Freelancer profile translated to English.
Back to original languageAbout Martin
Data scientist specialized in computer vision.
I have worked on intelligent sensor systems applied to healthcare, developing fall detection algorithms, optimizing AI models for embedded systems, and creating web and mobile applications for real-time monitoring. My research at CNRS focused on EEG signal analysis, machine learning for epilepsy detection, and modeling spiking neural networks. I also have freelance developer experience in implementing computer vision algorithms and creating interactive applications.
My toolkit includes Python, PyTorch, web technologies (React Native, JavaScript), Docker, SQL, Git, and more. I enjoy designing efficient algorithms, transforming raw data into actionable insights, and building reliable systems from hardware to cloud.
French
Native or bilingual
English
Fluent
Spanish
Conversational
Can work on-site
Toulouse (up to 15km)
Experience
- wiiCareEdge AI EngineerINTERNET OF THINGS (IOT)October 2020 - December 2024 (4 years and 2 months)Toulouse, FranceDevelopment of an intelligent sensor-based solution for fall detection and monitoring of dependent individuals' behavior. Design of detection algorithms, optimization of AI models for embedded systems, management and monitoring of sensors, as well as creation of web and mobile applications for real-time monitoring. Also participated in hardware design and testing.
- Design and implementation of fall detection algorithms
- Optimization of AI models for embedded systems
- Management, monitoring, and integration of intelligent sensors
- Development of web and mobile applications for alert and sensor monitoring
- Hardware design and testing
- CNRS - CerCo UMR5549 , CHU Toulouse, Université Toulouse IIIResearch EngineerPUBLIC SECTOROctober 2014 - January 2019 (4 years and 4 months)Toulouse, FranceResearch work within the Memory and Learning of Objects and Scenes team. EEG signal analysis and processing for projects on epilepsy and memory. Development of automatic classification tools and visualization interfaces, modeling of spiking neural networks, and exploration of unsupervised learning mechanisms (STDP).EpiFaR Project:
- iEEG data analysis and processing
- Automatic detection of Fast-Ripples
- Development of a signal visualization interface
- Optimization of recording conditions to reduce noise level
- EEG signal classification, Multi-Variate Pattern Analysis
- Spike Sorting
M4 Project: Memory Mechanisms in Man and Machine:- Spiking neural network modeling
- Unsupervised learning via STDP (Spike Timing Dependent Plasticity)
- Development of a cochlear model for converting audio signals to spikes
- Orange Labs, RennesEnd-of-studies internshipTRANSPORTATIONFebruary 2013 - July 2019 (6 years and 6 months)Design of a speech/noise classifier used for estimating noise's DSP, later integrated into VoIP software for noise reduction.
Reviews
Xavier
wiiCare
Reviewed on 11/2/2020
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Education
- Signal Processing EngineerGrenoble INP, Phelma2013Fillière Signal and Image Processing, Communication Systems and Multimedia (SICOM)