1002 West Green Street
Urbana, IL 61801
Robert J. Brunner is a professor in the School of Information Sciences and in the Department of Accountancy in the College of Business. He has affiliate appointments in the Astronomy, Computer Science, Electrical and Computer Engineering, Informatics, Physics, and Statistics Departments; at the Beckman Institute, in the Computational Science and Engineering program; and at the National Center for Supercomputing Applications. He is also the Data Science Expert in Residence at the Research Park at the University of Illinois.
His primary research goal focuses on the application of statistical and machine learning to a variety of real-world problems, and in making these efforts easier, faster, and more precise. This work spans fundamental algorithm design to more effectively incorporate uncertainty to optimization using novel computational technologies. More generally, Brunner helps lead efforts to promote data science across campus and to encourage effective data management, analysis, and visualization techniques.
Brunner earned his PhD in astrophysics at the Johns Hopkins University working under Alex Szalay on the development of the science archive for the Sloan Digital Sky Survey. His PhD thesis helped develop the statistical approach to quantifying galaxy evolution, where large data are used to place constraints on the original and evolution of the Universe. He subsequently spent five years as a postdoctoral scholar at the California Institute of Technology working under S. George Djorgovsi and Tom Prince as the project scientist for the Digital Sky project.
Statistical and Machine Learning
Advanced Computational Techniques
Transient and Variable Phenomena
The development of data science, the application of machine learning, algorithmic optimization, statistical uncertainty and its incorporation in machine learning, data management, effective visualization, and data storytelling.
Ph.D. Johns Hopkins University
Additional Campus Affiliations
Chief Disruption Officer, Gies College of Business
Interim Director, University of Illinois-Deloitte Foundation Center for Business Analytics, Gies College of Business
Arthur Andersen Faculty Fellow, Accountancy
Professor, Computer Science
Professor, Electrical and Computer Engineering
Professor, National Center for Supercomputing Applications (NCSA)
Professor, Beckman Institute for Advanced Science and Technology
Bracht, E., Kindratenko, V., & Brunner, R. J. (2022). Sparse Spatio-Temporal Neural Network for Large-Scale Forecasting. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10036330
Kaikaus, J., Hobson, J. L., & Brunner, R. J. (2022). Truth or Fiction: Multimodal Learning Applied to Earnings Calls. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 3607-3612). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020307
Hariri, S., Kind, M. C., & Brunner, R. J. (2021). Extended Isolation Forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479-1489. Article 8888179. https://doi.org/10.1109/TKDE.2019.2947676
Lacy, M., Surace, J. A., Farrah, D., Nyland, K., Afonso, J., Brandt, W. N., Clements, D. L., Lagos, C. D. P., Maraston, C., Pforr, J., Sajina, A., Sako, M., Vaccari, M., Wilson, G., Ballantyne, D. R., Barkhouse, W. A., Brunner, R., Cane, R., Clarke, T. E., ... Wood-Vasey, W. M. (2021). A Spitzer survey of Deep Drilling Fields to be targeted by the Vera C. Rubin Observatory Legacy Survey of Space and Time. Monthly Notices of the Royal Astronomical Society, 501(1), 892-910. https://doi.org/10.1093/mnras/staa3714
Ikegwu, K. M., Trauger, J., McMullin, J., & Brunner, R. J. (2020). PyIF: A Fast and Light Weight Implementation to Estimate Bivariate Transfer Entropy for Big Data. In IEEE SoutheastCon 2020, SoutheastCon 2020 Article 9249650 (Conference Proceedings - IEEE SOUTHEASTCON; Vol. 2020-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SoutheastCon44009.2020.9249650