Department of Computer Science
University of Texas at San Antonio
Areas: Visual analysis, machine learning, data management
Upcoming Spring 2014: Special Topics: Bioinformatics - A CS Perspective (Counts as an upper division CS elective.)
Research: My research focuses on modeling, visualization, analysis, and management of multimedia data sets in three major application areas: neuroinformatics, bioinformatics, and medical informatics. Our group is currently developing algorithms and tools for automated annotation and retrieval of EEG and other signals based on content, as well as tools to detect enrichment of co-occurrences of events and signal patterns.
This problem domain shares many characteristics with problems in bioinformatics and in the mining of medical information in general --- the significant variation among and within individuals, the variety and non-uniformity of information available for an individual under any particular conditions, and the unique and complex mosaic of environmental factors that might contribute to a particular response. We seek to design approaches that are applicable and accessible to researchers in a variety of areas. You can see more about our work and most recent projects at: Visualization and modeling laboratory (VML).
Teaching: My teaching focus has been on developing curriculum and supporting infrastructure that is relevant, effective, and sustainable. Starting in 2008, and with the support of colleagues and NSF, I created materials and techniques for CS 1173 Data Analysis and Visualization. This course, which is currently required of biology majors and civil engineers, is also included in the mathematics electives of the core curriculum. The course teaches students computing in the context of learning practical data analysis skills. The website for this course is: http://www.cs.utsa.edu/~cs1173
I am also actively involved in curriculum and program development in bioinformatics and am the UTSA PI on the NIH/NCI supported UTSA/UTHSCSA Cancer Bioinformatics Initiative. This effort brings together cancer researchers and quantitative scientists to further engage students in data-driven research in cancer and other diseases. The website for this effort is: http://www.bioinformatics-sa.org
Some recent publications
K. Kleifges, N. Bigdely-Shamlo, S. Kerick, and K. Robbins (2017). BLINKER: Automated extraction of ocular indices from EEG enabling large-scale analysis. Frontiers in Neuroscience: Neurotechnology. 03 February 2017 | https://doi.org/10.3389/fnins.2017.00012.
K. Ball, W. D. Hairson, P. Franaszczuk, and K. Robbins (2016 eversion). BLASST: Band limited atomic sampling with spectral tuning with applications to utility line noise filtering. IEEE Transactions on Biomedical Eng. DOI: 10.1109/TBME.2016.2632119.
N. Bigdely-Shamlo, J. Cockfield, S. Makeig, T. Rognon, C. LaValle, M. Miyakoshi, and K. Robbins (2016). Hierarchical Event Descriptors (HED): Semi-structured tagging for real-world events in large-scale EEG, Frontiers in Neuroinformatics doi: 10.3389/fninf.2016.00042.
N. Bigdely-Shamlo, S. Makeig, and K. Robbins (2016). Preparing laboratory and real-world EEG data for large-scale analysis: A containerized approach, Frontiers in Neuroinformatics 1 08 March 2016 | http://dx.doi.org/10.3389/fninf.2016.00007. PMID: 27014048, PMCID: PMC4782059.
K. Ball, N. Bigdely-Shamlo, T. Mullen, and K. Robbins (2016). PWC-ICA: A method for stationary ordered blind source separation with application to EEG. Computational Intelligence in Neuroscience. 2 June 2016 | doi: 10.1155/2016/9754813, PMID: 27340397. PMCID: PMC4909972.
K-M. Su, W. Hairston, and K. Robbins (2016). Adaptive thresholding and reweighting to improve domain transfer learning for unbalanced data with applications to EEG, ICMLA 2016 (Dec. 18-20, Anaheim, CA).
N. Bigdely-Shamlo, T. Mullen, C. Kothe, K.-M. Su, and K. Robbins (2015). The PREP pipeline: standardized preprocessing for large-scale EEG analysis, Frontiers in Neuroinformatics 18 June 2015 | http://dx.doi.org/10.3389/fninf.2015.00016.
J. Meng, L. M. Merino, K. Robbins, Y. Huang (2014). Classification of imperfectly time-locked image RSVP events with EEG device, Neuroinformatics, 12(2):261-275. PMID: 24037139.
V. Lawhern, S. Kerick, K. Robbins (2013). Detection of alpha spindling and frequency shifts using discounted AR models, BMC Neuroscience, 14:101 doi:10.1186/1471-2202-14-101, PMC3848457.
V. Lawhern, W. D. Hairston, and K. Robbins (2013). DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals. PLoS ONE, 8(4):e62944.