About
Hammer has a master in industrial mathematics from 2003 and a PhD in computational statistics from 2008. His main research interests are within improving reliability and transparency of machine learning, reinforcement learning and deep learning models by developing methods within model interpretation, uncertainty quantification, robust statistics and causal inference.
Fields of study
Academic disciplines
Computer technology Health service and health administration research
Subject areas
Bayesian statistics Regression Artificial intelligence Machine learning Applied statistics Stochastic simulation Big Data
Research groups
Research projects
Completed research projects
-
Artificial intelligence - a novel tool in assisted reproduction technology
The project aims to improve the methods for selecting sperm and embryos and increase the chance of pregnancy and live-born children.
Publications and research
Scientific publications
Papachrysos, Nikolaos; Smedsrud, Pia Helen; Ånonsen, Kim Vidar; Berstad, Tor Jan; Espeland, Håvard; Petlund, Andreas; Hedenström, Per;
Halvorsen, Pål
; Varkey, Jonas;
Hammer, Hugo Lewi
;
Riegler, Michael Alexander
; de Lange, Thomas
(2025).
A comparative study benchmarking colon polyp with computer-aided detection (CADe) software.
DEN Open.
https://doi.org/10.1002/deo2.70061
Tveter, Mats; Tveitstøl, Thomas; Hatlestad-Hall, Christoffer; Pérez Teseyra, Ana Silvina; Taubøll, Erik;
Yazidi, Anis
;
Hammer, Hugo Lewi
; Haraldsen, Ira Hebold
(2024).
Advancing EEG prediction with deep learning and uncertainty estimation.
Brain Informatics.
Vol. 11.
https://doi.org/10.1186/s40708-024-00239-6
Yazidi, Anis
;
Hammer, Hugo Lewi
; Leslie, David S.
(2024).
A Two-Timescale Learning Automata Solution to the Nonlinear Stochastic Proportional Polling Problem.
IEEE Transactions on Systems, Man & Cybernetics. Systems.
Vol. 54.
https://doi.org/10.1109/TSMC.2024.3414832
Boeker, Matthias; Swarbrick, Dana; Côté-Allard, Ulysse;
Riegler, Michael
;
Halvorsen, Pål
;
Hammer, Hugo Lewi
(2024).
Predictive Modelling of Muscle Fatigue in Climbing.
Lienhart, Rainer; Moeslund, Thomas B.; Saito, Hideo (Ed.).
MMSports '24: Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports. p. 7-15.
Association for Computing Machinery (ACM).
https://doi.org/10.1145/3689061.3689066
Sheshkal, Sajad Amouei; Gundersen, Morten Pensgård;
Riegler, Michael
; Utheim, Øygunn Aass; Gunnar Gundersen, Kjell; Rootwelt, Helge; Elgstøen, Katja B. Prestø;
Hammer, Hugo Lewi
(2024).
Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data.
Diagnostics (Basel).
Vol. 14.
https://doi.org/10.3390/diagnostics14232696
Storås, Andrea; Mæland, Steffen; Isaksen, Jonas L; Hicks, Steven Alexander; Thambawita, Vajira L B; Graff, Claus;
Hammer, Hugo Lewi
;
Halvorsen, Pål
;
Riegler, Michael Alexander
; Kanters, Jørgen K
(2024).
Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.
9 p.
JAMIA Journal of the American Medical Informatics Association.
Vol. 32.
https://doi.org/10.1093/jamia/ocae280
Tveter, Mats; Tveitstøl, Thomas;
Nygaard, Tønnes
; Pérez Teseyra, Ana Silvina; Kulashekhar, Shrikanth; Bruña, Ricardo;
Hammer, Hugo Lewi
; Hatlestad-Hall, Christoffer; Haraldsen, Ira Hebold
(2024).
EEG electrodes and where to find them: automated localization from 3D scans.
Journal of Neural Engineering.
Vol. 21.
https://doi.org/10.1088/1741-2552/ad7c7e
Vo, Minh-Quan; Nguyen, Thu;
Riegler, Michael
;
Hammer, Hugo Lewi
(2024).
Efficient Estimation of Generative Models Using Tukey Depth.
16 p.
Algorithms.
Vol. 17.
https://doi.org/10.3390/a17030120
Svennevik, Hanna; Hicks, Steven;
Riegler, Michael
; Storelvmo, Trude;
Hammer, Hugo Lewi
(2024).
A dataset for predicting cloud cover over Europe.
Scientific Data.
Vol. 11.
https://doi.org/10.1038/s41597-024-03062-0
Tveitstøl, Thomas; Tveter, Mats; Pérez Teseyra, Ana Silvina; Hatlestad-Hall, Christoffer;
Yazidi, Anis
;
Hammer, Hugo Lewi
; Haraldsen, Ira Hebold
(2023).
Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models.
Frontiers in Neuroinformatics.
Vol. 17.
https://doi.org/10.3389/fninf.2023.1272791
