Current Members
Austin J. Brockmeier, Ph.D.
Assistant Professor
Curriculum Vitae Google Scholar Email
What: Data Science, Machine Learning, and Signal Processing
These involve the underlying mathematical analysis, design of statistical models, and software implementation of data and signal processing (filtering and neural networks) as well as optimization routines.
Why: To extract actionable information from complex data, especially to understand and interface with the brain
Carlos H. Mendoza Cárdenas, M.S.
Ph.D. student
Research focus: finding patterns in neural time series through convolutional sparse analysis.
Goal: to discover physiologically meaningful waveforms in multi-day continuous epileptic electrocorticographic (ECoG) recordings that can be used to build interpretable features for seizure prediction.
Methods: interpretable machine learning, clustering and sparse coding for time series, supervised learning for neural data
C. Cesar Claros Olivares, M.S.
Ph.D. student and M.S. alumnus
Error-aware and vigilant machine learning.
3D convolutional neural networks for brain age prediction from structural measures. Optimal transport for color and style transfer.
Yuksel Karahan, M.S.
Ph.D. student
Data science techniques (divergence measures; kernel methods) for covariate shift detection and semisupervised domain transfer.
Bilal Riaz, M.S.
Ph.D. student
Unsupervised learning as optimizations with positive semidefinite matrices.
Hassan Baker, M.S.
Ph.D. student
Signals processing and machine learning for neural signals.
My goal is to use signal processing and machine learning techniques for neural discoveries and facilitates fetching information from the raw neural data.
Hau Phan, B.S.
M.S. and Ph.D. student
Machine learning for underwater chemical source localization. Reinforcement learning for estimation and detection.
Karen Fonseca, B.Sc.
Visiting Scholar (Summer 2022)
Contrastive learning representations for image segmentation/detection
Alex Mulrooney
Undergraduate Researcher (Summer Scholar 2022)
Neural engineering: brain-AI interfaces and algorithms
Past Members
Andres Nicolas Lopez, MSc.
Visiting Scholar (Summer 2021)
Statistician working on the error modeling for synergistic machine learning. LinkedIn
Justin Labombard
Undergraduate (Summer Scholar 2021)
Dictionary Learning on Epileptic ECoG Waveforms
Evan Curtin
Undergraduate (Summer Scholar 2021)
Non-Negative Matrix Factorization as Dictionary Learning for Audio Separation
Edwin Salcedo, M.Sc., M.B.A.
Visiting Scholar (Summer 2019)
Machine learning for semisupervised domain transfer.