Inference Infrastructure for the Real World.
We build probabilistic inference systems for complex, real-world problem. Our technology reconstructs interpretable signals from messy, sparse, or drifting data — using physics and probabilistic inference.
No training data. No black boxes. Just grounded models that know what they do not know. Currently focused on medical imaging, where trust, interpretability, and robustness are essential.
REASON BEYOND NOISE.
Core principles
We don’t predict, we reveal.
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1The world is continuous. The data is not.
Reality doesn t come in clean rows and columns. Sensors drift, signals drop, instruments lie. We do not model outputs — we uncover the signal beneath the noise.
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2Inference is not prediction
We do not guess. We build interpretable systems that recover structure, quantify uncertainty, and explain themselves.
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3Uncertainty is signal.
In medicine and other high-stakes domains, knowing what you don t know is critical. We build systems that surface uncertainty — and use it as fuel for inference.
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4Deep learning is not enough.
It breaks when the data is sparse, drifting, or physical. We model where structure matters and trust is required — even if you only have five measurements.
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5We are the layer beneath.
We are not a medical company. Not a climate company. Not a communications company. We are inference infrastructure for the real world.
Our Solutions
Got a messy dataset and a headache?
We might be able to help.
Our research combines physics-based modeling and probabilistic inference to enhance both medical imaging and environmental monitoring. In magnetic resonance imaging, we improve resolution and signal quality by leveraging physical priors – enabling clearer images without relying on black-box methods. At the same time, we develop high-resolution maps of urban air pollution by fusing satellite data, sensor networks, and weather information. Our models deliver not only precise results but also reliable uncertainty estimates – supporting informed decisions in both clinical settings and urban planning.
Contact
Sharper images from noisy scans.
We’re developing high-resolution maps of urban air pollution by fusing satellite
observations, sensor networks, and weather data. Our probabilistic models help cities understand when, where, and how air quality fluctuates—complete with uncertainty estimates for decision-makers who need to trust what they see.
Questions? Contact us!
Our Technology
Beyond prediction.
Towards understanding.
At LambdaFields, we build models that don’t just fit data – they understand it. Our core technology is a general-purpose framework for probabilistic inference in spatiotemporal fields. It’s grounded in physics, designed for uncertainty, and built to adapt across domains.
What Technology and why?
Most systems that try to reconstruct fields from sparse or noisy data rely on black-box models or ad hoc interpolation. We do neither.
About us
LambdaFields is a research-driven deep-tech startup focused on probabilistic inference for complex physical systems.
LambdaFields has been founded by Dr. Jakob Knollmüller and Dr. Philipp Arras. Their scientific background is in astrophysics and statistical signal processing.
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Questions? Contact us!
Partnerships
