Data Science Research Experience for
Data science is a rapidly growing field due to increases in the volume and variety of data being generated.
Graduates with skill sets at the intersection of mathematics, statistics, computing, data analysis, and data modeling are in high demand, both in industry and academia.
We seek applications for Summer 2019 in the IUPUI REU in data science of risk and human activity.
APPLY HERE (Deadline Feb 15)
Risk and Human Activity
The REU program at Indiana University Purdue University Indianapolis provides eight undergraduate students from across the United States with the opportunity to conduct research on the data science of human activity.
Students spend ten weeks during the summer working with IUPUI faculty on data science projects. Recent projects have included applications such as crime forecasting, ambulance optimization, human mobility, the opioid crisis, and social networks.
Students also attend a data science bootcamp as part of the program that will provide training in the foundations of data science (statistics, machine learning, and software development).
Dates: June 3rd to August 9 2019
Eligibility: any undergrad student who is a United
States Citizen or permanent resident
$5000 stipend and additional $2500 to cover meals
$400 travel expenses
Students entering their junior or senior year with
backgrounds in mathematics, computer science, statistics, or other quantitative disciplines (physics, engineering, etc) are encouraged to apply.
Assoc. Professor, IUPUI
Assoc. Professor, IUPUI
Need more details? Contact
Publications and preprints
J. Lu, S. Sridhar, R. Pandey, M. Al Hasan, and G. Mohler, Investigate Transitions into Drug Addiction through Text Mining of Reddit Data. Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (KDD 2019).
K. Gray, D. Smolyak, S. Badirli, and G. Mohler. Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data. In review at Transactions on Spatial Algorithms and Systems.
Baas, A., Hung, F., Sha, H., Al Hasan, M., & Mohler, G. (2018, December). Predicting Virality on Networks Using Local Graphlet Frequency Distribution. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 2475-2482). IEEE.
R. Hosler, M. Saper, X. Liu, J. Carter, A. Ganci, J. Hill, R. Raje, and G. Mohler. RaspBary: Hawkes point process Wasserstein barycenters as a service. Github page: https://github.com/rjhosler/IUPUI-REU/