Daniel Larremore

Daniel B. Larremore

Asst. Professor, Univ. Colorado Boulder

BioFrontiers Institute & Dept. of Computer Science

CV - Google Scholar
Twitter - GitHub
daniel.larremore(at)colorado.edu
(gratuitous collaboration graph - hover or click)

News

About My Work

  • My research focuses on developing methods of networks, dynamical systems, and statistical inference, to solve problems in social and biological systems. I try to keep a tight loop between data and theory, and learn a lot from confronting models and algorithms with real problems.
  • I obtained my PhD in Applied Mathematics from the University of Colorado Boulder in 2012, advised by Juan G. Restrepo, after which I spent three years as a postdoctoral fellow at the Harvard T.H. Chan School of Public Health studying the genetic epidemiology of malaria in the Center for Communicable Disease Dynamics. I then joined the Santa Fe Institute as an Omidyar Fellow until 2017, when I joined the faculty at the University of Colorado Boulder in the Department of Computer Science and the BioFrontiers Institute.
  • Malaria's antigenic variation and evolution - The var genes of the malaria parasite P. falciparum evolve according to complicated and unknown rules, with selective pressures at multiple scales both within hosts and between hosts. I create and use mathematical tools to understand the structural and evolutionary constraints on var gene evolution, and their relationships with parasite virulence, population structure, and epidemiology.
  • Networks and theory - The processes that generate complex networks leave hints about themselves in the patterns of edges, and the relationships between those patterns and vertex metadata. I work on mathematical descriptions of graph ensembles, inference of community structures, vertex ordering or ranking, and using metadata to better understand network formation.
  • The scientific ecosystem - The scientific method of hypothesis, experiment, and conclusion poorly describes modern scientific discovery and productivity. Instead, science is done by people who play various social roles in the ecosystem of science. I investigate faculty hiring, productivity patterns, scientific careers, and the dynamics of discovery through large-scale data collection and modeling.

Research Group

  • PhD Students

  • tzu chi yen
  • Tzu-Chi Yen studies Computer Science and is interested in statistical models to understand data in the wild, as well as related algorithmic issues. He is also curious about American society and food, and wants to do more hiking and rock climbing in Colorado. He received a B.S. in Biology from National Taiwan University.
  • hunter wapman
  • Hunter Wapman studies Computer Science and is interested in (among other things) models of human behavior and structure in art. He spends his time outside the lab writing fiction, rock climbing, running distance, and reading. He received a B.S. in Computer Science from Tufts University.
  • erik johnson
  • Erik Johnson studies Applied Math and is interested in using statistical models to answer questions in biology, medicine, and public policy. Outside the lab, he enjoys trail running, road biking, music, and being cool. He received a B.A. in Math and Physics from Northwestern University and an M.S. in Applied Math from CU Boulder.
  • Undergrad Students

  • apara venkat
  • Apara Venkateswaran studies Applied Math and Computer Science. His research interests lie at the intersection of math and computation—mathematical modeling, stochastic processes, and machine learning. He is currently working to automatically discover structure in documents to facilitate the study of the scientific ecosystem.

  • suyog soti
  • Suyog Soti studies Computer Science and Applied Math. He is interested in everything that has the potential to enhance human lives; it's mainly machine learning and other AI techniques currently. He plays soccer and climbs when he is not trying to debug his latest project.
  • katie younglove
  • Katie Younglove studies Computer Science. She is primarily interested data science and machine learning, and is currently working on collecting and parsing data to study faculty hiring and retention. In her free time, Katie enjoys crafting convoluted puns and listening to npr.

Papers Under Review or In Prep

  • 2019 "webweb: a tool for creating, displaying, and sharing interactive network visualizations on the web"
  • K. Hunter Wapman, Daniel B. Larremore. Submitted.
  • 2019 "Network models for malaria: antigens, dynamics, and evolution over space and time"
  • Lauren M. Childs, Daniel B. Larremore. Submitted.
  • 2018 "Dynamics of Beneficial Epidemics"
  • Andrew Berdahl, Christa Brelsford, Caterina De Bacco, Marion Dumas, Vanessa Ferdinand, Joshua A. Grochow, Laurent Hébert-Dufresne, Yoav Kallus, Christopher P. Kempes, Artemy Kolchinsky, Daniel B. Larremore, Eric Libby, Eleanor A. Power, Caitlin A. Stern, Brendan Tracey. Under review.
  • [arXiv].
  • 2018 "Plasmodium falciparum population genetic complexity influences expression dynamics and immune recognition among highly related genotypic clusters"
  • Amy K. Bei, Daniel B. Larremore, Kazutoyo Miura, Ababacar Diouf , Nicholas Baro, Rachel F. Daniels, Allison Griggs, Eli L. Moss, Daniel E. Neafsey, Awa B. Deme, Mohammed Sy, Stephen Schaffner, Ambroise D. Ahouidi, Daouda Ndiaye , Tandakha Dieye, Souleymane Mboup, Caroline O. Buckee, Sarah K. Volkman, Carole A. Long, and Dyann F. Wirth. Under review.

