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.
T Zeng, DE Acuña, (2019) Dead Science: Most Resources Linked in Biomedical Articles Disappear in Eight Years, International Conference on Information
JF Liénard, T Achakulvisut, DE Acuña, SV David. (2018) Intellectual synthesis in mentorship determines success in academic careers, Nature Communications
Ramkumar P, Acuña DE, Berniker M, Grafton S, Turner RS, Körding KP. (2016) Chunking as the result of an efficiency–computation tradeoff. Nature Communication
Acuña, DE, Penner, Orion, Orton CG, (2013) The future h-index is an excellent way to predict scientists’ future impact, Med. Phys
Acuña, DE, Allesina, S., Kording, KP, (2012) Future impact: Predicting scientific success, Nature
co-PI: Daniel E. Acuña, PI: Stephen David (Oregon) NSF-SciSIP: Collaborative Research: Social Dynamics of Knowledge Transfer Through Scientific Mentorship and Publication, 10/1/2019 – 9/30/2021
PI: Daniel E. Acuña, DDHS: Office of Research Integrity: (Conference grant) Computational Research Integrity Conference (CRI-CON), 9/1/2019 – 8/31/2020
PI: Daniel E. Acuña, DDHS: Office of Research Integrity: Human-centered automatic tracing, detection, and evaluation of image and data tampering, 9/1/2019 – 8/31/2020
PI: Daniel E. Acuña, DDHS: Office of Research Integrity: Methods and tools for scalable figure reuse detection with statistical certainty, Award ORI2018000296, 8/1/2018 – 7/31/2020
PI: Daniel E. Acuña, co-PIs: Konrad Kording (UPenn), James Evans (U of Chicago) NSF-SciSIP: Optimizing Scientific Peer Review, Award #1800956, 7/1/2018 – 6/30/2021
If you are considering taking IST 718: Big Data Analytics with me, please read the following information. I like to combine strong theoretical foundations with practical use cases. The theoretical foundation will help students decide which solutions work best; the technical aspects of data science will help students get a job.