Contact Information
226 Astronomy
1002 West Green Street
M/C 221
Urbana, IL 61801
Biography
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.
Research Interests
Observational Cosmology
Information Science
Statistical and Machine Learning
Advanced Computational Techniques
Transient and Variable Phenomena
Research Description
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.
Education
Ph.D. Johns Hopkins University
Additional Campus Affiliations
Chief Disruption Officer, Gies College of Business
Arthur Andersen Faculty Fellow, Accountancy
Professor, Accountancy
Professor, Siebel School of Computing and Data Science
Professor, Electrical and Computer Engineering
Professor, Statistics
Professor, Astronomy
Professor, Informatics
Professor, National Center for Supercomputing Applications (NCSA)
Professor, Beckman Institute for Advanced Science and Technology
External Links
Recent Publications
Bracht, E., Brunner, R., & McMullin, J. (2025). Towards a unified approach to industry recovery: Insights from intraday stock data and advanced community detection methods. Physica A: Statistical Mechanics and its Applications, 669, Article 130501. https://doi.org/10.1016/j.physa.2025.130501
Kaikaus, J., Li, H., & Brunner, R. J. (2023). Humans vs. ChatGPT: Evaluating Annotation Methods for Financial Corpora. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 2831-2838). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData59044.2023.10386425
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