I am second year Ph.D student in Computational Biology. My advisor is Seyoung Kim. I was an undergrad in Peking Unversity in Biology, Maths, French (2006 - 2010)
currently working on:
- Developing statistical machine learning methods to apply in genome-scale data to study disease-related biological processes, from one or more datasets of genome, transcriptome, phenome, time series transcriptome, etc . The goal of these methods is to answer questions such as:
What can we learn about gene expression networks with transcriptome data? How are gene expression networks controlled by genotype? In this way genome-wise assocation and gene expression network learning are combined.
Is expression network structure perturbed and re-wired by different genotypes? The patient-specific disease mechanism learning will provide a powerful tool for personal genomics.
- Sparse graph regression of high dimensional continuous input variables and high dimensional continuous output variables.
- Exploratory data mining in limit order book data from NYSE.
Projects in the Past
- Music melody prediction with the sequence memoizer, a nonparametric Bayesian language model based on hierarchical Pitman Yor processes.
- The impact of demographic factors on the flu attack rate in different U.S. states with agent-based simulation system, presented at Models for Infectious Disease Agent Study (MIDAS) conference, Boston 2012. Advised by Prof. Roni Rosenfeld.
- Statistical Machine Learning (10-702)
I worked for non-profit organization STeLA (Science and Technology Leadership Association) leading the branch in China in 2009-2011. STeLA organizes yearly conference in August to provide very interesting and intense leadership training for science and engineering majors (graduates and undergrads) from top universities in the world. The next forum happens in Netherlands in August 2013. Click here to apply (online application opens around April, operated separately in U.S., Japan, China and EU).