Computational Neural and Information Eng. Lab.

Austin J. Brockmeier

My research interests include data science, machine learning, signal processing, and the underlying mathematics and design of statistical models, optimizations, and algorithms. I have experience interfacing with complex data from the domains of biomedical engineering, neuroscience, and natural language processing.

I am an assistant professor in the Department of Electrical and Computer Engineering (ECE) and the Department of Computer and Information Sciences (CIS) within the College of Engineering at the University of Delaware (UD), and a resident faculty in UD's Data Science Institute.

Contact:

306 Evans Hall
139 The Green
Newark, DE 19716
ajbrock @udel.edu

Google Scholar Profile

Select research outputs:

Quantifying the informativeness of similarity measurements, Journal of Machine Learning Research, 2017.

Learning shift-invariant waveforms to describe EEGs, IEEE Transactions on Biomedical Engineering, 2016.

Kernel-based metric learning for neural decoding, Neural Computation, 2014.

Current Members:

Past Visitors:

Prospective students:

The lab is open to both graduate and motivated undergraduate students. The project focus should include machine learning (theory, algorithms, and computation), data science (statistical analysis, information retrieval, text mining, and visualization), and/or signal processing (biomedical signals, audio, and remote sensing).

Students should have experience in mathematical modeling or analysis, numerical programming, and statistics and have an interest in the theoretical, computational, or algorithmic research aspects.

Students could also aim for the data science triad of statistics, computation, and domain expertise. These students should have a background in an analytical science, engineering, or medicine and have an interest in using machine learning, signal processing, and data science to pursue a scientific or engineering research project. There are opportunities to co-advise students interested robotics or neuroscience. For the latter, The lab is also interested in developing new techniques for processing and analyzying data from electrophysiology of either the central or peripheral nervous system (EEG, EMG, ECoG), and other brain imaging technology (fMRI, NIRS).

Information on admission and funding.

Background:

Austin J. Brockmeier Curriculum vitae

I was a research fellow at the National Centre for Text Mining within the School of Computer Science at the University of Manchester, UK. My mentor was Prof. Sophia Ananiadou. Before that I was at the University of Liverpool, where my mentor was Prof. John (Yannis) Goulermas. At both universities, I contributed to a project funded by the UK's Medical Research Council that developed techniques that can assist the literature screening stage of systematic reviews of literature, with public health as the main focus.

I received a Ph.D. in Electrical and Computer Engineering from the University of Florida. My advisor was Prof. Jose C. Principe. As a Ph.D. student I developed algorithms for extracting relevant patterns from signals and applied them to the analysis of multiscale neural data, both action potential timings (spike trains) and electrical potentials (local field potential and EEG recordings). We collaborated with researchers on sensory processing and brain machine interfacing.

I received a B.S. degree in computer engineering from the University of Nebraska–Lincoln (UNL) at its Omaha campus, having received the Walter Scott, Jr. Scholarship and the University of Nebraska Regents Scholarship. In Omaha, UNL's engineering departments are housed within the Peter Kiewit Institute, while my second major in mathematics and minor in computer science are from the respective departments at the University of Nebraska at Omaha.

I graduated from Cozad High School, and was honored to have received the Rick Wilson Undergraduate Scholarship. Among many activities, one of the most formative was my participation in the Nebraska Academic Decathlon.