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Robert Brunner

Professor

Primary Affiliation

Illinois Language and Literacy Initiative

Affiliations

Status Affiliate Faculty

Home Department of Accountancy

Phone 244-6099

Email bigdog@illinois.edu

Address

  • Biography

    Robert Brunner is a professor in the Department of Astronomy and Accountancy. He is also an affiliate faculty member of the Image Formation and Processing Group at Beckman Institute. Robert Brunner completed a postdoctoral position at the California Institute of technology. Robert Brunner is now focused on the nascent field of data science and is leading new educational opportunities in data science as well as working with companies as the data science expert in residence at the University of Illinois Research Park.

    Education

    • Ph.D., John Hopkins University

  • Honors

    • 2021: ACM Special Interest Group on the Management of Data (SIGMOD) Systems Award, SIGMOD

  • Research

    Research Interests:

    • Observational Cosmology

    • Information Science

    • 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.

  • 2023

    • J. Kaikaus, H. Li and R. J. Brunner, "Humans vs. ChatGPT: Evaluating Annotation Methods for Financial Corpora," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 2831-2838, doi: 10.1109/BigData59044.2023.10386425. keywords: {Analytical models;Annotations;Big Data;Chatbots;Data models;Reliability;Task analysis;large language models;earnings calls;emotion recognition;sentiment analysis},

    2022

    • E. Bracht, V. Kindratenko and R. J. Brunner, "Sparse Spatio-Temporal Neural Network for Large-Scale Forecasting," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 1-5, doi: 10.1109/BigData55660.2022.10036330. keywords: {Training;Temperature distribution;Sea surface;Runtime;Neural networks;Big Data;Predictive models},
    • J. Kaikaus, J. L. Hobson and R. J. Brunner, "Truth or Fiction: Multimodal Learning Applied to Earnings Calls," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 3607-3612, doi: 10.1109/BigData55660.2022.10020307. keywords: {Industries;Semantics;Companies;Predictive models;Big Data;Feature extraction;Data models;multimodal learning;natural language processing;speech processing;earnings calls;market movement},

    2021

    • ---. “DeepDrill SHARK Simulated Lightcone Catalog.” NASA IPAC DataSet, 2021, ui.adsabs.harvard.edu/abs/2021ipac.data.I501L/abstract, https://doi.org/10.26131/IRSA501. Accessed 16 Mar. 2025.
    • ---. “DeepDrill XMM-LSS 2-Band Catalog.” NASA IPAC DataSet, 2021, ui.adsabs.harvard.edu/abs/2021ipac.data.I500L/abstract, https://doi.org/10.26131/IRSA500. Accessed 16 Mar. 2025.
    • ---. “Spitzer Survey of Deep Drilling Fields.” NASA IPAC DataSet, 2021, ui.adsabs.harvard.edu/abs/2021ipac.data.I491L/abstract, https://doi.org/10.26131/IRSA491. Accessed 16 Mar. 2025.
    • “DeepDrill ELAIS S1 4.5 Micron Catalog.” NASA IPAC DataSet, 2021, ui.adsabs.harvard.edu/abs/2021ipac.data.I496L/abstract, https://doi.org/10.26131/IRSA496. Accessed 16 Mar. 2025.
    • M Lacy, J A Surace, D Farrah, K Nyland, J Afonso, W N Brandt, D L Clements, C D P Lagos, C Maraston, J Pforr, A Sajina, M Sako, M Vaccari, G Wilson, D R Ballantyne, W A Barkhouse, R Brunner, R Cane, T E Clarke, M Cooper, A Cooray, G Covone, C D’Andrea, A E Evrard, H C Ferguson, J Frieman, V Gonzalez-Perez, R Gupta, E Hatziminaoglou, J Huang, P Jagannathan, M J Jarvis, K M Jones, A Kimball, C Lidman, L Lubin, L Marchetti, P Martini, R G McMahon, S Mei, H Messias, E J Murphy, J A Newman, R Nichol, R P Norris, S Oliver, I Perez-Fournon, W M Peters, M Pierre, E Polisensky, G T Richards, S E Ridgway, H J A Röttgering, N Seymour, R Shirley, R Somerville, M A Strauss, N Suntzeff, P A Thorman, E van Kampen, A Verma, R Wechsler, W M Wood-Vasey, 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, Volume 501, Issue 1, February 2021, Pages 892–910, https://doi.org/10.1093/mnras/staa3714
    • M.;Surace, Lacy,. “DeepDrill CDFS 2-Band Catalog.” NASA IPAC DataSet, 2021, ui.adsabs.harvard.edu/abs/2021ipac.data.I494L/abstract, https://doi.org/10.26131/IRSA494. Accessed 16 Mar. 2025.

