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
C. Cesar Claros Olivares, M.S.
Ph.D. student and M.S. alumnus
Injury risk prediction following concussion in student athletes, as featured in UDaily “A game-changing tool”
3D convolutional neural networks for brain age prediction from structural measures, as featured in UDaily “How old is your brain?"
Out-of-distribution detection in supervised models.
Bilal Riaz, M.S.
Ph.D. student and M.S. alumnus
Applications of computational optimal transport in machine learning and signal
processing
Hassan Baker, M.S.
Ph.D. student
Improving Learning under Data Scarcity Constraints: Application in Brain MRI and Sonar Imaging
Justin Labombard
Ph.D. student (co-advised by Prof. Ken Barner) and prior Undergraduate Researcher (Summer Scholar 2021)
Multiple-domain and multiple fidelity learning
Alex Mulrooney
Incoming Ph.D. student (co-advised with Dr. David Hong) and former Undergraduate Researcher (Summer Scholar 2022, 2023)
Brain-AI alignment. Tensor decompositions.
Austin J. Meek
Ph.D. student
Human-AI alignment including brain-AI alignment.
Zhi Li
Ph.D. student (co-advised by Prof. Javier Garcia-Frias)
Research focus: disentangling vision-language vector-spaces for interpretable image retrieval
Eric Mans
ECE REU (Summer 2025)
Pinpointing EEG Components Spatial Origin from their Spectral Content
Past Members
Yalin Liao, Ph.D.
Ph.D. Alumnus
Statistical Divergences and Density Estimation for Anomaly Detection and Generative Modeling.
Current position: Accepted a post-doctoral research position at Moffitt Cancer Center, Tampa, Florida.
Yuksel Karahan, M.S., Ph.D.
Ph.D. alumnus
Data science techniques (divergence measures; kernel methods) for covariate shift detection and semisupervised domain transfer. Detecting distributional discrepancies using kernel landmarks.
Isabel Cano Achuri, M.S.
Visiting Scholar (Summer and Fall 2023)
Predicting genotypes from bag-of-waveforms as phenotypes in mouse models of epilepsy
David Cardenas
Undergraduate Researcher
Summer research experience as part of the NSF funded Data Science Corps grant working on signal processing and machine learning for music genre classification
Vance Steele
ECE REU (Summer 2024)
Simultaneous localization and mapping with brain waves
Kristina Holton, Ph.D.
Ph.D. alumna
Exploring early stage psychosis through multimodal approaches: a longitudinal study
Current positions: Bioinformatician, Harvard Department of Stem Cell and Regenerative Biology. Instructor, Brandeis University.
Travis Deputy
ECE REU Alumnus (Summer 2023)
Brain-AI research: human-subject interfaces and adversarial data augmentation for visual perception. Summer research poster title “Effects of Targeted Pixilation on Image Classification Using a Custom Computer Vision Model”
Carlos H. Mendoza Cárdenas, M.S., Ph.D.
Ph.D. alumnus
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
Current position: Appliled Scientist, Twitch Interactive, Inc.
Hau Phan, M.S.
M.S. alumnus
Machine learning for underwater chemical source localization. Reinforcement learning for estimation and detection.
Current position: Machine Learning Engineer, Qlik
Karen Fonseca, B.Sc.
Visiting Scholar (Summer 2022)
Contrastive learning representations for image segmentation/detection
Andres Nicolas Lopez, MSc.
Visiting Scholar (Summer 2021)
Statistician working on the error modeling for synergistic machine learning. LinkedIn
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.