Alise Danielle Midtfjord

Doctoral Research Fellow - Risk and Stochastics

$Image of Alise Danielle Midtfjord$

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Email alisedm@math.uio.no

Mobile phone +47 93427423

Room 1005

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Visiting address Moltke Moes vei 35 Niels Henrik Abels hus 0851 OSLO

Postal address Postboks 1053 Blindern 0316 OSLO

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Academic interests

Machine learning and Data Science
Explainable Artificial Intelligence
Applications within risk and safety

Background

I did my Master of Science at the Norwegian University of Life Sciences (NMBU), in Environmental Physics. In my thesis, I combined machine learning with image analysis of PET/CT-images of cancer tumors, to predict the treatment outcome. After that, I worked with technology consulting for Accenture, working with new technologies like machine learning and extended reality. Since August 2019, I have been a PhD Student at the Department of Mathematics at the University of Oslo. Here I am working on modelling and analysis of multidimensional high-resolution environmental data with application to airport runway condition management.

Tags: Risk, Statistics, Machine Learning, data science

Publications

Other

Midtfjord, Alise Danielle (2020). Explainable Artificial Intelligence (XAI). Show summary
As the use of “black box” algorithms such as deep learning increases rapidly, several challenges related to ethics and law arise. Explainable Artificial Intelligence (XAI) addresses these issues by creating understandable explanations of how and why AI systems arrive at their decisions, and we will try out some of these methods during this workshop.
Midtfjord, Alise Danielle (2020). Explainable Artificial Intelligence: How to make AI responsible. Show summary
As the use of “black box” algorithms such as deep learning increases rapidly, several challenges related to ethics and law arise. These systems are not capable of explaining their decisions and actions to human users, making it difficult to make sure they follow the laws are morals of our society. Explainable Artificial Intelligence (XAI) addresses these issues by creating understandable explanations of how and why AI systems arrive at their decisions. In this presentation you will learn about some of the moral challenges rising with the use of artificial intelligence, what explainable artificial intelligence is and how it can address a lot of these challenges and contribute to creating trustworthy and responsible AI.
Grøndahl, Aurora Rosvoll; Midtfjord, Alise Danielle; Langberg, Geir Severin Rakh Elvatun; Tomic, Oliver; Indahl, Ulf Geir; Knudtsen, Ingerid Skjei; Malinen, Eirik; Dale, Einar & Futsæther, Cecilia Marie (2019). Prediction of treatment outcome for head and neck cancers using radiomics of PET/CT images. Radiotherapy and Oncology. ISSN 0167-8140. 133, s 526- 526
Langberg, Geir Severin; Grøndahl, Aurora Rosvoll; Midtfjord, Alise Danielle; Tomic, Oliver; Liland, Kristian Hovde; Knudtsen, Ingerid Skjei; Dale, Einar; Malinen, Eirik & Futsæther, Cecilia Marie (2019). Establishing a complete radiomics framework for biomarker identification and outcome prediction using PET/CT images of head & neck cancers.

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Published Nov. 20, 2019 1:48 PM - Last modified Jan. 17, 2020 11:00 AM

Alise Danielle Midtfjord

Academic interests

Background

Publications

Research groups

Visiting address

Mail address

Phone and e-mail