Johan Mathe is co-founder and Chief Technology Officer at Atmo. He is currently leading scientific
technological development - pushing the boundaries of climate and weather science using AI, applied
mathematics and massively parallel distributed systems.
His lifelong mission is to bring applied mathematics and AI to the physical sciences. After 8 years at
Google and Google X working on Project Loon, leading critical efforts in the planning and simulation
he went to bring deep learning to the world of cardiology as one of the founding team members at Caption
Health. After building the first prototypes and products, he went to bring AI to the world of Plasma
Physics, as Director of Engineering at Apollo Fusion (now Astra Space) which recently launched their
propulsion system in orbit.
Johan holds 14 patents, various peer-reviewed publications in AI and applied mathematics, and is also a
recreational aerobatics pilot. While he was at caption health, he participated in outreach missions in
Eldoret, Kenya to help detect rheumatic heart diseases amongst more than 1000 children.
Projects, Research & Patents
Interest in signal processing, optimization, statistics/machine learning, image processing and control
PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic Power Forecasting from Numerical
arXiv Preprint, 2019
Photovoltaic (PV) power generation has emerged
as one of the lead renewable energy sources. Yet,
its production is characterized by high uncertainty,
being dependent on weather conditions like solar
irradiance and temperature. Predicting PV production,
even in the 24 hour forecast, remains a
challenge and leads energy providers to keep idle
- often carbon emitting - plants. In this paper we
introduce a Long-Term Recurrent Convolutional
Network using Numerical Weather Predictions
(NWP) to predict, in turn, PV production in the
24 hour and 48 hour forecast horizons. This network
architecture fully leverages both temporal
and spatial weather data, sampled over the whole
geographical area of interest. We train our model
on a NWP dataset from the National Oceanic and
Atmospheric Administration (NOAA) to predict
spatially aggregated PV production in Germany.
We compare its performance to the persistence
model and to state-of-the-art methods.
geomstats: a Python Package for Riemannian Geometry in Machine Learning
arXiv Preprint, 2018
A python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces,
spaces of symmetric positive definite matrices and Lie groups of transformations.
Performance Evaluation of the Alternating Direction Method of Multipliers
I implemented the Alternate Direction Method of Multipliers (ADMM) global consensus algorithm with the
and compare the resulting performance with another distributed optimization technique. We
will also investigate various caveats with this implementation. More recently I implemented a version of
ADMM for keras.
Project Loon Patents
- Selection between Explore Mode and Control Mode for Aerial Vehicle (USPTO#9836063).
- Efficient aerostat navigation by moving between atmospheric layers (USPTO#9665103)
- Optimal altitude controller for super pressure aerostatic balloon (USPTO#9550558)
Guided Navigation of an Ultrasound Probe
Embodiments of the invention provide for the guided navigation of an ultrasound probe. In an embodiment of
the invention, an ultrasound navigation assistance method includes acquiring an image by an ultrasound
probe of a target organ of a body. The method also includes processing the image in connection with an
estimator such as a neural network. The processing in turn determines a deviation of a contemporaneous
pose evident from the acquired image from an optimal pose of the ultrasound probe for imaging the target
organ. Finally, the method includes presenting the computed deviation to an end user operator of the
Atmo builds AI-enabled hardware-software systems that
solve weather prediction for any city, state, or country.
I was part of the Caption Health founding team. I designed some of the first guidance and diagnostic
algorithms. I spent a fair amount of time working on Rhumadic Heart
Disease. With a team of Echo sonographers and pediatricians from the American Society of
Echocadrigraphy we went to Kenya to help young children in remote villages get early diagnostics. Some
more details in Jack's
At Apollo I created various black box optimization methods in order to improve the performance of plasma
based systems with a reactor-in-the-loop experiment system. The performance improvements led to the
building of improved hall effect thrusters. I also led the design of the Power Propulsion Unit, which is
the part of a satellite that converts power from the main bus to the thruster itself.
Google X, Project Loon
At X and Loon, I designed some of the original guidance algorithms and software in project Loon. That's
where I started working extensively with weather data - especially wind data. I focused on optimal
control and path planning. We also worked on explore exploit strategies to allow unprecedented balloon
steering performance. You can find more details on this article from MIT Tech Review.
This video is a good starting point if you
never heard of project Loon. A great
article on The
Verge gives some interesting bits about loon navigation's system.
Google Software Engineer and SRE
At Google I worked on the design of distributed systems (Google File System and its successor colossus).
Subsequently I worked on the census library and the first version of what's publicly known as GRPC, the google RPC library.
After learning how to fly airplanes upside down, we decided to go and run some experiments with floating
water. We tried to pour water during inverted flight, during 0 g parabolas, and rolls. This kind of
aerobatics flight brings us from -1G all the way to 4Gs.
Flame-throwing Remote Controlled Kayak
We built an RC kayak using exclusively fans. To be more specific, there are no underwater moving parts -
only thrusters. Two orthogonal flame throwers are mounted on top of the kayak.