Ram Koppu
Embedded Software Engineering Consultant at Ohme
Gloucester, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolRam Koppu is an embedded software engineering consultant with over a decade of hands-on experience building safety-critical and consumer IoT systems across medical, appliance, EV charging and wearable domains. He has delivered embedded R&D for leading firms including Dyson, Siemens Healthineers and WITTENSTEIN, and now focuses on product innovation for intelligent EV charging at Ohme. His background spans real-time microcontroller firmware, mixed-signal control for superconducting MRI systems, SoC security architecture and Bluetooth RF protocol work, reflecting deep low-level systems expertise. Certified in TinyML, he brings an interest in bringing machine learning to constrained devices and contributes to Physical AI open-source efforts. Based in Gloucester, UK, Ram combines industry-grade engineering rigor with a pragmatic inventors’ drive—he even hopes one day his robots will handle the household chores. He is adept at turning complex hardware constraints into reliable, safety-conscious firmware that ships to market.
13 years of coding experience
21 years of employment as a software developer
Jawaharlal Nehru Technological University HyderabadProfessional Certificate program in Tiny Machine Learning(TinyML), Professional Certificate program in Tiny Machine Learning(TinyML) at HarvardX
English, Telugu, Hindi
Github Skills (114)
python10
deep-learning10
whisper10
ml10
neural-network10
data-science10
tensorflow10
speech-to-text10
machine-learning10
keras10
deep-neural-networks10
nlp9
pytorch9
language-processing9
inference9
Programming languages (7)
Github contributions (5)
ramkumarkoppu/ESW
Dec 2021 - Sep 2022
Contributions:2 PRs, 27 pushes, 1 branch in 8 months
ramkumarkoppu/tinyengine
Sep 2022 - Jan 2024
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; MCUNetV3: On-Device Training Under 256KB Memory
Contributions:2 pushes in 1 year 3 months
memorytrainingon-devicetinyneurips
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