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PhD Electrical and Computer Engineering
Thesis Advisor: John Pearson\
Thesis Title: Computational modeling for modern neuroethology\
M.Sc. Electrical & Computer Engineering\
Certificate in Cognitive Neuroscience\
Certificate in College Teaching
Sc.B. Cognitive Neuroscience\ Thesis Advisor: Joo-Hyun Song\ Thesis Title: The Effects of Observation on Visuomotor Adaptation
January 2026: Cosyne Presenters Travel Grant\ May 2024: Outstanding Graduate Teaching Assistant, Probabilistic Machine Learning\ June 2023: Ruth L. Kirschstein Predoctoral Individual National Research Service Award (F31)\ May 2020: Muriel Fain Sher Premium in Psychology\ May 2020,2019: Research at Brown Award\ August 2019: NINDS Exceptional Student Award
J. Qi, D. C. Schreiner, M. Martinez, J. Pearson, and R. Mooney, “Dual neuromodulatory dynamics underlie birdsong learning,” Nature, vol. 641, pp. 690–698, 2025.
L. M. Koponen, M. Martinez, E. Wood, et al., “Transcranial magnetic stimulation input-output curve slope differences suggest variation in recruitment across muscle representations in primary motor cortex,” Frontiers in Human Neuroscience, vol. 18, 2024.
T. S. L. Wang, M. Martinez, E. K. Festa, W. C. Heindel, and J. H. Song, “Age-related enhancement in visuomotor learning by a dual-task,” Scientific Reports, vol. 12, 1 2022.
M. Martinez and J. Pearson, “Reproducible, incremental representation learning with the Rosetta VAE,” in Bayesian Deep Learning Workshop, NeurIPS 2021, 2021.
M. Martinez and J. Pearson, “Flexible modeling of animal vocal communication,” [in prep].
M. Martinez and A. H. Williams, “Quasi-monte carlo methods enable extremely low-dimensional deep generative models,” [under review].
M. Martinez and J. Pearson, “Inferring structure in acoustic variability,” in Duke Department of Neurobiology Research in Progress Seminar Series, [talk], 2025.
M. Martinez, J. Qi, R. Mooney, and J. Pearson, “Song variability coding in the zebra finch basal ganglia,” in Neural Mechanisms of Acoustic Communication GRC, [poster], 2024.
M. Martinez, S. Brudner, R. Mooney, and J. Pearson, “Moduling tutor-directed dynamics in zebra finch song learning,” in CoSyNe, [poster], 2022.
M. Martinez, J. Qi, R. Mooney, and J. Pearson, “Data-driven exploration of natural song learning in the juvenile zebra finch,” in Neural Mechanisms of Acoustic Communication GRC, [poster], 2022.
M. Martinez, I. Osuarah, D. S. Reich, I. S. M. Cortese, and G. Nair, “Atlas-free brain segmentation by Classification using DErivative-based Features (C-DEF) in profressive multifocal leukoencephalopathy,” in NINDS Awards Ceremony, [talk,poster], 2019.
M. Martinez, M. Broderick, A. Anderson, and E. Lalor, “Recent and distant semantic information make distinct contributions to processing of natural, ongoing speech,” in University of Rochester Center for Visual Science Research Symposium, [poster], 2018
CNRI: Python and Concepts (Fall 2021 -- Spring 2025)
- Designed Python for Psychology class aimed at students with no prior research or programming experience
- Co-taught weekly Python lectures, advised students on experiment programming projects
- Taught systems neuroscience conceptual lectures aimed at exposing students to research in songbirds
DIBS Methods Meetings (Spring 2021 -- Spring 2024)
- Taught workshops for Duke's Intitute of Brain Sciences on machine learning and statistical analyses, aimed at exposing cognitive neuroscientists to analysis techniques
- Organized and coordinated speakers and funding (Fall 2023)
Probabilistic Machine Learning (Spring 2024)
- Taught weekly workshop on topics covered in lecture, designed multiple workshops
- Organized and coordinated 7 graduate TAs in homework grading, office hours, and problem set support
- Awarded ECE Department Outstanding Graduate TA award
Vector Space Methods with Applications (Fall 2022)
- Coordinated weekly office hours, graded homeworks and tests
- Designed problem set around developing intuition for optimization through alternating projections
Pearson Lab (Fall 2024 --)
- Managed two undergraduate students in analyzing complex neural and behavioral data
- Taught data management and statistical analyses in multiple programming languages
Cognitive Neuroscience Research Internship: Mentoring (Spring 2022 -- Spring 2025)
- Mentored 8 undergraduate students with no prior research experience
- Introduced basic concepts in systems neuroscience, statistics, and data science
Coding Languages Python,PyTorch,Jax,R,MATLAB,C++
Computational expertise Generative models, representation learning, image processing, audio analysis, biophysical models, big data, high-dimensional datasets, data visualization