Talmo Pereira

Talmo Pereira

PhD Candidate in Neuroscience

Princeton University


Hi, I’m Talmo. I’m currently working on my PhD in Neuroscience at the Princeton Neuroscience Institute. I’m co-advised by Mala Murthy and Joshua Shaevitz.

My research has focused on building computational tools to quantify animal behavior with the goal of better understanding the brain.

My work has been featured in Quanta Magazine, Nature Lab Animal, Nature Toolbox, The Scientist, Princeton Insights, and more.

In November 2021, I will be starting my lab as a Fellow at the Salk Institute for Biological Studies in San Diego.


  • Systems neuroscience
  • Ethology
  • Computer vision (pose estimation, action recognition)
  • Machine learning (deep learning, dimensionality reduction)


  • PhD in Neuroscience, 2021 (Expected)

    Princeton University

  • MS in Neuroscience, 2017

    Princeton University

  • BS in Bioinformatics and Computational Biology, 2015

    University of Maryland, Baltimore County



Research Intern

Google AI

Jun 2020 – Dec 2020 Mountain View, CA

Research internship at Google AI in Machine Perception. Developed deep learning systems for network architecture search and human action recognition.

Supervisor: Shay Ohayon


PhD Student

Princeton Neuroscience Institute

Jun 2015 – Present Princeton, NJ

Using fruit flies (Drosophila melanogaster) to study how the brain integrates sensory information to pattern behavioral sequences. Developed hardware and software for acquisition and processing of high resolution video, audio and realtime instrument control. Developed computational methods that leverage computer vision, deep learning, signal processing and unsupervised machine learning for quantifying behavior through few shot pose estimation and manifold embedding. Working on mapping the substrates and function of biological neural circuits involved in patterning the structure of complex motor behaviors.

Advisors: Mala Murthy, Joshua Shaevitz


Amgen Scholar

California Institute of Technology

Jun 2014 – Aug 2014 Pasadena, CA

Worked on computational analysis of aggressive behaviors in fruit flies using methods in computer vision for animal tracking and supervised machine learning for timeseries segmentation.

Advisor: David J. Anderson


Student Researcher

Massachusetts Institute of Technology

Jun 2013 – May 2014 Cambridge, MA

Worked on image processing algorithms for large scale (100-1000s GiB) electron microscopy image alignment/registration for connectomic reconstruction.

Advisor: Sebastian Seung


Student Researcher

University of Maryland, Baltimore County

Jan 2013 – May 2015 Baltimore, MD

Developed computational algorithm for GPU-accelerated transcription factor DNA binding site prediction in large scale (10-100s GiB) metagenomic sequencing data. to characterize transcriptional regulatory networks.

Advisor: Ivan Erill


Student Researcher

Broad Institute of MIT and Harvard

Jun 2012 – Aug 2012 Cambridge, MA

Worked on characterizing interactions between proteins associated with psychiatric disease in the axon initial segment via immunochemical assays.

Advisor: Jon Madison


HHMI Scholar

National Institutes of Health

Jun 2010 – Jun 2011 Bethesda, MD

Characterized the learning and memory impairment induced by knockout of a type-1 diabetes-associated neuroendocrine transport gene in mice.

Advisor: Abner L. Notkins


Porter Ogden Jacobus Fellowship

Awarded Princeton University’s top honor for graduate students.

See press release and award presentation video.

President’s Prize

Awarded honorific fellowship on admission to graduate program.

NSF Graduate Research Fellowship Program

One of 2,000 awardees selected from a pool of 16,500 applicants for the grant.

The Barry Goldwater Scholarship and Excellence in Education Program

Received an Honorable Mention from a pool of 1,166 nationwide nominees.

MARC Undergraduate Student Training in Academic Research

The MARC U-STAR scholarship is an award funded by the NIH in an effort to support undergraduates in pursuing experiences and a career in research. Provides a research stipend, full academic scholarship, funding for lab expenses and travel.

UMBC Undergraduate Research Award (URA)

Research grant awarded to undergraduates pursuing research on campus.

Howard Hughes Medical Institute Scholar

Highly selective HHMI-sponsored program aiming to increase diversity in the biomedical sciences. Supports students with a research stipend, pre-professional advising and funding for summer research.

Meyerhoff Scholar

Selective scholarship program at UMBC supporting students pursuing a career in research in the STEM fields.

Broad Prize Scholar

Broad Prize scholarships are awarded to graduating seniors who have a demonstrated record of improving their grades over the course of their high school career and have a financial need by the Eli and Edythe Broad Foundation. One of thirteen awardees in Montgomery County, MD.


Filter or search publications and abstracts.
(2020). Quantifying behavior to understand the brain. Nature Neuroscience.

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(2020). SLEAP: Multi-animal pose tracking. bioRxiv.

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(2020). A machine-vision approach for automated pain measurement at millisecond timescales. Elife.

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(2020). Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics. Annu. Rev. Neurosci..

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(2020). Inexpensive multi-patient respiratory monitoring system for helmet ventilation during COVID-19 pandemic. medRxiv.

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(2020). The Neural Basis for a Persistent Internal State in Drosophila Females. bioRxiv.

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(2019). Fast animal pose estimation using deep neural networks. Nat. Methods.

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(2018). Normal cognitive and social development require posterior cerebellar activity. Elife.

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(2018). Discovery of a New Song Mode in Drosophila Reveals Hidden Structure in the Sensory and Neural Drivers of Behavior. Curr. Biol..

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(2018). Quantifying Humans' Priors Over Graphical Representations of Tasks. International Conference on Complex Systems.

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(2018). Automated gesture tracking in head-fixed mice. J. Neurosci. Methods.

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(2017). To Fight or Not to Fight. Neuron.

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(2017). Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nat. Neurosci..

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(2016). A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data. Algorithms Mol. Biol..

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