Mikheil Oganesyan
Head Of AI & Applied Research at Onsera Health
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMikheil Oganesyan is a Head of AI & Applied Research with nine years’ experience building clinical-grade machine learning for remote patient monitoring and cardio-metabolic health. He led teams that deployed real-time AI at scale—monitoring tens of thousands of patients, delivering FDA Class II on-device models for bed-exit detection, and producing prognostic models that improved clinical outcomes. His background blends electrical engineering (MEng, Imperial College) with signal-processing research on EEG biomarkers and radar-based contactless monitoring, a niche that bridges hardware, embedded ML, and clinical data. Equally comfortable in startup and enterprise settings, he has a track record of founding analytics practices, contributing to patents and peer-reviewed forums, and piloting cross-industry ML products from fashion forecasting to health-tech. Notably, he now steers AI strategy for Onsera Health while maintaining hands-on applied research that turns sensor signals into actionable care.
9 years of coding experience
3 years of employment as a software developer
Master of Engineering (MEng) Electrical and Electronic Engineering, Master of Engineering (MEng) Electrical and Electronic Engineering at Imperial College London
Github Skills (53)
keras10
object-detection10
evaluation9
keras-models9
keras-tensorflow9
tensorflow-serving9
tensorflow9
instance-segmentation9
mask-rcnn9
mlmodel8
prompt-engineering8
ai8
model-management8
machine-learning8
observability8
Programming languages (4)
Github contributions (5)
moganesyan/tensorflow_model_deployment
Sep 2019 - Apr 2020
Exporting the Mask RCNN with TF-Serving (REST, GRPC, Notebooks provided)
Contributions:25 commits, 8 PRs, 38 pushes in 6 months
grpcmask-rcnnresttensorflowtensorflow-serving
moganesyan/tf-playground
Nov 2021 - Jul 2022
Tensorflow playground. Where I deploy implement random deep learning models and papers in Tensorflow / Keras
Contributions:6 PRs, 54 pushes, 4 branches in 7 months
deep-learningkerastensorflow
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