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.
The fifth Visual Intelligence annual report, showcasing the centre's activities, results, staff, funding and publications for 2024, is now available on our web pages.
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I juni møtes det norske KI-miljøet i Tromsø for å presentere ny forskning og diskutere nye retninger innen feltet. KI-forskere inviteres til å delta og vise fram forskningen sin under konferansen.
Visual Intelligence and the Digital Innovation Lab invited industry professionals to present ideas for master's projects to computer science and machine learning students at UiT The Arctic University of Norway.
Kunstig intelligens (KI) endrer måten vi løser komplekse problemer på. Ved UiT Norges arktiske universitet leder professor Robert Jenssen Visual Intelligence, et senter for forskningsdrevet innovasjon som utvikler neste generasjons KI-metoder.
Visual Intelligence researchers had the great pleasure of talking to high school students about the AI study programme at UiT The Arctic University of Norway during the UiT Open Day.
Director Robert Jenssen represented Visual Intelligence at the CuttingEdgeAI seminar "KI anno 2023: I offentlighetens interesse?" at the University of Bergen on March 7th.
Det hender at kunstig intelligens behandler menn og kvinner ulikt. Hvordan skjer dette? KI-forsker Elisabeth Wetzer forklarer hva som ligger bak skjevhetene i teknologien (Popular science story in forskning.no and sciencenorway.no)
Congratulations to Visual Intelligence board member Viktoria Gulliksen Stray for being featured in Abelia and ODA-Nettverk's list of Top 50 Influential Tech Women in Norway 2025!
Norwegian Minister of Research and Higher Education, Sigrun Aasland (Ap) got to hear about Visual Intelligence's work toward developing the next generation of deep learning methodology at a visit to UiT in Tromsø
Hva er lov og hva er ikke lov? Det er blant spørsmålene studentene stiller seg. (NRK.no story featuring director Robert Jenssen)
We had the pleasure of talking to students about the exciting career opportunities at Visual Intelligence and UiT The Arctic University of Norway during TEKdagen 2025
Last week, we welcomed students from Breivang upper secondary school to a full-day practical session on AI and programming at UiT The Arctic University of Norway
We happily welcome Solveig Thrun, who recently started as a doctoral research fellow at Visual Intelligence in Tromsø in January 2025.
Visual Intelligence and UiT researchers organized a special curricular course on collaborative coding and reproducible research at UiT The Arctic University of Norway.
Andrew McDonald, a PhD student from University of Cambridge and the British Antarctic Survey, presented his work on sea ice forecasting with diffusion models, as well as the downstream applications of those forecasts, at Visual Intelligence in Tromsø.
Deep learning researchers from 27 different countries congregated at the 8th edition of the NLDL conference. - I have seen several interesting posters and talks that have opened my mind about different paths to explore in my own research, says NLDL 2025 attendee Tiago Ramos.
NRK, the national broadcasting company in Norway, spoke to Visual Intelligence researchers Robert Jenssen, Suaiba Amina Salahuddin, Michael Kampffmeyer and Elisabeth Wetzer about this year's Northern Lights Deep Learning Conference in Tromsø, Norway.
Andre uka i januar samles forskere fra hele verden i Tromsø for å dele og lære om siste nytt innen forskning på kunstig intelligens.
In record time, principal investigator Michael Kampffmeyer became a professor in machine learning at the young age of 32. Read more about his academic journey and work in the latest UiT Researcher Portrait
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2024 has been a year full of exciting events and accomplishments, and we look forward to continuing our journey on researching the next generation of deep learning methodology in 2025!