* denotes equal contribution.

Peer-reviewed publications

  • 2019 "Productivity, prominence, and the effects of academic environment"
  • Samuel F. Way, Allison C. Morgan, Daniel B. Larremore*, Aaron Clauset*. Proceedings of National Academy of Sciences, USA.116 (18).
  • [PDF][PNAS].
  • 2019 "Bayes-optimal estimation of overlap between populations of fixed size"
  • Daniel B. Larremore. PLoS Computational Biology 15(3): e1006898.
  • [PDF][PLOS Computational Biology][code][web tool].
  • 2018 "Robust information capacity requires strong and balanced excitatory and inhibitory synapses"
  • Vidit Agrawal, Andrew B. Cowley, Woodrow L. Shew, Daniel B. Larremore, Juan G. Restrepo, and Qusay Alfaori. Chaos 28 103115.
  • [Chaos][arXiv].
  • 2018 "A physical model for efficient ranking in networks"
  • Caterina De Bacco*, Daniel B. Larremore*, and Cristopher Moore. Science Advances, 4(7) eaar8260.
  • [Science Advances][arXiv][code].
  • 2018 "Configuring random graph models with fixed degree sequences"
  • Bailey K. Fosdick*, Daniel B. Larremore*, Joel Nishimura*, and Johan Ugander*. SIAM Review 60 (2) 315-355.
  • [PDF][SIAM Review][arXiv][code and slides].
  • 2017 "The misleading narrative of the canonical faculty productivity trajectory"
  • Samuel F. Way, Allison C. Morgan, Aaron Clauset*, and Daniel B. Larremore*. Proceedings of the National Academy of Sciences, USA 114 (44) E9216-E9223.
  • [PNAS][PDF][arXiv]
  • 2017 "The ground truth about metadata and community detection in networks"
  • Leto Peel*, Daniel B. Larremore*, and Aaron Clauset. Science Advances 3(5) e1602548.
  • [PDF][Science Advances][Supplement][code and data].
  • 2017 "Community detection, link prediction, and layer interdependence in multilayer networks"
  • Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, and Cristopher Moore. Physical Review E 95 042317.
  • [PDF][Phys Rev E][arXiv].
  • 2016 "Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks"
  • Samuel F. Way, Daniel B. Larremore, and Aaron Clauset. Proc. 2016 World Wide Web Conference (WWW), 1169-1179.
  • [PDF][Proc WWW 2016]
  • 2015 "Ape parasite origins of human malaria virulence genes"
  • Daniel B. Larremore, Sesh A. Sundararaman, Weimin Liu, William R. Proto, Aaron Clauset, Dorothy E. Loy, Sheri Speede, Lindsey J. Plenderleith, Paul M. Sharp, Beatrice H. Hahn, Julian C. Rayner*, and Caroline O. Buckee*. Nature Communications 6, 8368.
  • [PDF][Nature Comms]
  • 2015 "Systematic inequality and hierarchy in faculty hiring networks"
  • Aaron Clauset, Samuel Arbesman, and Daniel B. Larremore, Science Advances. 1, e1400005.
  • [PDF][Science Advances][code and data][interactive data visualization]
  • 2015 "Immune characterization of P. falciparum parasites with a shared genetic signature in a region of decreasing transmission"
  • Amy K. Bei, Ababacar Diouf, Kazutoyo Miura, Daniel B. Larremore, Ulf Ribacke, Gregory Tullo, Eli L. Moss, Daniel E. Neafsey, Rachel F. Daniels, Amir E. Zeituni, Iguosadolo Nosamiefan, Sarah K. Volkman, Ambroise D. Ahouidi, Daouda Ndiaye, Tandakha Dieye, Souleymane Mboup, Caroline O. Buckee, Carole Long, and Dyann F. Wirth, Infection and Immunity 83(1), 276.
  • [PDF][Infection and Immunity]
  • 2014 "Efficiently inferring community structure in bipartite networks"
  • Daniel B. Larremore, Aaron Clauset, and Abigail Z. Jacobs, Physical Review E 90(1), 012805.
  • [PDF][Phys Rev E][code and data]
  • 2014 "Inhibition Causes Ceaseless Dynamics in Networks of Excitable Nodes"
  • Daniel B. Larremore, Woodrow L. Shew, Edward Ott, Francesco Sorrentino, and Juan G. Restrepo, Physical Review Letters 112, 138103.
  • [PDF] [Phys Rev Letters]
  • 2013 "A network approach to analyzing highly recombinant malaria parasite genes"
  • Daniel B. Larremore, Aaron Clauset, and Caroline O. Buckee. PLoS Computational Biology 9(10), e1003268.
  • [PDF] [PLoS Comput Biol] [interactive figures] [network and sequence data]
  • 2012 "Social Climber attachment in forming networks produces phase transition in a measure of connectivity"
  • Dane Taylor*, Daniel B. Larremore*. Physical Review E 86, 031140.
  • [PDF][Phys Rev E]
  • 2012 "Statistical properties of avalanches in networks"
  • Daniel B. Larremore, Marshall Y. Carpenter, Edward Ott, and Juan G. Restrepo. Physical Review E 85, 066131.
  • [PDF] [Phys Rev E]
  • 2011 "Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus"
  • Daniel B. Larremore, Woodrow L. Shew, Edward Ott, and Juan G. Restrepo. Chaos 21, 025117.
  • [PDF] [Chaos]
  • 2011 "Predicting criticality and dynamic range in complex networks: effects of topology"
  • Daniel B. Larremore, Woodrow L. Shew, and Juan G. Restrepo. Physical Review Letters 106, p. 058101.
  • [PDF] [Phys Rev Letters]

* denotes equal contribution.

