Michael Notter
Lead AI Research Scientist at AlphaLum
Lausanne, Vaud, Switzerland
Summary
👤
Senior🎓
Top SchoolMichael Notter is a Lead AI Research Scientist with 14 years of experience at the intersection of machine learning, neuroimaging, and computer vision, currently driving AI strategy and ultra-efficient sensing solutions in Lausanne. He translates academic rigor into product-ready systems for AR/VR, eye-tracking, vital-signs monitoring, and low-power optical HCI, holding patents that improve human-computer interaction. A neuroscientist by training (PhD) and experienced instructor, he builds end-to-end pipelines and trains teams to operationalize advanced algorithms on resource-constrained devices. Michael is an active open-source contributor to flagship neuroimaging projects like Nipype and Nilearn, where he’s fixed core processing bugs and improved visualization and signal-cleaning routines. His work blends real-time signal processing, sensor fusion, and energy-aware model design—often surfacing non-obvious gains by rethinking algorithmic order and I/O handling for embedded deployments. Colleagues rely on him for autonomous technical leadership that bridges research, engineering, and customer-driven product integration.
14 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Neuroscience, Doctor of Philosophy - PhD, Neuroscience at University of LausanneMaster of Science - MS, Cognitive Neuroscience; Minor in Neuroinformatics, Master of Science - MS, Cognitive Neuroscience; Minor in Neuroinformatics at University of Zurich
English, German, French
Github Skills (21)
algorithm10
algorithms10
python10
spu10
brain-imaging10
machine-learning10
workflow-engine10
spi10
spim10
implement10
fn10
neuroimaging10
spmd10
image-processing9
data-science9
Programming languages (7)
Github contributions (5)
nipy/nipype
Mar 2013 - Oct 2018
Workflows and interfaces for neuroimaging packages
Role in this project:
Back-end Developer & Algorithm Implementer
Back-end Developer & Algorithm ImplementerContributions:148 commits, 10 PRs, 1 push in 5 years 8 months
Contributions summary:Michael contributed to the development of the neuroimaging software, specifically focusing on the AFNI (Analysis of Functional NeuroImages) suite within the Nipype framework. They addressed issues related to data processing interfaces, addressing a bug in the `buildtemplateparallel.sh` script that involved handling input files and strings. Furthermore, the user enhanced the software by incorporating the 'colored' graph visualization to the program for improved workflow analysis. The commits also show corrections in SPM (Statistical Parametric Mapping) routines to match the latest version SPM12.
workflowneuroimagingpythondata-scienceworkflow-engine
nilearn/nilearn
Jul 2016 - Oct 2018
Machine learning for NeuroImaging in Python
Role in this project:Data Scientist
Data ScientistContributions:31 commits, 4 PRs, 30 comments in 2 years 3 months
Contributions summary:Michael primarily focused on improving the `nilearn` library, which is designed for machine learning in neuroimaging. Their contributions involved bug fixes, such as correcting hemisphere mix-ups in plotting functions and adapting to changes in external dependencies (e.g., matplotlib). They also implemented changes to the `clean_img` and `clean` functions, including modifications to the order of operations and improved error handling, and added tests to verify these changes.
neuroimagingpythondecodingbrain-connectivitymachine-learning
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