Daniel Larremore

Daniel B. Larremore

Asst. Professor, Univ. Colorado Boulder

BioFrontiers Institute & Dept. of Computer Science

CV - Google Scholar
Twitter - GitHub
(gratuitous collaboration graph - hover or click)


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 at Boulder in 2012, advised by Juan G. Restrepo, after which I spent three years as a postdoctoral fellow at the Harvard 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.
  • 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.
  • 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 use tools from applied math and statistical physics to understand the structural and evolutionary constraints on var gene evolution, and its their relationships with parasite virulence, population structure, and epidemiology.
  • Academic labor market dynamics - PhDs become faculty each year, but the influences of prestige, advisor, gender, publication record, among other factors, on actual hiring outcomes are not well known, even within individual fields. I investigate inequalities and dynamics of the academic labor market through large-scale data collection and generative models.

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 "Productivity, prominence, and the effects of academic environment"
  • Samuel F. Way, Allison C. Morgan, Daniel B. Larremore*, Aaron Clauset*. 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 "Bayes-optimal estimation of overlap between populations of fixed size"
  • Daniel B. Larremore. PLoS Computational Biology 15(3): e1006898.
  • [PDF][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]


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