Neeraj Wagh
Research Fellow at University of Illinois Urbana-Champaign
Minneapolis, Minnesota, United States
Summary
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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 (60)
graph10
decode9
pytorch9
python9
machine-learning9
neuroimaging9
deep-learning9
neuroscience9
statistics8
spotlight8
visualization7
git-annex7
data-storage7
optuna6
representation-learning6
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
guidedeeg-signalsdiagnosiseeg-gcnnneurips
neerajwagh/evaluating-eeg-representations
Sep 2022 - Nov 2022
Contributions:6 commits, 5 pushes, 1 branch in 1 month
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