Daniel E. Acuna
Research Associate at the Rehabilitation Institute of Chicago
Research Affiliate in the Department of Biomedical Engineering at Northwestern University
Office room: RIC 1479
- New formula beats citation index and can predict success, 3 scientist say (2012), The Chronicle of Higher Education, A20 Article
- Predicting scientific success (September 13, 2012), Nature Podcast Podcast
- Science Friday, NPR Spanish, Podcast
- April, 2016 - Plenary talk - Tools to improve peer review and scholarly research, University of Wisconsin, Madison
- March, 2016 - Lighting talk - Predicting who will agree to review, Washington, DC
- March, 2016 - Plenary talk - Data science to understand knowledge discovery and expertise, ChiPy, International Symposium on Science of Science, Chicago, IL
- October, 2015 - Lightning talk - Should we allow authors to suggest reviewers?, Quantifying Science satellite conference, Tempe, AZ
- July, 2015 - Tools and software to accelerate science, Metaknowledge Network Summer Retreat, Asilomar, California
- March, 2015 - Science of science, Metaknowledge Network Spring Retreat, University of Chicago
- November, 2014 - Plenary talk - Big data science of science, Science Week 2014, Loyola University, Chicago
- August 2014, Automatic detection of figure element reuse in biological science articles, Science of Team Science Conference, Austin, TX
- May 2014, Big data machine learning for prediction and classification (invited academic speaker, plenary), The Tenth Workshop on the Development of Advanced Algorithms for Security Applications (ADSA10)
- March 2013, An investigation of how prior beliefs influence decision-making under uncertainty in a 2AFC task, (plenary) COSYNE 2013
- Ethier, C, Acuna, DE, Solla, S, Miller, L Adaptive Neuron-to-Muscle Decoder Training for FES Neuroprostheses, Journal of Neural Engineering, In Press
- Ramkumar P, Acuna DE, Berniker M, Grafton S, Turner RS, Körding KP. Chunking as the result of an efficiency–computation tradeoff. Nature Communications, In Press.
- Acuna, DE, Berniker, M, Fernandes, H, Kording, K, (2015) Using psychophysics to ask if the brain samples or maximizes, Journal of Vision
- Acuna, DE, Wymbs, Nicholas F., Reynolds, Chelsea A., Picard, Nathalie, Turner, Robert S., Strick, Peter L., Grafton, Scott T.,
Kording, Konrad P. (2014) Multi-faceted aspects of chunking enable robust algorithms, Journal of Neurophysiology, Link, code
- Acuna, DE, Penner, Orion, Orton CG, (2013) The future h-index is an excellent way to predict scientists’ future impact, Med. Phys. 40, 110601 (Link)
- Acuna, DE, Allesina, S., Kording, KP, (2012) Future impact: Predicting scientific success, Nature, Volume 489, Number 7415, 201-202
- Avraham G, Nisky I, Fernandes HL, Acuna DE, Kording KP, Loeb GE, Karniel A, (2011) Towards Perceiving Robots as Humans – Three handshake models face the Turing-like Handshake Test, IEEE Transactions on Haptics
- Acuna, DE, (2011) Rational Bayesian Analysis of Sequential Decision-Making Under Uncertainty In Humans and Machines, Ph.D. Thesis, University of Minnesota-Twin Cities
- Acuna, DE & Schrater, P. (2010). Structure Learning in Human Sequential Decision-Making, PLoS Computational Biology 6(12): e1001003
- Acuna, DE & Parada, V. (2010). People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours, PLoS ONE 5(7):e11685
- Acuna, DE & Schrater, P. (2009). Improving Bayesian Reinforcement Learning using Transition Abstraction. ICML/UAI/CLT Workshop on Abstraction in Reinforcement Learning 2009
- Acuna, DE & Schrater, P. (2009). Structure Learning in Human Sequential Decision-Making. NIPS 2009
- Acuna, DE & Schrater, P. (2008). Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Washington, DC: Cognitive Science
We have been awarded an Amazon Web Services (AWS) grant! This will allow us to analyze big datasets with arbitrary software or with many of the frameworks that they already have available.
“Big Data” Science of Science
I study large data sets to make sense of the scientific enterprise. In a manner similar to what Google, Amazon, and Netflix do, my colleagues and I try to find statistical regularities in large, unstructured data from heterogeneous sources to better understand publication, funding, and teaching activities. By using machine learning techniques, we hope to distill the rules that tell apart successful from less successful ways of doing science and that predict quantities such as the h-index, yearly funding cost, and students’ teaching evaluations.
I am currently involved in projects that span many areas of research. While I keep my interest in sequential decision-making under uncertainty, I have expanded my interests toward applying machine learning to scientometrics on large to very-large data sets (recently known as “big data” science). Following are projects in reverse chronological order
Predicting scientific career trajectories
Thanks to the recent advances of “open access” projects (publications , collaboration networks, funding), it is now easier than ever to quantitatively answer questions about the scientific enterprise. In a recent research article, we (together with Konrad Kording and Stefano Allesina) built a large-scale data set starting from 38K scientists (mainly from neuroscience) and their publication history—among other things. Our aim was to predict h-index (a simple measure of publication ‘success’), new funding, and teaching scores. In a Nature commentary, we investigated the features that are most important and predictive of future h-index. Try the Online calculator.
Large-scale automated reviewer suggestion
We have recently acquire a large database of abstracts and full-texts that will be used to study and improve the reliability, quality, and turnaround time of reviews. This is a collaboration project with the Amaral Lab.