Daniel Acuna

Dr. Acuña studied a Ph.D. in Computer Science at the University of Minnesota, Twin Cities. During his graduate studies, he was part of the Center for Cognitive Sciences in the Department of Psychology and received a NIH Neuro-physical-computational Sciences (NPCS) Graduate Training Fellowship from the Department of Neuroscience. He additionally received the support of a CONICYT-World Bank fellowship and a travel award from the Neural Information Processing Systems (NIPS) 2008 conference. During his postdoctoral studies at the Rehabilitation Institute of Chicago and Northwestern University, Dr. Acuña gave multiple invited plenary talks and was interviewed by Nature Podcast, The Chronicle of Higher Education, NPR Science Friday, and The Scientist. Amazon AWS and Microsoft Azure have generously supported his big data analytics work with three academic computational credit awards. Dr. Acuña runs the Science of Science and Computational Discovery Lab (SOS+CD). Since his Bachelor studies in Computer Science at the University of Santiago, Chile, Dr. Acuña has had a long interest in understanding human decision making and mimicking human semi-optimal strategies with algorithms. His long-term goal is to teach computers to learn from humans and enhance human decision making through the use of Machine Learning and Artificial Intelligence. As a postdoctoral researcher at the Rehabilitation Institute of Chicago and Northwestern University, Dr. Acuña studied machine learning, statistical decision theory, and the neural basis of learning.

The goal of his current research is to understand decision making in Science—from helping hiring committees to predict future academic success to removing the potential biases that scientists and funding agencies commit during peer review. To achieve these tasks, Dr. Acuña harnesses vast datasets about scientific activities and applies Machine Learning and A.I. to uncover rules that make publication, collaboration, and funding decisions more successful. Simultaneously, he has created tools to improve literature search (http:/eileen.io), peer review (http://pr.scienceofscience.org), and modeling of scientific expertise (http://map.scienceofscience.org). Dr. Acuña imagines a future in which humans and A.I. agents seamlessly cooperate to make science more agile and accurate.

Daniel enjoys making contributions to the open source Data Science community, often creating his own packages and tools (https://github.com/daniel-acuna). For example, he recently gave a talk to the Chicago Python User Group, where he shared his views with over 80 professional developers on how science and industry face similar challenges. He is also looking to license multiple technologies co-invented by him.

Education

• PhD University of Minnesota (2006 — 2011)
• M.S. Universidad de Santiago de Chile
• B.S. Universidad de Santiago de Chile

Companies

• Assistant Professor Syracuse University (2016)
• Research Associate Rehabilitation Institute of Chicago (2011 — 2016)
• Research Associate Northwestern University (2011 — 2016)
• Summer Research Assistant Carlson School of Management (2009 — 2009)
• Summer Research Assistant University of Minnesota (2008 — 2008)
• Teaching Assistant University of Minnesota (2008 — 2008)
• Teaching Assistant University of Minnesota (2007 — 2007)
• Summer Research Assistant University of Minnesota (2007 — 2007)
• Teaching Assistant University of Minnesota (2007 — 2007)

Skills

• R
• Java
• Algorithms

Other

Mathematica, C++, Data Mining, Mathematical Modeling, Science, Data Analysis, Matlab, Python, Machine Learning