Associate professor Kristoffer Wickstrøm
Image:
Petter Bjørklund/SFI Visual Intelligence

Associate professor Kristoffer Wickstrøm

Best Paper Award to associate professor Kristoffer Wickstrøm

Congratulations to Kristoffer Wickstrøm for receiving the Best Paper Award 2024 from the journal Pattern Recognition Letters!

Best Paper Award 2024 to associate professor Kristoffer Wickstrøm

Congratulations to Kristoffer Wickstrøm for receiving the Best Paper Award 2024 from the journal Pattern Recognition Letters!

By Petter Bjørklund, Communication Advisor at SFI Visual Intelligence

- Such a recognition is very exciting and a clear sign that the work we do is of high quality, says Wickstrøm.

He is an associate professor at SFI Visual Intelligence and UiT Machine Learning Group. He published a research article in 2022 in the academic journal Pattern Recognition Letters.

Since then, it has been cited more than a hundred times. This milestone has earned him a prestigious award for best research paper of 2024.

Pattern Recognition Letters is a renowned academic journal within AI and image analysis. The journal is reputed for its high quality publications.

- The fact that this article is now in this excellent company is fantastic, especially considering the tough competition from so many other strong contributions, says Wickstrøm.

Makes it easier to "train" AI

The data used to train AI often contain labels which describe the data's contents. Wickstrøm's research involved training AI to analyze so-called "time series" without using labeled data.

A time series is a form of data which describes how something develops over time. How many steps you walk each day, how many times your heart beats each minute, and how long you sleep each night are examples of such data.

Such data contain a lot of valuable information. For instance, analyzing number of heartbeats per minute can reveal potential symptoms of heart diseases.

But there are a lot different forms of time series. These variations make it challenging to develop AI systems that perform well on different time series.

- This means that the program has to be tailored to a particular type of time series. This makes developing more general AI systems difficult and time-consuming, Wickstrøm explains.

In the article, Wickstrøm developed a software that performs well, regardless of which time series it is analyzing.

- We tested this technology on 100 different time series, with excellent results. Our method has the potential to considerably improve analysis of such data, says Wickstrøm.

The researchers also conducted an experiment on time series data within health and medicine, such as patients' heart rhythm and blood values. The results show that the method can make analyzing such data much simpler.

- The experiment showed that our method can reduce the amount of training data needed to learn AI how to analyze health-related data, such as patients' heart rhythm, says Wickstrøm

Wickstrøm's award and research was mentioned in the newspaper Khrono.

Reference:

Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen: Mixing up contrastive learning: Self-supervised representation learning for time series. Pattern Recognition Letters, Volume 155, 2022.

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