, fast inference, and implementing for hospitals! Very impressive. Congratulations!!! pic.twitter.com/2IXKfO25fd
The Engelhardt Group develops statistical models and methods for high-dimensional genomic data. In particular, we study human genetic variation and its impact on genomic regulation, including gene expression and splicing, with the goal of identifying mechanisms of human disorders and diseases. There are a wide range of projects in these areas in the group (see Research for more information), including:
- Sparse factor analysis for estimating population structure, low-dimensional mapping of complex phenotypes, and gene co-expression analysis
- Statistical models for improving power to detect trans-expression quantitative trait loci and small genetic effects
- Alternative splicing and its role in complex phenotypes
- Global approaches to estimate differential networks
- Bayesian structured sparse regression for fine mapping of traits to genetic variants
- Genome-wide association studies for pharmacogenomic, metabolic, and cardiovascular phenotypes
Prof. Engelhardt is a PI in the Genotype-Tissue Expression (GTEx) consortium, within which our group is developing statistical models to analyze cis- and trans-eQTLs across cell types and the sexual dimorphism of transcription regulation.
Postdoc job opening: There is a postdoc opening in my group for a talented statistical geneticist, quantitative geneticist, or statistician who wants to study genomic phenomena by developing data-driven, statistical or machine learning approaches. Please contact me if you might be that postdoc (bee-at-princeton-dot-edu).
Prospective graduate students: If you are interested in working in our group at Princeton for your PhD, you must be admitted to a graduate program at Princeton University such as Computer Science or Quantitative and Computational Biology.