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Alfredo Rago

Postdoctoral fellow | PhD

Machine learning algorithms inspired by evolution have recently taken the spotlight for performing incredible tasks. My work turns this logic around and asks if using evolutionary models inspired by learning processes can help us explain the incredible diversity of nature.

Can organisms become better at evolving, or do they only adapt to their immediate surroundings? Are their evolutionary options always open, or does their past preclude some of their potential paths?

If that is the case, studying an organism could allow us to both reconstruct its past and predict its future. However, in order to do so we need to understand how past experiences are stored in present development, and how present development affects future evolution.

To answer these question I study the organization of evolving developmental systems, using a combination of simulations and gene expression analyses. My empirical work informs the assumptions in my models, and my modelling provides strong theoretical predictions to my empyrical analyses.

Solving these issues requries a multidisciplinary approach. As such, I am currently working both within the Evolutionary Biology Uller group in Lunds University and with the Computational Modelling Watson group in the University of Southampton (UK).

Publications

Retrieved from Lund University's publications database

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Publications

Retrieved from Lund University's publications database

Publications

Retrieved from Lund University's publications database

Page Manager:
Alfredo Rago
E-mail: alfredo [dot] rago [dot] 0517 [at] biol [dot] lu [dot] se

Postdoctoral fellow

Evolutionary ecology

Sölvegatan 37, Lund

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