I'm a first year PhD student in computer science at Princeton University working with Jonathan Pillow and Ryan Adams on problems in machine learning and computational neuroscience. I'm broadly interested in probabilistic modeling, scalable Bayesian inference, and time-series models.
I graduated with a BS in Computer Science from Johns Hopkins University, where I worked with Alexis Battle on understanding the impact of human genetic variation on complex traits using probabilistic modeling. In my earlier years as an undergrad, I worked on robotic grasping and prosthetics development at the Johns Hopkins Applied Physics Lab. My contributions were covered in the JHU Engineering Magazine and in a video with JHU Rising to the Challenge, which you can find here.
This past fall, I started my PhD in computer science at Princeton University. I'm currently working on developing probabilistic models of spike train data to help understand how neurons process sensory and visual information and on methods to scale variational inference to large-scale datasets.
A framework for predicting tissue-specific effects of rare genetic variants
Damani F., Kim Y., Li X., Tsang E., Davis J., Chiang C., Zappala Z., Strober B., Scott A., Hall I., GTEx Consortium, Montgomery S., Battle A.
In preparation.
[Manuscript][Code][Slides][Talk]
The impact of rare variation on gene expression across tissues
Li X., Kim Y., Tsang E., Davis J., Damani F., Chiang C., Zappala Z., Strober B., Scott A., Ganna A., Merker J., GTEx Consortium, Battle A., Montgomery S.
Nature 2017
[Paper][Code]
The impact of structural variation on gene expression
Chiang C., Scott A., Davis J., Tsang E., Li X., Kim Y., Damani F., Ganel L., GTEx Consortium, Montgomery S., Battle A., Conrad D., Hall I.
Nature Genetics 2017
[Paper]
