J Guntupalli
Research Scientist at Google DeepMind
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolJ Guntupalli is a cognitive neuroscientist and research scientist with 16 years of experience translating high-dimensional brain data into computational models of information coding. Based in the San Francisco Bay Area, he currently contributes to foundational AI and neuroscience research at Google DeepMind after leading and scaling research efforts at Vicarious. He developed and extended hyperalignment methods to align multivariate neural representational spaces across subjects, enabling cross-subject decoding and shared high-dimensional common models. His background blends rigorous PhD- and postdoc-level neuroscience with applied software and data-science skills (including bug fixes and enhancements to the widely used hypertools Python toolbox). He has experience with both human fMRI and invasive macaque recordings, revealing conserved functional connectivity patterns and practical methods for leveraging naturalistic stimuli. This combination of theoretical innovation, hands-on experimental work, and open-source contributions positions him to bridge neuroscience insights and scalable computational methods.
16 years of coding experience
11 years of employment as a software developer
Indian Institute of Technology MadrasDoctor of Philosophy (Ph.D.), Cognitive Neuroscience, Doctor of Philosophy (Ph.D.), Cognitive Neuroscience at Dartmouth CollegeMS, Computer Science, MS, Computer Science at Texas A&M University
Github Skills (8)
data-wrangling10
data-visualizations10
data-visualization10
data-visualisation10
python10
numpy10
scikit8
scikit-learn8
Programming languages (4)
Github contributions (5)
ContextLab/hypertools
Dec 2016 - Dec 2016
A Python toolbox for gaining geometric insights into high-dimensional data
Role in this project:
Data Scientist
Data ScientistContributions:8 commits in 1 day
Contributions summary:J primarily focused on improving and debugging the `hypertools` library, which provides geometric insights into high-dimensional data. They fixed major bugs in the `procrustes` and `align` functions, addressing issues related to data handling and shallow copies that were altering input datasets. Furthermore, the user updated an example to highlight the impact of hyperalignment on misaligned datasets using a spiral example.
high-dimensional-datapythontoolboxdimensionalpython-toolbox
swaroopgj/PyMVPA
Jan 2015 - Apr 2018
multivariate pattern analysis in Python
Contributions:9 PRs, 119 pushes, 37 branches in 3 years 3 months
loopspythonanalysispattern-analysismultivariate
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