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Daniel E. Acuna

Daniel E. Acuna

Assistant Professor

312 Hinds Hall

deacuna@syr.edu

https://acuna.io

Overview

Professor Acuna is looking for Ph.D. and first-year Master's students. See announcement

About me

Dr. Acuna 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. Acuna 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.

Grants

PI: Daniel E. Acuna, DDHS: Office of Research Integrity: Methods and tools for scalable figure reuse detection with statistical certainty, Award ORI2018000296, reporting, 8/1/2018 - 7/31/2019

PI: Daniel E. Acuna, co-PIs: Konrad Kording (UPenn), James Evans (U of Chicago) NSF-SciSIP: Optimizing Scientific Peer Review, Award #1800956, 7/1/2018 - 6/30/2021

PI: Daniel E. Acuna, NSF EAGER: Improving scientific innovation by linking funding and scholarly literature, Award#1646763, 9/1/2016 - 8/31/2018

Research

Since his Bachelor studies in Computer Science at the University of Santiago, Chile, Dr. Acuna 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. Acuna 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. Acuna 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:/scholarfy.net), peer review (http://pr.scienceofscience.org), and modeling of scientific expertise (http://map.scienceofscience.org). Dr. Acuna 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.

Selected publications

  • Ramkumar P, Acuna DE, Berniker M, Grafton S, Turner RS, Körding KP. (2016) Chunking as the result of an efficiency–computation tradeoff. Nature Communication
  • Acuna, DE, Berniker, M, Fernandes, H, Kording, K, (2015) Using psychophysics to ask if the brain samples or maximizes, Journal of Vision
  • Acuna, DE, Penner, Orion, Orton CG, (2013) The future h-index is an excellent way to predict scientists’ future impact, Med. Phys
  • Acuna, DE, Allesina, S., Kording, KP, (2012) Future impact: Predicting scientific success, Nature

Teaching

If you are considering taking IST 718: Advanced Information 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 ;)

Personal

Economics, Theoretical Computer Science, the unfinished works of classical piano composers, and why cats like catnip.

Teaching


Teaching History - 2019-2020
Semester Course Section Title
Fall 2019 IST718 M002 Big Data Analytics
Fall 2019 IST718 M003 Big Data Analytics
Teaching History - 2018-2019
Semester Course Section Title
Fall 2018 IST718 M002 Big Data Analytics
Fall 2018 IST718 M003 Big Data Analytics
Spring 2019 IST718 M002 Big Data Analytics
Spring 2019 IST718 M003 Big Data Analytics
Summer 2019 IST718 M001 Big Data Analytics
Teaching History - 2017-2018
Semester Course Section Title
Fall 2017 IST718 M002 Advanced Information Analytics
Fall 2017 IST690 M800 Independent Study
Spring 2018 IST718 M002 Advanced Information Analytics
Spring 2018 IST718 M003 Advanced Information Analytics
Summer 2018 IST718 M001 Advanced Information Analytics
Teaching History - 2016-2017
Semester Course Section Title
Fall 2016 IST718 M003 Advanced Information Analytics
Spring 2017 IST718 M002 Advanced Information Analytics
Spring 2017 IST718 M003 Advanced Information Analytics
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