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.
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!
In a Dagens Medisin op-ed, SPKI director Karl Øyvind Mikalsen, Kristine Bø, Bjorn Anton Graff and Kurt Vanvik discuss how interregional collaborations in Norway now contributes to the implementation of AI radiology solutions across the Norwegian health regions
Jenssen has established several important collaborations between Visual Intelligence and international research environments within artificial intelligence. He has now joined the scientific advisory committee at Europe's most prominent research centre within intelligent systems.
2024 marks the research centre's fourth year of researching the next generation of deep learning methodology for extracting knowledge from complex image data. We look back at various innovation highlights achieved in the first half of Visual Intelligence's run.
The graph community in Tromsø gathered on November 27th for the northernmost "Learning on Graph" Conference meetup.
Centre director Robert Jenssen recently gave a talk about the use and benefits of AI technologies during Pasientsikkerhetskonferansen 2024
Congratulations to Kristoffer Wickstrøm for receiving the Best Paper Award 2024 from the journal Pattern Recognition Letters!
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Professor Michael Kampffmeyer gave a presentation titled "Kunstig intelligens som forklarer hva den tenker" as part of a Norwegian Centre for E-Health Research Webinar (Norwegian dialogue).
I teorien ja, men i praksis fungerer det ikke slik.
EUGLOH students from partner institutions under the EUGLOH alliance can now apply for an exclusive mobility scholarship for the NLDL 2025 Winter School which covers travel, accommodation, and sustenance for successful applicants.
Visual Intelligence researchers represented the centre during Inspirasjonsdagen 2024. The event aimed to stimulate high school students' interest and curiosity for STEM and healthcare-related study programmes at UiT The Arctic University of Norway.
Our summer students have worked on projects related to fatigue and stress recognition with machine learning as their first research experience. Their results were presented at Georg Sverdrups hus at University of Oslo on October 16th 2024.
PhD candidate Rwiddhi Chakraborty recently gave an invited research talk titled "Perspectives on Multimodal Reasoning" at the Pioneer Centre for AI at University of Copenhagen.
Visual Intelligence was represented at Frampeik 2024 by associate professor Elisabeth Wetzer. The event gathered student researchers at Verdensteatret in Tromsø for discussions around AI-related topics.
Registration for the Northern Lights Deep Learning Conference 2025 is now open. The general deadline for registration is January 1st 2025.