CV

EDUCATION

Duke University 2020-2026

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

Brown University 2016-2020

Sc.B. Cognitive Neuroscience
Thesis Advisor: Joo-Hyun Song
Thesis Title: The Effects of Observation on Visuomotor Adaptation

Honors, Awards, & Fellowships

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

Publications

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.

Under Review/In Prep

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].

Posters & Presentations

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

Teaching

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

Mentoring

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

Skills

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

*****

Computational neuroethologist from Berkeley, CA