Anna Godwin, Halee Mason, Cory Everington, Danny Brady, and Kazlin Mason, aka Team HACK’D, won the Data Story Telling component of the Charlottesville Open Data Challenge. The team presented at the Tom Tom Founders Festival Machine Learning Conference as one of two finalist. Using open data, the goal of the Challenge was to engage the growing data science community within Charlottesville and the surrounding areas to help the City better understand pedestrian use of the Downtown Mall. For the data storytelling portion, each team was asked to use the data to craft a narrative and visualizations that explain what is happening in the data (trends, anomalies, outliers, etc.). The goal of data storytelling is to enlighten members of the target audience to insights that would not be clear without charts or graphs. The team also finished in fourth place for the Best Predictive Model component of the challenge. View the winning story here.
Elder Research Team Wins Data Story Telling Component of Charlottesville Open Data Challenge
Trent Bradberry, Ph.D. Receives Award from the Journal of Neural Engineering
Trent Bradberry, Ph.D., received an Outstanding Reviewer Award from the Journal of Neural Engineering. The annual award is given to reviewers who are judged by the editorial team to have provided high quality neuroscientific/engineering reviews of manuscripts under consideration for publication. Trent is one of only 24 award recipients for 2017 selected from an international community of reviewers. Journal of Neural Engineering was created to help scientists, clinicians and engineers to understand, replace, repair and enhance the nervous system.
Elder Research Sponsors Tom Tom Festival Applied Machine Learning Conference
Elder Research will be a Theme Sponsor for the Applied Machine Learning Conference on April 12, 2018 at the Violet Crown theater in downtown Charlottesville. Machine Learning is a technology that helps make sense of the massive amounts of data and, in today’s world, it is the key to survival for businesses. This day-long conference will convene researchers, entrepreneurs, and practitioners who use big data and machine learning applications on a wide variety of topics.
Jennifer Schaff Announced as a Winner of the DREAM Parkinson’s Disease Digital Biomarker Challenge
Data Scientist Jennifer Schaff, Ph.D. was announced as one of the winners of the DREAM Parkinson’s Disease Digital Biomarker Challenge. Jennifer used statistical methods to derive features and feature selection to develop the top performing submission in predicting dyskinesia severity with a 59% improvement over baseline models. More than 440 data experts participated in the challenge worldwide. The DREAM Challenge is funded by the Michael J. Fox Foundation and the Robert Wood Johnson Foundation.
Gerhard Pilcher & Jeff Deal Interviewed for The Big Biz Show
Aric LaBarr Spoke on Model Validation at Duke University
Senior Data Scientist Aric LaBarr spoke on Model Validation to Masters of Statistical Science students at Duke University on October 31, 2017.
RightShip Presenting Predictive Model Validated by Elder Research at PAW Business
Bryan Guenther, Qi Program Manager at RightShip, will present "Overcoming Challenges Implementing a Risk Model in the Maritime Industry" at Predictive Analytics World for Business in New York City on October 31st. The session will highlight challenges with implementing Predictive Analytics in a unique, ground-breaking case study of predictive model deployment in the maritime industry. Prior to launch RightShip contracted Elder Research to validate the Qi models predictive performance.
Wanye Folta's Article on Nested Cross Validation Published in PA Times
Data Scientist Wayne Folta's article "Nested Cross Validation: When (Simple) Cross Validation Isn’t Enough" was published in Predictive Analytics Times. The article addresses the proper use and partitioning of cross validation to help avoid under-performance in predictive models once deployed.
Zach Lamberty to Teach Advanced Mathematical and Statistical Computing at Georgetown University
Data Scientist Zach Lamberty designed and will teach a new graduate course Advanced Mathematical and Statistical Computing for the Department of Mathematics and Statistics at Georgetown University. The goals for this hands-on course are to learn about advanced computational tools and methods utilized in the data science field, including how to use and administer cloud computing resources, useful utilities in the Linux environment, and the AWS suite of cloud services. Among the AWS services the course will focus on general use services (e.g. EC2, S3), the Hadoop distributed computing ecosystem, the map reduce framework, and the application of these concepts to working with and understanding large datasets.
Stuart Price to Teach Mathematical and Statistical Computing at Georgetown University
Data Scientist Dr. Stuart Price will teach the graduate course Mathematical and Statistical Computing for the Department of Mathematics and Statistics at Georgetown University. The goal of this course is to provide students with programming background sufficient for graduate level study in mathematics and statistics. The course gives an introduction to R, SAS, Python and cloud computing. Statistical topics to be covered include data management, simulation, descriptive statistics, graphical displays, hypothesis testing, correlation, regression models, and simple multivariate analysis methods.