CV

Education

• M.Sc. in Psychological Research (Awarded With Merit), University of Edinburgh, 2022-2023
  • Advisor: Dr. Hilary Richardson
• B.A. in Computer Science and Neuroscience (Cum Laude), Scripps College, 2017-2021
  • Advisors: Dr. Tessa Solomon-Lane and Dr. Julie Medero

Research experience

Postgraduate Researcher, University of Edinburgh

• September 2022 - September 2025
• Supervisor: Dr. Hilary Richardson
  • Developed methods to investigate associations between behavioral data using a theory of mind task and neural data extracted from structural and functional MRI in 5- to 12-year old children
  • Methods & Skills: Python, Linux, R, fMRI (fMRIPrep, FSL), DTI (FSL, MRtrix3, ANTs)

Junior Research Associate, New York University

• May 2021 - August 2022
• Supervisor: Dr. Catherine A. Hartley
  • Characterized developmental changes in attentional strategies, social abilities, and mnemonic specificity as related to reward-driven decision-making around adolescence, while also managing lab operations (ethics, finances, codebase)
  • Methods & Skills: Python, HTML/CSS, Linux, jsPsych, Prolific, Qualtrics, Reinforcement Learning, Eye-Tracking, fMRI

Software Engineer (Part-Time), Stanford University

• June 2021 - October 2021
• Supervisor: Dr. Hyowon Gweon
  • Streamlined participant database retrieval and organization to integrate RedCap information with new scripts
  • Methods & Skills: Python, RedCap, SQL

Contributing Developer, Peekbank Project

• August 2020 - May 2021
• Supervisors: Dr. Martin Zettersten (Princeton), Dr. Michael C. Frank (Stanford)
  • Imported and standardized developmental eye-tracking data for a large-scale, multi-institution database, containing data from ~20 labs
  • Methods & Skills: R, Database Organization and Management

Undergraduate Thesis Researcher, Scripps College

• August 2020 - May 2021
• Supervisors: Dr. Bria Long (Stanford), Dr. Michael C. Frank (Stanford), Dr. John G. Milton (Scripps)
  • Crafted tools for recognizing objects in infant egocentric head-mounted camera video frames to train computer vision models and inform early word learning
  • Methods & Skills: Python, Tensorflow, Detectron2, R, Computer Vision, Statistical Modeling

Summer Research Intern, Stanford University

• June 2020 - August 2020
• Supervisors: Dr. Bria Long, Dr. Michael C. Frank
  • Participated in eight-week training through the Center for the Study of Language and Information NSF-funded Research for Undergraduates (REU) Summer Internship
  • Crowdsourced labels for categorizing objects in frames from infant egocentric and naturalistic videos to create and analyze an annotated dataset to characterize early infant visual experiences
  • Methods & Skills: Amazon Sagemaker, R, Python, Amazon Mechanical Turk, Javascript

Summer Research Intern, University of Minnesota

• May 2019 - August 2019
• Supervisors: Dr. Katherine E.M. Tregillus, Dr. Stephen A. Engel
  • Participated in ten-week intensive training in MRI and EEG neuroimaging through the Cognitive Neuroscience NSF-funded Research for Undergraduates (REU) Program
  • Evaluated classifiers using fMRI patterns of activation to predict perceived color and uncover plasticity of illusory color perception from the McCullough Effect illusion
  • Methods & Skills: MATLAB, fMRI, Linux, Supervised Machine Learning

Publications and Presentations

Jiménez-Sánchez, L., Thye, M., Abel, S., Bhatt, N. S., Bonich, C., Luo, M., Mckinnon, K., Rubin, R., Skelton, J., Smikle, R., Vaher, K., & Richardson, H. (2025). Behind the scenes: Using movies to study the human brain. Frontiers for Young Minds.

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.

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

Bhatt, N. S. (2021). Uncovering Object Categories in Infant Views. Scripps Senior Theses. 1664.

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.

