Mathis Richter
Application Software Lead
Bochum, North Rhine-Westphalia, Germany
Summary
👤
Senior🎓
Top SchoolMathis Richter is an Application Software Lead at Intel Labs with 15 years of experience bridging academic research and production software in neuromorphic computing and neural cognitive systems. He holds a Dr.-Ing. in Artificial Intelligence and has a strong track record at Ruhr University Bochum developing recurrent architectures, managing a large C++ codebase, publishing extensively, and teaching. At Intel he moved from research scientist to lead, implementing neuromorphic state machines on Loihi and driving application-level software. An active back-end developer and software architect on the open-source lava neuromorphic framework, he has refactored core systems, introduced virtual ports, and steered the project toward permissive licensing and stronger testing. Colleagues rely on him for deep systems design, rigorous validation, and translating theoretical models into deployable neuromorphic software.
15 years of coding experience
14 years of employment as a software developer
Dr.-Ing. (PhD), Artificial Intelligence, magna cum laude, Dr.-Ing. (PhD), Artificial Intelligence, magna cum laude at Ruhr University BochumComputer Science, Computer Science at University of BirminghamEast Union Middle-High School
German, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (12)
neural-network10
architectures10
python10
morph10
architecture10
unit-testing9
deep-learning8
refactoring8
refactor8
git7
process-model7
process-modeling7
Programming languages (2)
Github contributions (5)
lava-nc/lava
Oct 2021 - Jan 2023
A Software Framework for Neuromorphic Computing
Role in this project:
Back-end Developer / Software Architect
Back-end Developer / Software ArchitectContributions:1 release, 157 reviews, 30 commits in 1 year 2 months
Contributions summary:Mathis primarily focused on refactoring and improving the codebase of a neuromorphic computing framework. Their contributions include removing proprietary headers and adding open-source licenses, indicating a shift towards a more open and accessible project. They addressed bugs related to testing, demonstrating involvement in the testing framework. Furthermore, the user implemented virtual ports, a significant architectural enhancement, showcasing design and implementation of core functionalities.
neurosciencepythondeep-learningneural-networksmachine-learning
lava-nc/lava-dnf
Nov 2021 - Jan 2023
Dynamic Neural Fields with Lava
Contributions:1 release, 39 reviews, 14 commits in 1 year 2 months
lavaneural-fieldsmachine-learningneural-networkfields
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