Ali Ramezani-Kebrya
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Ali Ramezani-Kebrya

Visual Intelligence PI awarded prestigious research grant

Congratulations to associate professor Ali Ramezani-Kebrya for receiving a prestigious FRIPRO grant from the Research Council of Norway!

Visual Intelligence PI awarded prestigious national grant

Congratulations to associate professor Ali Ramezani-Kebrya for receiving the prestigious FRIPRO grant from the Research Council of Norway!

By Petter Bjørklund, Communications Advisor at SFI Visual Intelligence

On April 9th, the Research Council of Norway announced that he was awarded a prestigious national grant of NOK 8 million for his project Machine Learning in Real World (MLReal) through the FRIPRO programme. Ramezani-Kebrya is a principal investigator at Visual Intelligence and Integreat – Norwegian Centre for Knowledge-driven Machine Learning.

FRIPRO is a national, open-competition funding scheme designed to support bold, curiosity-driven research across all disciplines. The program aims to advance the state of the art by enabling innovative ideas from experienced researchers with a proven track record of excellence.

Machine Learning in the Real World

Machine learning (ML) with deep neural networks has made significant progress in recent years, enabling tasks like detecting anomalies in medical images and translating between less commonly studied languages. However, we are still far from reaching ML’s full potential, mainly because current learning theory doesn’t fully explain how to achieve the lowest possible error in real-world conditions. Practical tools that try to deal with real-world data often lack generality and are tailored to specific scenarios. Realistic conditions—such as limited computing power, missing data, or system failures—differ greatly from ideal lab settings. One of the key questions is how neural networks identify and extract the most relevant information from input data to make accurate predictions.

The MLReal project aims to understand and improve how neural networks process information, focusing on two key ideas: sufficiency (capturing enough information for prediction) and minimality (capturing only what’s necessary). By studying how data is transformed through different network layers using advanced mathematical tools, MLReal introduces new ways to measure and reduce prediction errors under real-world constraints.

This understanding allows the development of practical ML tools that can handle challenges across diverse fields like neuroscience, marine research, and emotion recognition—pushing the boundaries of what ML can achieve in everyday, imperfect conditions.

Ramezani-Kebrya brings extensive international experience from institutions such as EPFL and the Vector Institute, and he is an active contributor to leading AI research communities, including ELLIS, NeurIPS, and AISTATS. His research interests span from neural network theory to practical applications in emotion recognition, marine data, and neuroscience.

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