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. Preprint
Bilal Riaz, M.S., Ph.D.
Ph.D. alumnus and M.S. alumnus
Applications of computational optimal transport in machine learning and signal
processing
Optimal Transport with Subset Selection
Hassan Baker, M.S., Ph.D.
Ph.D. alumnus
Improving Learning under Data Scarcity Constraints: Application in Brain MRI, Sonar, and Natural Images
Papers:
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 Preprint. Tensor decompositions.
Austin J. Meek
Ph.D. student
Human-AI alignment including brain-AI alignment. Measuring Chain-of-Thought Monitorability through Faithfulness and Verbosity Domain adaptation for EEG
Zhi Li
Ph.D. student (co-advised by Prof. Javier Garcia-Frias)
Research focus: disentangling vision-language vector-spaces for interpretable image retrieval Paper
Past Members
Yalin Liao, Ph.D.
Ph.D. alumnus
Statistical Divergences and Density Estimation for Anomaly Detection and Generative Modeling. Preprint
Current position: Accepted a post-doctoral research position at Moffitt Cancer Center, Tampa, Florida.
Yuksel Karahan, M.S., Ph.D.
Ph.D. alumnus
Detecting distributional discrepancies using kernel landmarks
Kristina Holton, Ph.D.
Ph.D. alumna
Exploring early stage psychosis through multimodal approaches: a longitudinal study
Dissertation involved three modalities: Resting state functional connectivity Auditory evoked response Cortical thickness
Current positions: Bioinformatician, Harvard Department of Stem Cell and Regenerative Biology. Instructor, Brandeis University.
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
Papers:
- Bag of Waveforms for Independent Component Labeling
- Shift-invariant waveform learning on epileptic ECoG
- Searching for waveforms on spatially-filtered epileptic ECoG 10.1109/NER49283.2021.9441220 Current position: Appliled Scientist, Twitch Interactive, Inc.
Eric Mans
ECE REU (Summer 2025)
Predicting the Spatial Origin of EEG Independent
Components from their Spectral-Temporal Features Poster
Isabel Cano Achuri, M.S.
Visiting Scholar (Summer and Fall 2023)
Predicting genotypes from bag-of-waveforms as phenotypes in mouse models of epilepsy. Preprint
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
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”
Hau Phan, M.S.
M.S. alumnus
Machine learning for underwater chemical source localization. Reinforcement learning for estimation and detection. Paper
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.