    2020

    • K. M. Ikegwu, J. Trauger, J. McMullin and R. J. Brunner, "PyIF: A Fast and Light Weight Implementation to Estimate Bivariate Transfer Entropy for Big Data," 2020 SoutheastCon, Raleigh, NC, USA, 2020, pp. 1-6, doi: 10.1109/SoutheastCon44009.2020.9249650. keywords: {Social networking (online);Graphics processing units;Big Data;Systems neuroscience;Entropy;Time measurement;Software development management;Transfer Entropy;Parallel Processing},
    • S.;Shi, Thomas. “The 1st Agriculture-Vision Challenge: Methods and Results.” ArXiv E-Prints, Apr. 2020, p. arXiv:2004.09754, ui.adsabs.harvard.edu/abs/2020arXiv200409754T/abstract, https://doi.org/10.48550/arXiv.2004.09754. Accessed 16 Mar. 2025.

    2019

    • Anand, Vic and Brunner, Robert and Ikegwu, Kelechi and Sougiannis, Theodore, Predicting Profitability Using Machine Learning (October 8, 2019). Available at SSRN: https://ssrn.com/abstract=3466478 or http://dx.doi.org/10.2139/ssrn.3466478
    • S. Hariri, M. C. Kind and R. J. Brunner, "Extended Isolation Forest," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1479-1489, 1 April 2021, doi: 10.1109/TKDE.2019.2947676. keywords: {Forestry;Vegetation;Distributed databases;Anomaly detection;Standards;Clustering algorithms;Heating systems;Anomaly detection;isolation forest},
    • Sun, Hao, et al. “Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder.” ArXiv.org, 2019, arxiv.org/abs/1910.14056. Accessed 16 Mar. 2025.

    2018

    • Brunner, Robert J. “The Data Science Handbook. Field Cady. Hoboken, NJ: John Wiley and Sons, Inc., 2017. 416 Pp. $59.95 (Hardcover). (ISBN 9781119092940).” Journal of the Association for Information Science and Technology, vol. 69, no. 6, 7 Feb. 2018, pp. 861–863, https://doi.org/10.1002/asi.23942. Accessed 12 July 2020.
    • I Sevilla-Noarbe, B Hoyle, M J Marchã, M T Soumagnac, K Bechtol, A Drlica-Wagner, F Abdalla, J Aleksic, C Avestruz, E Balbinot, M Banerji, E Bertin, C Bonnett, R Brunner, M Carrasco-Kind, A Choi, T Giannantonio, E Kim, O Lahav, B Moraes, B Nord, A J Ross, E S Rykoff, B Santiago, E Sheldon, K Wei, W Wester, B Yanny, T Abbott, S Allam, D Brooks, A Carnero-Rosell, J Carretero, C Cunha, L da Costa, C Davis, J de Vicente, S Desai, P Doel, E Fernandez, B Flaugher, J Frieman, J Garcia-Bellido, E Gaztanaga, D Gruen, R Gruendl, J Gschwend, G Gutierrez, D L Hollowood, K Honscheid, D James, T Jeltema, D Kirk, E Krause, K Kuehn, T S Li, M Lima, M A G Maia, M March, R G McMahon, F Menanteau, R Miquel, R L C Ogando, A A Plazas, E Sanchez, V Scarpine, R Schindler, M Schubnell, M Smith, R C Smith, M Soares-Santos, F Sobreira, E Suchyta, M E C Swanson, G Tarle, D Thomas, D L Tucker, A R Walker, (The DES Collaboration), Star–galaxy classification in the Dark Energy Survey Y1 data set, Monthly Notices of the Royal Astronomical Society, Volume 481, Issue 4, December 2018, Pages 5451–5469, https://doi.org/10.1093/mnras/sty2579
    • William Biscarri, Sihai Dave Zhao, Robert J. Brunner, A simple and fast method for computing the Poisson binomial distribution function, Computational Statistics & Data Analysis, Volume 122, 2018, Pages 92-100, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2018.01.007. (https://www.sciencedirect.com/science/article/pii/S0167947318300082)

    2017

    • W. Yu, M. Carrasco Kind, R.J. Brunner, Vizic: A Jupyter-based interactive visualization tool for astronomical catalogs, Astronomy and Computing, Volume 20, 2017, Pages 128-139, ISSN 2213-1337, https://doi.org/10.1016/j.ascom.2017.06.004. (https://www.sciencedirect.com/science/article/pii/S2213133716301500)
    • Weixiang;Carrasco, Yu,. “A Jupyter-Based Interactive Visualization Tool for Astronomical Catalogs.” American Astronomical Society Meeting Abstracts #229, vol. 229, 2017, p. 438.02, ui.adsabs.harvard.edu/abs/2017AAS...22943802Y/abstract. Accessed 16 Mar. 2025.

