基本信息
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职业迁徙
个人简介
I work in the Core Data Science team at Facebook, doing quantitative research to understand people's online behavior in the context of a large-scale social network.
Education
2010 PHD IN COMPUTER SCIENCE, Rutgers University-New Brunswick. Dissertation: “An Object-oriented Representation for Efficient Reinforcement Learning”. Advisor: Michael Littman. 2003 LICENCIATURA IN COMPUTER SCIENCE (6-year degree), University of Buenos Aires. Thesis: “A Computational Tool for the Reconstruction of Genealogies”.
Experience
2009-2013 PRINCETON UNIVERSITY. Research Scientist (2013) and Postdoctoral Research Associate (2009-2012). Yael Niv and Matthew Botvinick laboratories. Department of Psychology and Neuroscience Institute. 2007 GOOGLE (NY). Graduate Summer Intern in the Personalized Search group. 2006 INTEL RESEARCH. Graduate Research Intern in the Distributed Detection and Inference group. 2004 YALE UNIVERSITY. Developer of web infrastructure for research projects in the Dept. of Public Health. 1998-03 UNIVERSITY OF BUENOS AIRES. Research Assistant for Enrique Tándeter, developing algorithms for genealogy reconstruction from historical records. 2003 Project Manager at Tecnonexo (USA office). 2000-03 Independent consultant for more than 10 major companies in Argentina 1998-00 Project Leader at Lemma Informatics for Movicom/Bellsouth Argentina and the Public Libraries of the City of Buenos Aires.
Publications:
In press. “Divide and conquer: hierarchical reinforcement learning and task decomposition in humans.”, Carlos Diuk, Anna Schapiro, Natalia Córdova, José Ribas-Fernandes, Yael Niv and Matthew M. Botvinick. In Computational and Robotic Models of the Hierarchical Organization of Behavior. Edited by Baldassare G and Mirolli M. Springer Verlag.
2014. “Optimal Behavioral Hierarchy”, Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew M. Botvinick. PLOS Computational Biology. link
2014. “Parsing heuristic and forward search in first-graders game-play behavior”, Luciano Paz, Andrea Goldin, Carlos Diuk and Mariano Sigman. Cognitive Science. link
2013. “Hierarchical Learning Induces Two Simultaneous, But Separable, Prediction Errors in Human Basal Ganglia”, Carlos Diuk, Karin Tsai, Jonathan Wallis, Matthew M. Botvinick and Yael Niv. The Journal of Neuroscience. link
2012. “A quantitative philology of introspection”, Carlos Diuk, Diego F. Slezak, Iván Raskovsky, Mariano Sigman and Guillermo Cecchi. Frontiers in Integrative Neuroscience. link
2011. “A Neural Signature of Hierarchical Reinforcement Learning”, José J.F. Ribas-Fernandes, Alec Solway, Carlos Diuk, Joseph T. McGuire, Andrew G. Barto, Yael Niv and Matthew M. Botvinick. Neuron, Volume 71, Issue 2, 370-379. abstract
2010. PhD Thesis: "An object-oriented representation for efficient reinforcement learning". pdf
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Computational and Robotic Models of the Hierarchical Organization of Behaviorpp.271-291, (2013)
Neuronno. 2 (2011): 370-379
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