Sara Mathieson Assistant Professor Computer Science Department Haverford College phone: (610) 896-1296 office: KINSC L302 email: smathieson [at] haverford [dot] edu

Teaching

• Current Semester
• Previous Courses

Research

• Funding
• Publications
• Lab
• Talks

Other

• Bio

I am currently an Assistant Professor in the Computer Science Department at Haverford College. I received my PhD in Computer Science in 2015 from UC Berkeley 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.

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Current Semester

Spring 2024 Schedule
Machine Learning
Lecture	Tues/Thurs 11:30am-1pm	H108
Introduction to Programming in Biology
Labs	Thurs 2-3pm and 3-4pm	H110
Office Hours	Mon 4-5pm	H110

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Previous Courses

Haverford College

Bioinformatics Superlab (BIO 301)	Spring 2021
Introduction to Data Structures (CS 106)	Spring 2020, Spring 2021
Foundations of Data Science (CS 260)	Fall 2021, Fall 2023
Machine Learning (CS 360)	Fall 2019, Fall 2020

Swarthmore College

Introduction to Computer Science (CS 21)	Fall 2017, Fall 2018
Machine Learning (CS 66)	Spring 2019
Bioinformatics (CS 68)	Spring 2018

Smith College

How Computers Work (CSC 103)	Spring 2016
Introduction to Computer Science (CSC 111)	Spring 2017
Programming with Data Structures (CSC 212)	Spring 2016
Computer Graphics (CSC 240)	Fall 2015, Fall 2016
Topics in Computational Biology (CSC 334)	Fall 2015
Topics in Artificial Intelligence (CSC 390)	Fall 2016

UC Berkeley (Teaching Assistant)

Discrete Mathematics and Probability Theory (CS 70)	Spring 2014
Algorithms for Computational Biology (CS 176)	Fall 2013

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Funding

• NIH R15 (R15HG011528), "Adaptive evolutionary inference frameworks for understudied populations using generative neural networks", 2020-2024

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Publications

More information on my Google Scholar page.

Publications:

• Interpreting Generative Adversarial Networks to Infer Natural Selection from Genetic Data
  Rebecca Riley, Iain Mathieson, Sara Mathieson
  Genetics, February 2024 [software]
• The genomic footprint of social stratification in admixing American populations
  Alex Mas-Sandoval, Sara Mathieson, Matteo Fumagalli
  eLife, December 2023
• Automatic Inference of Demographic Parameters Using Generative Adversarial Networks
  Zhanpeng Wang, Jiaping Wang, Michael Kourakos, Nhung Hoang, Hyong Hark Lee, Iain Mathieson, Sara Mathieson
  Molecular Ecology Resources -- Special Issue: Machine Learning in Molecular Ecology, March 2021 [pg-gan software]
• Ancestral Haplotype Reconstruction in Endogamous Populations using Identity-By-Descent
  Kelly Finke, Michael Kourakos, Gabriela Brown, Huyen Trang Dang, Shi Jie Samuel Tan, Yuval B. Simons, Alejandro A. Schaffer, Rachel L. Kember, Maja Bucan, Sara Mathieson
  PLoS Computational Biology, February 2021 [thread software]
• ImaGene: a convolutional neural network to quantify natural selection from genomic data
  Luis Torada, Lucrezia Lorenzon, Alice Beddis, Ulas Isildak, Linda Pattini, Sara Mathieson, Matteo Fumagalli
  BMC Bioinformatics, November 2019
• 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 [defiNETti software]
• 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 [evoNet software]
• 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 [diCal software]
• 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

Thesis:

• Scalable Algorithms for Population Genomic Inference
  PhD Thesis, UC Berkeley, May 2015

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Lab

News

• Jan 2023:
  Congratulations to Sam for being a 2023 CRA Outstanding Undergraduate Researcher Award recipient!

• Aug 2022:
  Trang and Sam present their work at ACM-BCB.

• May 2019:
  Congratulations to Kelly for receiving the Goldwater Scholarship!

