I will be looking to hire soon, so stay tuned.
]]>PS: We can thank the Complex Systems Summer School at SFI for this project, since three of us met during the 2015 editions.
]]>The project has been a long time in the making; the Dynamica Lab started working on this project back in 2016 at a retreat (see a picture of the retreat after the break!). The product of our work first appeared as a preprint in 2018, and has significantly changed since: follow the link to take a look at what it became! Enormous thanks should go to the anonymous reviewers for their input that helped shape the manuscript. Details about where to find code, slides, and supplementary information are listed in my publication page.
PS: We already followed up on this work, together with Georce T. Cantwell at the University of Michigan, and Guillaume St-Onge at the Dynamica Labs. In the “sequel,” we derive an efficient and exact recovery algorithm for the past state of growing trees. By limiting ourselves to this special class of networks, we obtain a significant efficiency improvement on the algorithm used in the PRX paper, which relied on a state-of-the-art but costly sequential Monte-Carlo sampling method. Our manuscript is currently on the arXiv as a preprint.
(Part of) the team at our 2016 retreat.
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