    2016

    • Collaboration, D. E. S., Abbott, T., Abdalla, F. B., Aleksic, J., Allam, S., Amara, A., and Zuntz, J. (2016). The Dark Energy Survey: more than dark energy-an overview. Monthly Notices of the Royal Astronomical Society (MNRAS), 460, 1270-1299.
    • Dark Energy Survey Collaboration:, T. Abbott, F. B. Abdalla, J. Aleksic, S. Allam, A. Amara, D. Bacon, E. Balbinot, M. Banerji, K. Bechtol, A. Benoit-Lévy, G. M. Bernstein, E. Bertin, J. Blazek, C. Bonnett, S. Bridle, D. Brooks, R. J. Brunner, E. Buckley-Geer, D. L. Burke, G. B. Caminha, D. Capozzi, J. Carlsen, A. Carnero-Rosell, M. Carollo, M. Carrasco-Kind, J. Carretero, F. J. Castander, L. Clerkin, T. Collett, C. Conselice, M. Crocce, C. E. Cunha, C. B. D'Andrea, L. N. da Costa, T. M. Davis, S. Desai, H. T. Diehl, J. P. Dietrich, S. Dodelson, P. Doel, A. Drlica-Wagner, J. Estrada, J. Etherington, A. E. Evrard, J. Fabbri, D. A. Finley, B. Flaugher, R. J. Foley, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztanaga, D. W. Gerdes, T. Giannantonio, D. A. Goldstein, D. Gruen, R. A. Gruendl, P. Guarnieri, G. Gutierrez, W. Hartley, K. Honscheid, B. Jain, D. J. James, T. Jeltema, S. Jouvel, R. Kessler, A. King, D. Kirk, R. Kron, K. Kuehn, N. Kuropatkin, O. Lahav, T. S. Li, M. Lima, H. Lin, M. A. G. Maia, M. Makler, M. Manera, C. Maraston, J. L. Marshall, P. Martini, R. G. McMahon, P. Melchior, A. Merson, C. J. Miller, R. Miquel, J. J. Mohr, X. Morice-Atkinson, K. Naidoo, E. Neilsen, R. C. Nichol, B. Nord, R. Ogando, F. Ostrovski, A. Palmese, A. Papadopoulos, H. V. Peiris, J. Peoples, W. J. Percival, A. A. Plazas, S. L. Reed, A. Refregier, A. K. Romer, A. Roodman, A. Ross, E. Rozo, E. S. Rykoff, I. Sadeh, M. Sako, C. Sánchez, E. Sanchez, B. Santiago, V. Scarpine, M. Schubnell, I. Sevilla-Noarbe, E. Sheldon, M. Smith, R. C. Smith, M. Soares-Santos, F. Sobreira, M. Soumagnac, E. Suchyta, M. Sullivan, M. Swanson, G. Tarle, J. Thaler, D. Thomas, R. C. Thomas, D. Tucker, J. D. Vieira, V. Vikram, A. R. Walker, R. H. Wechsler, J. Weller, W. Wester, L. Whiteway, H. Wilcox, B. Yanny, Y. Zhang, J. Zuntz, The Dark Energy Survey: more than dark energy – an overview, Monthly Notices of the Royal Astronomical Society, Volume 460, Issue 2, 01 August 2016, Pages 1270–1299, https://doi.org/10.1093/mnras/stw641
    • Edward J. Kim, Robert J. Brunner, Star–galaxy classification using deep convolutional neural networks, Monthly Notices of the Royal Astronomical Society, Volume 464, Issue 4, February 2017, Pages 4463–4475, https://doi.org/10.1093/mnras/stw2672
    • Edward J. Kim, Robert J. Brunner, Star–galaxy classification using deep convolutional neural networks, Monthly Notices of the Royal Astronomical Society, Volume 464, Issue 4, February 2017, Pages 4463–4475, https://doi.org/10.1093/mnras/stw2672
    • Harshil M. Kamdar, Matthew J. Turk, Robert J. Brunner, Machine learning and cosmological simulations – I. Semi-analytical models, Monthly Notices of the Royal Astronomical Society, Volume 455, Issue 1, 01 January 2016, Pages 642–658, https://doi.org/10.1093/mnras/stv2310
    • Harshil M. Kamdar, Matthew J. Turk, Robert J. Brunner, Machine learning and cosmological simulations – II. Hydrodynamical simulations, Monthly Notices of the Royal Astronomical Society, Volume 457, Issue 2, 01 April 2016, Pages 1162–1179, https://doi.org/10.1093/mnras/stv2981
    • J. Asorey, M. Carrasco Kind, I. Sevilla-Noarbe, R. J. Brunner, J. Thaler, Galaxy clustering with photometric surveys using PDF redshift information, Monthly Notices of the Royal Astronomical Society, Volume 459, Issue 2, 21 June 2016, Pages 1293–1309, https://doi.org/10.1093/mnras/stw721
    • Robert J. Brunner, Edward J. Kim, Teaching Data Science, Procedia Computer Science, Volume 80, 2016, Pages 1947-1956, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2016.05.513.(https://www.sciencedirect.com/science/article/pii/S1877050916310006)