Daniel E. Acuna

Daniel E. Acuna

Assistant Professor

312 Hinds Hall

deacuna@syr.edu

http://acuna.io

Overview

My work involves:

The methods and techniques used in my research include:

  • Big data Machine Learning with Apache Spark including bagging and boosting
  • Deep learning with Tensorflow
  • Bayesian statistics
  • Python, R, Hadoop, and SQL.

If you have questions, send me an email to deacuna AT syr DOT edu. Otherwise, apply to the Ph.D. program and mention my name in you materials.

Part of the funding for these positions has been generously provided by NSF #1646763, Amazon AWS, and Microsoft Azure.

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, 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://pubmed.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

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


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
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