Baladitya Yellapragada
Machine Learning Engineer at Tangible Robots
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolBaladitya Yellapragada is a Machine Learning Engineer and applied computer vision specialist with 12 years of experience translating noisy, real-world data into robust models and interpretable insights. He holds a Ph.D. in Vision Science and EECS degrees from UC Berkeley and combines neuroscience-inspired experimentation with production ML—having moved retinal disease segmentation from 60 expert labels to a 100x augmented dataset with near-expert performance. Bala has built end-to-end anomaly and perception pipelines for industry (vehicle telemetry, medical web UI, edge drone inference) and contributed backend ROI and SVG spline tools to the pycortex fMRI visualization project. He blends research rigor—publishing work on self-supervised networks that reveal clinician-relevant imaging groupings and motion-sensitive units—with hands-on deployment at companies like Clover Therapeutics, Pull Systems, and Tangible Robots. Known for extracting signal from messy data and linking model behavior to biological systems, he is comfortable shifting between lab-style experiments and customer-facing productionization. Based in Berkeley, he uniquely pairs neuroscience intuition with pragmatic engineering to make opaque models actionable.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Vision Science/Physiological Optics, Doctor of Philosophy - PhD Vision Science/Physiological Optics at University of California, Berkeley
Github Skills (6)
svg10
python10
neuroimaging10
mri10
scipy8
numpy8
Programming languages (2)
Github contributions (5)
gallantlab/pycortex
Jun 2014 - Oct 2014
Pycortex is a python-based toolkit for surface visualization of fMRI data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 commits in 4 months
Contributions summary:Baladitya primarily contributed to the development of a Python-based toolkit for surface visualization of fMRI data. Their commits focused on implementing ROI (Region of Interest) functionality, specifically adding features related to SVG (Scalable Vector Graphics) manipulation for defining and visualizing ROIs. They wrote new classes for handling splines within the SVG context, enabling the creation of more complex ROI shapes. The contributions included modifications to existing files, integrating the new spline classes into the core of the pycortex project.
neuroimagingpythonfmritopological-data-analysissurface
bddmodelcar/users_allowed_keys
Aug 2017 - May 2018
Contributions:6 PRs, 6 pushes in 9 months
directoryallowedfor-eachkeys
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