I recently started as an Assistant Professor in the Department of Computer Science at Rice University.
My research focus is in computational biology, where I develop machine learning and statistical methods to improve our understanding of the biological circuitry that underlies living organisms and how its dysregulation may lead to disease. More specifically, I have worked on modeling tissue and cell type specificity as well as disease progression, both by developing general methods (such as semi-supervised network integration) and in applying them to decipher the molecular underpinnings of diseases such as Alzheimer’s, Parkinson’s, and rheumatoid arthritis.
An important facet of my research is building intuitive, interactive systems as interfaces to the models and predictions that I develop, and I have built such systems whenever appropriate. For example, check out my cell-type-specific functional networks for human neurons relevant to Alzheimer’s, tissue-gene expression pattern predictions in the worm, or crowdsourcing game to annotate biological pathways.
I especially enjoy collaboration, working closely with experimental biologists and clinicians in many of my projects, including:
- Paul Greengard’s Lab (Rockefeller University):
- modeling selective neuronal vulnerability in Alzheimer’s
- predicting transcription factors to direct stem cell differentiation
- understanding therapeutic delay in antidepressants
- Coleen Murphy’s Lab (Princeton University)
- predicting tissue-specific gene expression in C. elegans
- prioritizing Parkinson’s disease gene candidates for experimental screening and targeted studies
- Bob Darnell’s Lab (Rockefeller University)
- modeling molecular level changes leading up to flare in rheumatoid arthritis patients
RNA Identification of PRIME Cells Predicting Rheumatoid Arthritis FlaresNew England Journal of Medicine. Jul 2020.
Selective Neuronal Vulnerability in Alzheimer’s Disease: A Network-Based AnalysisNeuron. Jun 2020.
Accurate genome-wide predictions of spatio-temporal gene expression during embryonic developmentPLoS Genetics. Sep 2019.
Mapping the physiological and molecular markers of stress and SSRI antidepressant treatment in S100a10 corticostriatal neuronsMolecular Psychiatry. Aug 2019.
An integrative tissue-network approach to identify and test human disease genesNature Biotechnology. Oct 2018.
Enabling Precision Medicine through Integrative Network ModelsJournal of Molecular Biology. Sep 2018.
Transcriptome analysis of adult Caenorhabditis elegans cells reveals tissue-specific gene and isoform expressionPLoS Genetics. Aug 2018.
A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study dataPLoS Computational Biology. May 2018.
Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorderNature Neuroscience. Nov 2016.
Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. eleganseLife. Jul 2016.
IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networksNucleic Acids Research. Jul 2015.
FNTM: a server for predicting functional networks of tissues in mouseNucleic Acids Research. Jul 2015.
A community computational challenge to predict the activity of pairs of compoundsNature Biotechnology. Dec 2014.
Nucleosome-coupled expression differences in closely-related speciesBMC Genomics. Sep 2011.
I have been the mentor and direct supervisor for several high school, undergraduate, and junior graduate students from diverse backgrounds, ranging from computer science majors to students with no programming experience at all. More specifically, my responsibilities have included initial project design based on the students’ backgrounds and interests, close working relationship throughout the project, as well as final presentation and paper writing guidance. Furthermore, several of these students have won awards for the work they completed with me.
I was a TA for COS 323: Computing for the Physical and Social Sciences both Fall of 2012 and 2013. This data science course had over 100 students and was focused on introducing principles of scientific computation (e.g., simulations, optimization algorithms) through real-world applications.
I also enjoy volunteering for outreach events. For example, I was one of the coaches for a Django Girls workshop at Princeton. This was a free programming workshop geared towards women with little or no technical background, introducing them to HTML, CSS, Python, and, of course, Django.
One of the side projects that I worked on as part of HackPrinceton was “What Would I Say?”—a fun Facebook app that takes a user’s statuses and generates new ones that sound roughly like them. It has had millions of users and been covered by several news outlets (including The New Yorker, PC Magazine, and ABC News).
I am a food aficionado and have probably eaten at ~83% of the restaurants in the Princeton area (working towards completion). Some of my favorite food items are the croissants at The Little Chef and ice cream at the bent spoon. Be sure to check them out if you’re ever in town!