Winter School

NLDL 2024 Winter School

The PhD winter school will consist of tutorials by experts in the field and is co-hosted by NORA as part of the NORA Research School. A preliminary program will be provided below soon. 

Note: UiT The Arctic University of Norway will award 5 ECTS for the Winter School. In order to receive the credits, participants will have to register at UiT before 1st of November. It is also possible to attend the Winter School without obtaining the 5 ECTS credits. More information on this will be made available soon.

Course Description

Deep learning is a rapidly growing segment of machine learning. It is increasingly used to deliver near-human level accuracy for many tasks such as image classification, voice recognition and natural language processing. Applications areas include facial recognition, scene detection, advanced medical and pharmaceutical research, and autonomous, self-driving vehicles. 

This 5-day course is build upon tutorials on specific topics on deep learning from perspectives such as Causality, Generative Models, and Explainability. The course further encompasses among others keynote talks as well as special sessions on industry and diversity in AI as part of the NLDL conference program

To obtain credits, participants will be required to present an ongoing research project (poster presentation) as part of the winter school and complete a home exam afterwards.


Venue: TEO-H1 1.820-AUD1

Monday January 8th:


January 10-12th: Main Conference Program

Friday January 13th:


Tutorial Speakers

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.

Gitta Kutyniok completed her Diploma in Mathematics and Computer Science in 1996 at the Universität Paderborn in Germany. She was then employed as a Scientific Assistant and in 2000 received her Ph.D. degree in the area of time-frequency analysis from the same university. In 2001, she spent one term as a Visiting Assistant Professor at the Georgia Institute of Technology. After having returned to Germany, she accepted a position as a Scientific Assistant at the Justus-Liebig-Universität Giessen. In 2004, she was awarded a Research Fellowship by the DFG-German Research Foundation, with which she spend one year at Washington University in St. Louis and at the Georgia Institute of Technology. She then returned to Germany, completed her Habilitation in Mathematics in 2006 and received her venia legendi. In 2007 and 2008, being awarded one of the highly competitive “Heisenberg Fellowships” by the DFG-German Research Foundation, she spent half a year at each, Princeton University, Stanford University, and Yale University. After returning to Germany in October 2008, she became a full professor for Applied Analysis at the Universität Osnabrück. Gitta Kutyniok was awarded various prizes for both her teaching and research, among which were the “Weierstrass Prize for outstanding teaching of the Universität Paderborn” in 1998, the “Research Prize of the Universität Paderborn” in 2003 as well as the “Prize of the University Gießen” in 2006. Just recently, in 2007, she received the prestigious “von Kaven Prize” awarded annually by the DFG-German Research Foundation. 

Since 2007, she is an Associate Editor for the Journal of Wavelet Theory and Applications, and since 2009, she is a Corresponding Editor for Acta Applicandae Mathematicae. She was a panelist for the NSF in 2008 and serves as a reviewer for the NSF, GIF, NWO, WWTF as well as for over 30 journals. 

Her research interests include the areas of applied harmonic analysis, numerical analysis, and approximation theory, in particular, sparse approximations, compressed sensing, geometric multiscale analysis, sampling theory, time-frequency analysis, and frame theory with applications in signal and image processing.

Rogelio A. Mancisidor received his B.Sc. and M.Sc. in Finance from BI Norwegian Business School and his Ph.D. in Machine Learning from UiT The Arctic University of Norway in 2021. During his Ph.D., Rogelio developed different unsupervised, supervised and semi-supervised models using variational autoencoders. He is currently an assistant professor in the Department of Data Science and Analytics at BI Norwegian Business School, and his current research interests include deep generative models, Bayesian modeling, variational inference, multimodal learning and text analytics.