Neeraj Wagh
Data Scientist
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolNeeraj Wagh is a research-focused data scientist and bioengineer with a decade of experience applying AI and statistical methods to healthcare and environmental science. Currently a Research Fellow at Mayo Clinic and a PhD candidate in Bioengineering at UIUC, he specializes in AI-driven epilepsy diagnosis and digital health research informed by an MS in Statistics and a background in computer engineering. His work blends reproducible scientific computing, model optimization, and translational analytics—evidenced by prior internships at Janssen and Express Scripts where he delivered predictive models with production-focused documentation. He has also contributed to atmospheric science research, accelerating computational models by 4x through parallelization, a signal of strong numerical and systems skills beyond typical ML pipelines. Comfortable bridging lab research and product teams, Neeraj has a track record of turning complex data into deployable solutions that save clinician/recruiter time and improve decision-making. Based in Minneapolis, he brings academic rigor, practical engineering, and cross-domain curiosity to clinical AI challenges.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering - BE Computer Engineering, Bachelor of Engineering - BE Computer Engineering at Pune Institute of Computer TechnologyDoctor of Philosophy - PhD Bioengineering and Biomedical Engineering, Doctor of Philosophy - PhD Bioengineering and Biomedical Engineering at University of Illinois Urbana-Champaign
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (63)
convolutional-neural-networks10
graph10
deep-learning10
pytorch9
python9
machine-learning9
neuroscience9
neuroimaging8
representation-learning8
web-application8
visualization7
multi-task-learning7
statistics7
git-annex7
robustness7
Programming languages (4)
Github contributions (5)
neerajwagh/eeg-gcnn
Nov 2020 - Oct 2022
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.
Contributions:16 commits, 3 PRs, 15 pushes in 1 year 10 months
convolutional-neural-networksgraphgraph-neural-networkseeg-classificationeeg-signals-processing
neerajwagh/evaluating-eeg-representations
Sep 2022 - Nov 2022
Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.
Contributions:6 commits, 5 pushes, 1 branch in 1 month
mlmodelaugmentationdata-shifteeg-classificationeeg-data
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