Research Assistant · Phelps Lab · Harvard
I do research at the intersection of computational psychiatry, cognitive neuroscience , and computational modeling. Currently an RA at the Phelps Lab (Harvard), where I work with clinical imaging data and build computational pipelines to study how brain connectivity tracks transdiagnostic psychiatric symptoms. Previously trained at Harvard’s Kempner Institute.
Updates
PosterPresented at CPConf 2026 — “Resting-State Network Connectivity and Transdiagnostic Psychosis-like Symptoms: A CPM Approach.”
Joined the Phelps Lab at Harvard as a Research Assistant — memory, emotion, and clinical fMRI.
Completed the Kempner Institute Post-Baccalaureate Program — a two-year, master’s-equivalent research experience in computational neuroscience.
Released rsFC-Predictive-Pipeline on GitHub — an open CPM pipeline for resting-state fMRI.
CCNPoster at Conference on Cognitive Computational Neuroscience — DNN–brain alignment in vision models.
Publications & Projects
CPConf 2026 · Poster
Used a 5-fold CPM pipeline with ridge regression on 7-network resting-state connectivity to predict BPRS-positive symptoms across bipolar disorder, schizophrenia, and ADHD — supporting an RDoC-style dimensional view of psychosis.
CCN 2025 · Poster
Investigated alignment between deep neural network representations and human brain responses using RSA. Explored how architecture and training objectives shape alignment across the visual cortex hierarchy.
GitHub · Open Source
An open-source CPM pipeline for resting-state fMRI: within-fold edge selection, multiple regularized regression models (ridge, elastic net, PCR), and out-of-sample evaluation on clinical symptom dimensions.
Science Communication
I write about computational psychiatry, brain methods, and navigating science as a first-gen Latina researcher. Honest, accessible, always curious.
Methods Explained
Breaking down Connectome-based Predictive Modeling: how brain connectivity fingerprints can predict behavior, and what it means to test that transdiagnostically.
Big Ideas
The DSM groups people by symptoms. RDoC groups them by circuits. Here’s why that shift matters — and where the field still falls short.
Personal
What no one tells you about building a research identity when you don’t see yourself in the field — and why I started writing about it anyway.
Background
I’m a researcher with a background in Neurobiology and a deep interest in how the brain implements emotion, memory, and the features that go awry in psychiatric illness. My current work at the Phelps Lab gives me the chance to work with clinical fMRI data while building toward a PhD in computational psychiatry.
I spent two years at Harvard’s Kempner Institute training in computational cognitive neuroscience — taking graduate courses, running independent projects in DNN-to-brain alignment, and learning the methods that now anchor my research identity: connectome-based predictive modeling, cross-validation design, regularized regression, and representational similarity analysis.
I care about making science accessible — especially for curious young women who don’t see themselves reflected in technical research spaces. This site is part of that effort: a place to share methods in plain language, document the journey honestly, and connect with people asking similar questions.
When I’m not in the lab, I’m reading the latest fantasy novel, working out, or spending time with friends.
Experience
Research Assistant
Phelps Lab · Harvard University
Clinical fMRI data, memory & emotion research, and independent computational psychiatry projects.
Post-Baccalaureate Researcher
Kempner Institute · Harvard University
Graduate-level coursework and independent projects in DNN-brain alignment and computational cognitive neuroscience.
Poster Presenter
CCN 2025 · Conference on Cognitive Computational Neuroscience
DNN–brain alignment in vision models.
Participant
Neuromatch Academy
Computational neuroscience curriculum — deep learning, linear models, neural data analysis.