• Oct 2018:
  Nhung, Kelly, and Mikey present their work at the Mount Sinai Undergraduate Research Symposium. Congratulations to Nhung for receiving a Best Poster award!

• July 2018:
  Lab prepares for department presentations.

Lab members

• Kai Britt '26
  
• Henry Britton '24
  
• Jadyn Elliott '25
  
• Sarah Keim '25
  
• Darshan Mehta '25
  

Lab alumni

• Sam Tan '23
• Trang Dang '23
• Rebecca Riley (postbac Research Associate)
• Joseph Tadrous '23
• Yuxuan Sun '23
• Mohamed Ali '22
• Claire Johnson '22
• Tung Do '23
• Juvia Han '22
• Alton Wiggers '21
• Kyra Zhang '21
• Luis Contreras-Orendain '21
• Kendra Griesman '20
• Russell Gerhard '20
• Pablo Thiel '20
• Jiaping Wang '20
• Zhanpeng Wang '20
• Michael Kourakos '21
• Dylan Slack '19
• Kelly Finke '21
• Nhung Hoang '19
• Hunter Lee '19
• Gabriela Brown '18
• Chloe Lee '17
• Artemis Metaxa-Kakavouli '19
• Silvana Saca '19
• Alice Yang '17
• Alice Mu '19
• Dejia Tang '18

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Selected Talks

• Bioinformatics and Computational Biology in the Liberal Arts, Reed College: Teaching computational biology to upper level CS students as an algorithms course.
  Portland, OR. June 22, 2023.
• Evolution in Action: Using Generative Adversarial Networks to Infer Natural Selection.
  Monte Verita, Switzerland. June 12, 2023.
• Ecological Genomics Seminar, University of New Hampshire: Deep Learning for Population Genetic Data.
  Durham, NH. October 5, 2022.
• Vienna Population Genetics seminar: Automatic evolutionary inference using Generative Adversarial Networks.
  (virtual) April 26, 2022. [video]
• NICHD-ASRM AI/ML Meeting: Deep neural networks for population genetics and evolutionary biology.
  (virtual) October 21, 2021.
• University of Toronto Scarborough: Automatic evolutionary inference using Generative Adversarial Networks.
  (virtual) September 24, 2021.
• Evolution meeting: Adaptive evolutionary inference across human populations using convolutional neural networks.
  (virtual) June 25, 2021.
• Probabilistic Modeling in Genomics (ProbGen): Automatic inference of demographic parameters using generative adversarial networks.
  (virtual) April 14, 2021.
• Machine Learning in Genomics workshop, NIH: Automatic evolutionary inference using Generative Adversarial Networks.
  (virtual) April 13, 2021.
• The Use of Machine Learning in the Behaviour, Ecology and Evolution; University of Neuchâtel: Automatic evolutionary inference using Generative Adversarial Networks.
  (virtual) March 9, 2021.
• Biological Discovery from Big Data, Drexel University: Population genetics with pedigrees and generative adversarial networks.
  (virtual) December 15, 2020.
• Brown University: Ancestral Haplotype Reconstruction in Endogamous Populations using Identity-By-Descent.
  Providence, RI. January 15, 2020.
• Janelia Research Campus, Women in Computational Biology conference: Population Genetic Inference Using Convolutional Neural Networks.
  Ashburn, VA. November 12, 2019.
• Society for Molecular Biology and Evolution (SMBE) meeting: Ancestral Haplotype Reconstruction in Endogamous Populations using Identity-By-Descent.
  Manchester, UK. July 23, 2019.
• 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.

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History

2019 -	Haverford College Assistant Professor Department of Computer Science Haverford, PA
2017 - 2019	Swarthmore College Visiting Assistant Professor Department of Computer Science Swarthmore, PA
2015 - 2017	Smith College Assistant Professor Department of Computer Science Northampton, MA
2010 - 2015	UC Berkeley PhD, Computer Science Designated Emphasis in Computational and Genomic Biology Berkeley, CA
2008 - 2010	Massachusetts Institute of Technology BS, Mathematics with Computer Science (18C) Cambridge, MA
2006 - 2007	Harvey Mudd College Claremont, CA