Zettersten, M., Bergey, C. A., Bhatt, N. S., …, & Frank, M. C. (2021). Peekbank: Exploring children’s word recognition through an open, large-scale repository for developmental eye-tracking data. Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

Bhatt, N. S., Tregillus, K. E. M., & Engel, S. A. (2019). Classification Analyses of fMRI Data Predict Perceived Color. Poster presented at Bay Area Vision Research Day (BAVRD), Berkeley, California.

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), San Marcos, California.

Teaching Experience

Lab Teaching Assistant, University of Edinburgh

• 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

Teaching Assistant, Harvey Mudd College

• Computability and Logic (CSCI-081) with Dr. George Montañez
  • Spring 2020
  • Led weekly sessions and tutored ~20 students to clarify topics relating to computability theory, set countability, formal logics, Chomsky grammars and languages, Turing machines, and automata

Teaching Assistant, Pomona College

• Calculus II w/ Applications to Science (MATH-031S) with Dr. Blerta Shtylla
  • Autumn 2019
  • Led twice weekly sessions and tutored ~30 students to help understand series and sequences, integration, differential equations, and probabilistic applications using examples in the sciences

Work Experience

Writing Tutor, PPLS Skills Centre, University of Edinburgh

• September 2023 - August 2025
  • Guided students through editing process for various writing projects, from coursework to dissertations

Open Research Facilitator, PPLS Open Science, University of Edinburgh

• October 2023 - August 2025
  • Lead open science practices within ongoing research at the School of Philosophy, Psychology, and Language Sciences by hosting information sessions and holding appointments
  • Initiated Peer Code Review program to encourage reproducibility, auditability, and other best practices

STEAM Coordinator, Scripps College Academy

• January 2021 - May 2021
  • Implemented program for >80 underrepresented high schoolers in Los Angeles and Inland Empire area to provide access to experiences in science, technology, engineering, art, and mathematics

Programming Projects

Manifold-based Binary Classification using Jointly Embedded Geometric Data

• Nonlinear Data Analytics (MATH-178), Summer 2020
  • Jointly embedded mobile phone data and eye tracking data in low dimensional manifold space to more effectively reproduce the classification of whether or not users were attending to their phones

Endangered? A tool for labeling and classifying animals in the wild

• Software Development (CSCI-121), Autumn 2019
  • Built a React Native application classifying an uploaded image of an animal, returning predicted genus, species, and conservation status for users to interact with the natural world with concern for other species

Professional & Academic Service

Postgraduate Research Student Representative in Psychology, University of Edinburgh

• 2023-2025

Psychology Society Community & Research Association Postgraduate Liason, University of Edinburgh

• 2022-2023

Programme Representative for M.Sc. Psychological Research, University of Edinburgh

• 2022-2023

Flux Society Pre-Conference Workshop Facilitator: Computational Modelling in Development

• 2021

Conference Volunteer

• Women in Machine Learning @ NeurIPS, 2021
• Women in Machine Learning @ NeurIPS, 2019

Fellowships, Awards, and Honors

• UKRI Economic and Social Research Council Doctoral Training Studentship, 2022-2025
• Scripps College Humanities Institute Fellowship, 2021
• Scripps Success Grant Scholarship, 2020-2021
• NSF REU at the Center for the Study of Language and Information (Award #1950223), 2020
• Scripps Student Conference Travel Fund, 2019
• NSF REU in Cognitive Science and Neuroimaging (Award #1757390), 2019
• Scripps College Dean’s List, 2018, 2020

Skills

• Programming
  • Python (Tensorflow, Keras, Detectron2), R (tidyverse), Linux, Docker, MATLAB, C++, Java, Javascript/HTML (jsPsych, React Native)
• Neuroimaging
  • fMRI (fMRIPrep, FSL), DTI (FSL, MRtrix3, ANTs)
• Research Tools
  • Pavlovia, Amazon Mechanical Turk, Prolific, Qualtrics, RedCap, Amazon Sagemaker
• Program Management
  • Agile, Scrum, Kanban, Waterfall