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)

• Evaluating frontier language models on reasoning and coding tasks, designing and using rubrics to identify failure modes.
• Comparing model judgments across ambiguous prompts to surface alignment gaps, especially regarding reproducibility.

• Designed analyses for studying theory of mind development in children aged 5 to 12, integrating structural and functional MRI with behavioral outcome measurements.
• Built reproducible Python-based analysis pipelines for processing and analyzing functional and diffusion MRI data, paying special attention to anonymization to protect data from child participants.
• Led workshops and hackathons to align researchers, clinicians, and technical contributors across concurrent projects, teaching data processing, organization, and analysis for studying cognitive development.

• 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.

• Managed IRB/ethics compliance, budgeting, and codebase for high-impact research on adolescent decision-making, reinforcement learning, and mental health risk factors.
• Assured high quality collection, processing, and analysis workflows for multimodal data, including online experiments, eye-tracking, and functional MRI.
• Led school outreach and participant recruitment with NYC public and private schools, coordinating with educators and caregivers to support safe, informed participation of minors.

• 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.

• 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.

• Built human-in-the-loop scalable annotation workflows with AWS tooling to label egocentric videos collected in infants’ home environments.

• 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) with Dr. Umberto Noe, Autumn 2023 – Spring 2025
• Psychology 1A/B with Dr. Hannah Cornish, Autumn 2023 – Spring 2024
• Data Analysis for Psychology in R 3 (PSYL10168) with Dr. Umberto Noe, Autumn 2023

• Computability and Logic (CSCI-081) with Dr. George Montañez

• Calculus II w/ Applications to Science (MATH-031S) with Dr. Blerta Shtylla

• 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.

[1] Nussenbaum, K., Martin, R. E., Maulhardt, S., Yang, Y., Bizzell-Hatcher, G., Bhatt, N. S., Scheuplein, M., Rosenbaum, G. M., O’Doherty, J. P., Cockburn, J., & Hartley, C. A. (2023). Novelty and uncertainty differentially drive exploration across development. eLife.

[2] Zettersten, M., …, Bhatt, N. S., Bergey, C. A., & Frank, M. C. (2022). Peekbank: Exploring children’s word recognition through an open, large-scale repository for developmental eye-tracking data. Behavior Research Methods.

[3] Long, B., Kachergis, G., Bhatt, N. S., & Frank, M. C. (2021). Characterizing the object categories two children see and interact with in a dense dataset of naturalistic visual experience. Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

[4] Bhatt, N. S., Tregillus, K. E. M., & Engel, S. A. (2019). Classification Analyses of fMRI Data Predict Perceived Color. Poster presented at Southern California Conference for Undergraduate Research (SCCUR) and Bay Area Vision Research Day (BAVRD).

• 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.