Rwiddhi Chakraborty defending his PhD thesis at UiT The Arctic University of Norway
Congratulations to Rwiddhi Chakraborty for successfully defending his PhD thesis and achieving the PhD degree in Science at UiT The Arctic University of Norway on December 13th 2024.
Congratulations to Rwiddhi Chakraborty for successfully defending his PhD thesis and achieving the PhD degree in Science at UiT The Arctic University of Norway on December 13th 2024.
By Petter Bjørklund, Communications Advisor, SFI Visual Intelligence.
Chakraborty is a researcher at UiT Machine Learning group and SFI Visual Intelligence. His thesis, "Model and Data Diagnosis under Limited Supervision in Modern AI, proposes novel contributions in few-shot learning, semi-supervised learning, unsupervised learning, and in data diagnosis and debiasing techniques
The title of Chakraborty's trial lecture was "Unsupervised Semantic Segmentation".
Professor Yuki M. Asano, Head of the Fundamental AI Lab, University of Technology Nuremberg
Professor Adín Ramírez Rivera, IFI, University of Oslo (2nd opponent)
Associate Professor Elisabeth Wetzer, IFT, UiT (internal member and leader of the committee)
Professor Michael Kampffmeyer, IFT, UiT (Main Supervisor)
Professor Robert Jenssen, IFT, UiT (Co-Supervisor)
Associate Professor Benjamin Ricaud, IFT, UiT (Co-Supervisor)
Associate Professor Shujian Yu, IFT, UiT (Co-Supervisor)
Deep Learning in modern Artificial Intelligence (AI) has witnessed unprecedented success on a variety of domains over the past decade, ranging from computer vision to natural language reasoning tasks. This success is owed primarily to the availability of large, annotated datasets, the existence of powerful mathematical models, and the mechanism to train large models on such data with advanced resources of compute. However, this success has led to increased scrutiny on the failure points of models trained on suspect data. Issues such as model and data bias, reliance on spurious correlations, and poor generalization capability on challenging test data, to name a few, have surfaced in the research community. As a result, it seems imperative to diagnose such systems for generalization performance on challenging test data, and uncovering potential biases hidden in datasets. In this thesis, we address these key challenges through the following directions: first, in the generalization capabilities with limited labeled data - few-shot learning, semi-supervised learning, and unsupervised learning. Second, towards bias discovery in existing models and datasets, particularly in unsupervised group robust learning, and debiased synthetic data generation. Our two broad directions are encapsulated by a common challenge: the paucity of labeled data, since manually annotating large datasets is a time consuming and expensive process for humans. This motivation is relevant today due to the exponential growth in the sizes of models and datasets in use. It is becoming more and more intractable for humans to annotate billions of data points, leading to large benchmark datasets that are not well calibrated with human expectations on fairness. These issues, if left unchecked, are inevitably exacerbated when models train on such datasets. We consider these two directions, i.e. model generalization with limited labels, and the existence of biased data, to be two sides of the same coin, and thus coin the framework encapsulating such research as Model and Data Diagnosis. This work proposes novel contributions in few-shot learning, semi-supervised learning, unsupervised learning, and in data diagnosis and debiasing techniques. Further, we show that model and data diagnosis do not necessarily exist as disparate entities, and can be viewed in a co-dependent context. Finally, this thesis hopes to amplify the scrutiny surrounding model capabilities, however impressive, trained on datasets, however vast.
Alba Ordoñez and Anders U. Waldeland presented ongoing work on seismic foundation models and an interactive seismic interpretation engine at EAGE Annual 2025 in Toulouse, France.
Østmo was invited to Abels tårn—one of the largest popular science radio shows in Norway—to answer listener-submitted questions related to artificial Intelligence (AI). The live show took place at Blårock Cafe in Tromsø, Norway on June 12th.
VI industry partner Kongsberg Satellite Services (KSAT) received an Industrial PhD grant from the Research Council of Norway. The project will be closely connected to Visual Intelligence's "Earth observation" innovation area.
Visual Intelligence Researchers Amund Vedal and Arnt Børre Salberg recently presented ongoing Visual Intelligence research at a plankton-themed workshop organized by the Institute of Marine Research (IMR), Norway
We happily welcome Dennis Adamek, who recently started as a Doctoral Research Fellow at Visual Intelligence in Tromsø in April 2025.
Congratulations to Professor Lars Ailo for receiving the NORA Award for Diversity in AI at the NORA Annual Conference in Halden, Norway.
Teresa Dorszewski is a PhD Candidate at the Section for Cognitive Systems at the Technical University of Denmark. She visited Visual Intelligence in Tromsø from January to April 2025.
Centre Director Robert Jenssen was invited to give a keynote and participate in a panel discussion on AI as critical national infrastructure at the NORA Annual Conference 2025 in Halden, Norway.
Både forskere og statsråd mener kunstig intelligens bør tydelig merkes. Men forlaget som lager puslespillet som ekspertene mener er KI-generert, sier de ikke har noe med hvordan illustratører lager produktene sine (Norwegian news article by NRK)
Researchers have developed an artificial intelligence to map specific immune cells in lung cancer tumors. It can lead to less costly examinations and more personalised cancer treatment (English news story on sciencenorway.no).
The Department of Physics and Technology at UiT The Arctic University of Norway is pleased to announce 4 exciting PhD Fellowships within machine learning at SFI Visual Intelligence. Application deadline: June 17th.
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Norsk forskning har utviklet kunstig intelligens som raskt kan analysere lungekreft. Ekspertene forklarer hvordan dette kan bidra til en mer effektiv og persontilpasset behandling (Norwegian news article in vg.no)
We invite submissions presenting new and original research on all aspects of Deep Learning for the Northern Lights Deep Learning (NLDL) Conference 2026.
Partiet Sentrum er blant dem som tar grepet. – Ganske smart, mener valgforsker (Norwegian news article in TV2.no)
Visual Intelligence will be well presented at ICML 2025—one of the leading international academic conference in machine learning—with four newly accepted research papers.
After four years of operation, an international AI expert panel was appointed to assess Visual Intelligence's progress and results. The evaluation was characterized by its excellent remarks on the centre's scientific quality and innovation output.
Senior Researcher Fredrik Dahl recently gave a talk about Norsk Regnesentral's work on developing AI algorithms for automatic analysis of image quality and cancer detection at Norsk Radiografforbund's mammography symposium in Oslo.
Han høster anerkjennelse for sitt forskningsarbeid innen utvikling av mer effektive KI-modeller (Norwegian news article in Nordlys).
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Congratulations to associate professor Ali Ramezani-Kebrya for receiving a prestigious FRIPRO grant from the Research Council of Norway!
KI har en tendens til å behandle menn og kvinner ulikt. Ekspert mener teknologien reproduserer gamle fordommer (Norwegian news story in NRK.no)