Pradeep Reddy Raamana
Director, Data Science & Operations
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolPradeep Reddy Raamana is a data science leader and neuroscientist with 11 years of applied experience and a PhD in Biomedical Engineering, now directing Data Science & Operations to bring ML, AI and LLMs into regulated drug development workflows. He blends deep domain expertise in neuroimaging and clinical biomarkers with hands-on engineering—contributing test automation and data-processing improvements to prominent open-source projects like DIPY, Nilearn and NiBabel. Across academia, industry and consulting he has built scalable pipelines, semantic-graph NLP prototypes for clinical text, and robust forecasting models, always emphasizing reproducibility, QC and model observability. Known for translating rigorous research into production-ready systems, he pairs grant‑level scientific rigor with product-minded delivery in healthcare settings.
11 years of coding experience
14 years of employment as a software developer
Indian Institute of Technology MadrasB.S Mathematics Physics Computer Science, B.S Mathematics Physics Computer Science at Sri Venkateswara UniversityPh. D Biomedical Engineering, Ph. D Biomedical Engineering at Simon Fraser University
Stackoverflow
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0reached
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Github Skills (17)
data-formats10
python10
brain-imaging10
signal-processing10
testing10
machine-learning10
data-format10
numpy10
neuroimaging10
affine-transformation10
data-analysis9
medical-imaging9
mri8
tractography8
diffusion-mri7
Programming languages (16)
Github contributions (5)
nipy/nibabel
Aug 2017 - Mar 2018
Python package to access a cacophony of neuro-imaging file formats
Role in this project:
Data Scientist
Data ScientistContributions:10 commits, 32 comments, 4 issues in 7 months
Contributions summary:Pradeep contributed to the `nibabel` repository, which focuses on neuroimaging data formats. They implemented a reader for aseg.stats files from Freesurfer, enabling the extraction of subcortical statistics. The user also added a reader for aparc.stats files, facilitating the retrieval of cortical feature statistics, enhancing the library's ability to process Freesurfer outputs. Furthermore, they introduced a test case to validate the aseg.stats reader.
pythontckimagingtrkstreamlines
nilearn/nilearn
Mar 2018 - Apr 2018
Machine learning for NeuroImaging in Python
Role in this project:Data Scientist
Data ScientistContributions:14 commits, 3 PRs, 26 comments in 19 days
Contributions summary:Pradeep primarily focused on improving the `nilearn` library's signal processing capabilities, particularly within the context of neuroimaging data. Their contributions included bug fixes related to the application of time repetition (t_r) parameters and filter cutoffs, code refactoring to resolve conflicts, and improvements to existing tests to ensure accuracy when cleaning data. The user's work involved modifying filtering functions and testing various sampling rates to improve the reliability of the signal cleaning process.
neuroimagingpythondecodingbrain-connectivitymachine-learning
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