
Anand Bhaskar
S222 Clark Center
Stanford University
Email: firstname dot lastname at gmail

About
I am a CEHG postdoctoral research fellow at Stanford University, being mentored by
Jonathan Pritchard.
Before that, I was a
JSPS postdoctoral fellow graciously hosted by
Hideki Innan at the
Graduate University for Advanced Studies in Hayama, Japan.
Prior to that, I was a SimonsBerkeley research fellow in the semesterlong program on
Evolutionary Biology and the Theory of Computing at the Simons Institute for the Theory of Computing.
I received my PhD in Computer Science at the University of California, Berkeley where I worked with
Yun S. Song on theoretical population genetics.
Before that, I got my B.S. and M.Eng. degrees in Computer Science from Cornell University, and an M.A. in Statistics from Berkeley.
I also spent two wonderful years in
boarding school while studying in the 11
^{th} and 12
^{th} grades in
Raffles Junior College, Singapore.
Research Interests
Population genetics, computational and mathematical biology, machine learning
Publications

Efficient inference of population size histories and locusspecific mutation rates from largesample genomic variation data
Bhaskar A., Wang Y.X.R., Song Y.S.
Genome Research, in press
[bioRxiv]

Descartes' rule of signs and the identifiability of population demographic models from genomic variation data
Bhaskar A. and Song Y.S.
Annals of Statistics, 42(6):2469–2493, 2014
[pdf][arXiv]

A novel spectral method for inferring general selection from time series genetic data
Steinrücken M.*, Bhaskar A.*, Song Y.S.
Annals of Applied Statistics, 8(4):2203–2222, 2014
[arXiv]

Distortion of genealogical properties when the sample is very large
Bhaskar A., Clark A.G., Song Y.S.
PNAS, 111(6):2385–2390, 2014
[pdf][arXiv][software]

Approximate sampling formulas for general finitealleles models of mutation
Bhaskar A., Kamm J.A., Song Y.S.
Advances in Applied Probability, 44:408–428, 2012
[pdf][arXiv]

Closedform asymptotic sampling distributions under the coalescent with recombination for an arbitrary number of loci
Bhaskar A. and Song Y.S.
Advances in Applied Probability, 44:391–407, 2012
[pdf][arXiv]

A model and framework for reliable build systems
Coetzee D., Bhaskar A., Necula G.
EECS Department, University of California, Berkeley, Technical Report No. UCB/EECS201227, 2012
[pdf][arXiv]

Confidently estimating the number of DNA Replication Origins
Bhaskar A. and Keich U.
Statistical Applications in Genetics and Molecular Biology, Vol. 9: Iss. 1, Article 28, 2010
[pdf]

A comprehensive genomewide map of Autonomously Replicating Sequences in a naive genome
Liachko I., Bhaskar A., Li C., Chung S.C.C., Tye B.K., Keich U.
PLoS Genetics, 6(5):e1000946, 2010
[pdf]

Multilocus match probability in a finite population: A fundamental difference between the Moran and WrightFisher models
Bhaskar A. and Song Y.S.
Proceedings of ISMB 2009. Bioinformatics, 25(12):i187–i195, 2009
[pdf] [software]

Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast
Keich U., Gao H., Garretson JS., Bhaskar A., Liachko I., Donato J., Tye B.
BMC Bioinformatics, 9:372, 2008
[pdf]

Efficient keyword search over virtual XML views
Shao F., Guo L., Botev C., Bhaskar A., Chettiar M., Yang F., Shanmugasundaram J.
VLDB Conference, 2007
Also invited for publication in the VLDB Journal, 2009.
[journal version]

Quark: An efficient XQuery fulltext implementation
Bhaskar A., Botev C., Muthaia Chettiar M., Guo L., Shanmugasundaram J., Shao F.,Yang F.
ACM SIGMOD Conference, 2006
[pdf]
Theses

Statistical, algorithmic, and robustness aspects of population demographic inference from genomic variation data
Ph.D. dissertation. Computer Science, UC Berkeley, 2013
[pdf]

Approximate sampling formulas under the coalescent with finitealleles models of mutation
M.A. thesis. Statistics, UC Berkeley, 2012
[pdf]
Links
Github
Sourceforge
Quantum reading group
Fall '09,
Spring '10