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- 24 Aug 2017
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Welcome to the second episode of the Santa Fe Institute's Education podcast, Green Chile Science! As a reminder, this podcast features the Director of Education, Dr. Paul Hooper, asking his scientist guests to chat about their research while downing gut-searing green chile from the Horseman's Haven Café in Santa Fe, New Mexico. You can follow the feed at greenchilescience.podbean.com. Listen to episode 2 here.
This time of year, the first chile roasters are getting set up around Santa Fe (though locals say you shouldn't buy your chile until after Santa Fe's Fiesta at the end of August). Soon the smell of roasting green chile will fill the air as big metal drums toss fresh chiles over scorching flames in parking lots around the city. By the time the next episode airs, freezers across Santa Fe will be stocked with different levels of hot, delicious, roasted green chile.
Green Chile Science Episode 2: Dr. Caterina DeBacco
Like in Dante's Inferno, Caterina and Paul descend across three levels of green chile hell to get to the heart of Caterina's research. Would you like to predict which soccer team will win the EuroCup? Or want to know how the alpha female got to the top of the dominance hierarchy? Maybe you want to establish once and for all which restaurant serves the best green chile cheeseburger? Caterina's algorithms for ranking players in networks might be just what you need. Caterina also tells us how her love of soccer led to a career in science, and describes new methods for finding communities in networks with many kinds of interaction. Listen to Episode 2 here.Explore Caterina's research in more depth:
- Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, and Cristopher Moore (2017) Community detection, link prediction, and layer interdependence in multilayer networks. Phys. Rev. E. 95:042317. https://link.aps.org/doi/10.1103/PhysRevE.95.042317
- SpringRank software on github: https://github.com/cdebacco/SpringRank
- Caterina De Bacco, Daniel B. Larremore, and Cristopher Moore (2017) A physical model for efficient ranking in networks. Arxiv preprint. https://arxiv.org/abs/1709.09002
