I am currently a Visiting Assistant Professor in the
Computer Science Department
at Swarthmore College
received my PhD in Computer Science in 2015 from
where I was advised by Yun S. Song
. My research areas include computational and population genetics, with a focus on demographic inference. I am generally interested in developing statistical and machine learning methods for problems in biology.
Spring 2019 Schedule
| CS 66 Machine Learning
|| MWF 10:30-11:20am
|| 181 Science Center
|| Wednesday 1:15-2:45pm (A), 3:00-4:30pm (B)
|| 016 Clothier
| Office Hours
|| Monday 12:30-2pm, Friday 1-3pm, and by appointment
UC Berkeley (Teaching Assistant)
| Discrete Mathematics and Probability Theory (CS 70)
|| Spring 2014
| Algorithms for Computational Biology (CS 176)
|| Fall 2013
More information on my Google Scholar page
- A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
NeurIPS, December 2018
- FADS1 and the timing of human adaptation to agriculture
Sara Mathieson and Iain Mathieson
MBE, October 2018
- Deep learning for population genetic inference
Sara Sheehan and Yun S. Song
PLoS Computational Biology, March 2016
- Decoding coalescent hidden Markov models in linear time
Kelley Harris, Sara Sheehan, John A. Kamm, and Yun S. Song
RECOMB, April 2014
- Estimating variable effective population sizes from multiple genomes: A sequentially Markov conditional sampling distribution approach
Sara Sheehan*, Kelley Harris*, and Yun S. Song
Genetics, July 2013 [code]
- Distributed Pipeline for Genomic Variant Calling
Richard Xia, Sara Sheehan, Yuchen Zhang, Ameet Talwalkar, Matei
Zaharia, Jonathan Terhorst, Michael Jordan, Yun S. Song, Armando
Fox, and David Patterson
NIPS: Big Learning workshop, December 2012
- Telescoper: de novo assembly of highly repetitive regions
Ma'ayan Bresler, Sara Sheehan, Andrew H. Chan, and Yun S. Song
Bioinformatics, September 2012
Last summer I worked with four Swarthmore students
. In October they presented their work at the
Sinai Undergraduate Research Symposium
. My current lab members are:
- Society for Molecular Biology and Evolution (SMBE) meeting: A Likelihood-Free Inference Framework for Population Genetic Data using Permutation-Invariant Neural Networks.
Yokohama, Japan. July 10, 2018.
- VU Women in Tech Conference, Villanova University: Machine Learning: What it Is and Why it Matters.
Villanova, PA. January 27, 2018.
- Statistical and Computational Challenges in Large Scale Molecular Biology, BIRS Workshop: Towards automated population genetic inference using deep neural networks.
Banff, Canada. March 29, 2017. [video]
- Computer Science Colloquium, Williams College: Deep learning for population genetic inference.
Williamstown, MA. October 28, 2016.
- Evolgenome Seminar, Stanford University: A deep learning approach to ancestral inference.
Stanford, CA. October 1, 2014.
- Society for Molecular Biology and Evolution (SMBE) meeting: A deep learning approach to ancestral inference.
San Juan, Puerto Rico. June 9, 2014.
- American Society of Human Genetics (ASHG) meeting: Estimating human population sizes using the coalescent with recombination.
San Francisco, CA. November 8, 2012.
- Bay Area Population Genetics (BAPG) meeting at UC Davis: Estimating ancient population sizes using the coalescent with recombination.
Davis, CA. May 26, 2012.
Visiting Assistant Professor
Department of Computer Science
|2015 - 2017
Assistant Professor Department of Computer Science
|2010 - 2015
PhD, Computer Science
Designated Emphasis in Computational and Genomic Biology
|2008 - 2010
Massachusetts Institute of Technology
BS, Mathematics with Computer Science (18C)
|2006 - 2007
Harvey Mudd College