CV

• Doctoral-track Candidate in Psychology (09/2023–10/2025)
• Project: Structural and Functional Neural Mechanisms of Theory of Mind Development
• M.Sc. in Psychological Research, Awarded With Merit (11/2025)
• Dissertation: Studying the distinct functions of theory of mind brain regions during movie-viewing
• UKRI ESRC DTP Studentship (09/2022–10/2025)

• B.A. in Computer Science (via HMC) and Neuroscience (via Keck Science), Awarded Cum Laude (05/2021)
• Thesis: Uncovering Object Categories in Infant Views
• Scripps College Humanities Institute Fellowship (01/2021)
• Scripps College Success Grant Scholarship (08/2020, 08/2021)
• Stanford University NSF REU Scholarship at Center for the Study of Language and Information (02/2020)
• University of Minnesota NSF REU Scholarship in Neuroimaging for Cognitive Neuroscience (05/2019)

• Designing evaluation rubrics for frontier language models informed by developmental cognition research, targeting reasoning and coding tasks where model failure modes parallel challenges in human cognitive development.
• Identifying systematic alignment gaps by comparing model judgments across ambiguous prompts, using experimental design methods to isolate sources of inconsistency in reproducibility and generalization.

• Built scalable MRI analysis infrastructure for child development research, including reproducible Python pipelines and anonymization workflows for sensitive participant data.
• Led workshops and hackathons to align researchers, clinicians, and technical contributors on shared data processing, organization, and analysis standards across concurrent projects.

• Designed and scaled a program for researchers across 3 departments to review code for publication-ready projects.
• Advised researchers on open science practices at all stages of the research lifecycle, hosting workshops and 1:1 consultations on data governance, preregistration, and reproducible research design.

• Owned regulatory compliance and data protection protocols, budgeting, and technical infrastructure for research on adolescent decision-making, reinforcement learning, and mental health risk factors.
• Built and maintained end-to-end data pipelines for multimodal behavioral and neuroimaging data, including online experiments, eye-tracking, and MRI.
• Managed participant pipelines across partner schools and organizations to support safe, informed research participation.

• Engineered a participant database system with custom Python tooling for all lab participants, improving scalability and coordinating overhead in human-subjects research operations.

• Built computer vision pipelines for infant egocentric video using Detectron2 models to characterize early visual experience and relate naturalistic input to object and word learning.

• Engineered human-in-the-loop annotation workflows with AWS tooling to produce evaluation-ready labeled datasets from infants' home-environment video data.

• Streamlined integration of child development datasets from 12+ research labs into a shared open-access repository, enabling large-scale cross-study analyses of early language and attention.

• Implemented supervised machine learning models to successfully predict perceived color using functional MRI data.

• Designed and delivered two interdisciplinary lectures bridging expertise in cognitive neuroscience and AI for advanced secondary-school students.
• Brain-Inspired Artificial Intelligence (slides): led discussion-based lesson on the neurobiological visual system (covering receptive fields, Hubel & Wiesel, ventral pathway), and mapped those mechanisms to the history of neural networks (i.e., McCulloch–Pitt threshold units, Hebbian learning, Rosenblatt/multi-layer perceptrons, and convolution).
• Applications of AI: Cognitive Science (slides): taught interdisciplinary foundations of cognitive science (e.g., brain versus mind), introduced functional MRI methodologies, and connected neuroimaging data to machine learning tools, such as multivoxel pattern analysis and support vector machines.

• Data Analysis for Psychology in R 1 (PSYL08013): Mentored students through group-based pair programming assignments, teaching fundamentals of univariate statistics and R programming.
• Data Analysis for Psychology in R 3 (PSYL10168): Mentored students through fundamental questions relating to multivariate statistics and R programming.

• Computability and Logic (CSCI-081): Led weekly sessions and tutored 20 students to clarify topics relating to computability theory, set countability, formal logics (predicate, propositional, etc.), Chomsky grammars and languages, Turing machines, and automata; continued teaching virtually during pandemic.

• Calculus II w/ Applications to Science (MATH-031S): Led twice-weekly sessions and tutored 30 students to help understand series and sequences, integration, differential equations, and probabilistic applications using examples in the science.

• Designed a 201-item benchmark spanning 12 Theory of Mind dimensions grounded in developmental psychology, mapping each item to empirically established human age norms.
• Evaluated 53 models across Claude, GPT, Llama, Qwen, and Mistral families (~10,000 observations) using dual analytic pipelines: mastery-based age mapping and IRT/GLMM model-based estimation with task-format covariates.
• Revealed uneven cognitive profiles across frontier model families, enabling age-equivalent comparison of Theory of Mind performance rather than aggregate benchmark scoring alone.

GitHub Repository ↗

• Built and deployed a React-based full-stack web and iOS application enabling family groups to set, track, and share daily and weekly health goals; integrated with the iOS Health API for automated step and activity syncing; includes password-protected group accounts and custom goal assignment.

• Building a browser-based implementation of the validated ToM Booklet task (Sotomayor-Enriquez et al., 2023), with audio narration, animated stimuli, and automated response capture to enable large-scale online data collection.

• Jointly embedded mobile phone and eye-tracking data in a low-dimensional manifold space to classify attentional engagement; demonstrated improved classification accuracy over baseline feature-space methods.

• Built a React Native application that classifies user-uploaded wildlife images, returning predicted genus, species, and IUCN conservation status via a trained computer vision model.

• Elected by 30+ postgraduate researchers to represent student interests in faculty board meetings and university-level committees; successfully advocated to preserve community funding amidst university-wide budget cuts.

• Designed and presented an invited workshop on computational modeling for cognitive neuroscience for attendees at an international conference.

• Founded and led a program serving underrepresented high school students across Los Angeles and the Inland Empire.

• Supported community-building and logistics for workshop attendees within the broader NeurIPS research community.