Biological Statistics and Computational Genomics

Be solid, be open-minded

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We have two main research directions:

1. Statistical theory and algorithm development. We are interested in developing new statistical theories and algorithms for the problems coming from biological research, medical research as well as researches in computer sciences. These may include (but not restricted to) statistical analysis of high-throughput sequencing data, parametric and nonparametric variable selection methods, Bayesian statistics, massive data analysis and MCMC theory and its applications. Our goal is to provide efficient and practically useful statistical models, algorithms and computer packages to help investigators from other fields to perform their research. At the same time, we also aim to provide solid statistical theories for these models and algorithms. 

2. Computational genomics and bioinformatics. Recent break-through in biological technologies has enabled researchers to generate massive amount genomic data. We will use existing statistical and computational methods developed by us as well as by the colleagues in the community to analyze these genomic data and gain biological insights from the analysis. In the mean while, we will also develop new computational methods (not only statistical methods) to address new problems we encounter. Currently, we mainly focus on cancer genomics and epigenetics.

How To Join Us

If you are interested in one of our research directions listed above and would like to do research with me, you are encouraged to send me an email with your CV attached. All levels of students (undergraduate, graduate and post-docs) are welcomed to join us. I will get back to you as soon as possible. If you do not receive a reply from me in a week, feel free to send me a reminding email.