Essays, Columns, Book Chapters, Misc

  • 2017 "More Inclusive Scholarship Begins With Active Experimentation"
  • Daniel B. Larremore, Allison C. Morgan, and Aaron Clauset. The Chronicle of Higher Education, 1 Nov, 2017.
  • [PDF][The Chronicle of Higher Education]
  • 2017 "Why predicting the future is more than just horseplay"
  • Daniel B. Larremore and Aaron Clauset. The Christian Science Monitor, 24 April, 2017.
  • [The Christian Science Monitor]
  • 2017 "On the records"
  • Andrew Berdahl, Uttam Bhat, Vanessa Ferdinand, Joshua Garland, Keyan Ghazi-Zahedi, Justin Grana, Joshua A. Grochow, Elizabeth Hobson, Yoav Kallus, Christopher P. Kempes, Artemy Kolchinsky, Daniel B. Larremore, Eric Libby, Eleanor A. Power, Brendan D. Tracey. [arXiv].
  • 2017 "Data-driven predictions in the science of science"
  • Aaron Clauset, Daniel B. Larremore, and Roberta Sinatra. Science 355(6324) 477-480.
  • [Science][PDF]
  • 2014 Critical Dynamics in Complex Networks, chapter in Criticality in Neural Systems.
  • Daniel B. Larremore, Woodrow L. Shew, Juan G. Restrepo. Wiley, 365-392, 2014.
  • [Wiley]
  • 2012 "Progess Is Infectious"
  • Daniel E. Geer Jr., Daniel B. Larremore. IEEE Security & Privacy, 10(6), p. 94-95.
  • [PDF] [IEEE S&P]

Software and Code

  • webweb
  • webweb is a free software tool for creating interactive network visualizations in NetworkX, Python, and MATLAB, that are viewable in your web browser. The network at the top of this page is an example of webweb, with some modifications.
  • Why? I wanted a function that I could just call to instantly view/explore a network adjacency matrix. webweb(A) does exactly that
  • [installation and docs][github]
  • percoVIS icon
  • PercoVIS is a free software tool, created to visualize the process of percolation on a network. It was developed with much inspiration and feedback from my collaborator, Dane Taylor. It includes Erdos-Renyi and Adjacent Edge decision rules for link addition, as well as the Social Climber attachment model. Documentation about these processes can be found on the download page. If you are interested in a similar tool to visualize Kuramoto oscillators, please check out Sebastian Skardal's Synched software.
  • [Download PercoVIS]
  • biSBM
  • biSBM is free and open-source code, created to apply the method described in the paper "Efficiently inferring community structure in bipartite networks". The code finds communities in a bipartite network using a maximum-likelihood approach stochastic block model. The code requires MATLAB as well as a C++ compiler. Details, and the publication on which this code is based, can be found through the link below.
  • [Download biSBM]
  • MATLAB Codes
    • findNetworkComponents - take an undirected network adjacency matrix and return the number of components, their sizes, and their membership lists.
    • [MATLAB File Exchange]

Press

Teaching, Workshops, Recordings

Current Courses (Spring 2019):

Slides and Lecture Recordings:

Previous courses (and my evaluations)

  • University of Colorado at Boulder
  • 2018 Spring - CSCI 3022, Intro to Data Science w/ Prob & Stats
  • 2017 Fall - CSCI 3022, Intro to Data Science w/ Prob & Stats (Rebuilt)
  • -
  • 2012 Spring - APPM 2350, Calculus III (Instructor)
  • 2011 Fall - APPM 2350, Calculus III (Instructor)

Workshops and Guest Lecturing:

  • University of Colorado at Boulder
  • 2014 Oct 9, CSCI 5352. Network Analysis and Modeling
  • 2013 Nov 5-7, PHYS 7810 / CHEM 6711 / MCDB 6400. Foundations of Quantitative Biology
  • Harvard Public Health summer nanocourse: Modeling Infectious Disease
  • 2014 July 24 and 27. [details]
  • AMS Mathematics Research Community
  • 2014 June 24-30, Network Science. [details]
  • Oxford Tropical Disease Network Meeting, Kilifi, Kenya
  • 2013 Oct 3, Network Analysis Workshop.

Contact Details

BioFrontiers Institute
3415 Colorado Avenue
Boulder, CO 80303
USA