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
Subject areas
Bayesian statistics Regression Artificial intelligence Machine learning Applied statistics Stochastic simulation Big Data
Countries
Research groups
Research projects
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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
Vo, Minh-Quan; Nguyen, Thu;
Riegler, Michael
;
Hammer, Hugo Lewi
(2024).
Efficient Estimation of Generative Models Using Tukey Depth.
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.
6 p.
Scientific Data.
Vol. 11.
https://doi.org/10.1038/s41597-024-03062-0
Storås, Andrea;
Riegler, Michael Alexander
;
Haugen, Trine B.
; Thambawita, Vajira L B; Hicks, Steven;
Hammer, Hugo Lewi
; Kakulavarapu, Radhika;
Halvorsen, Pål
; Stensen, Mette Haug
(2023).
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure.
Zouganeli, Evi; Yazidi, Anis; Borges Moreno e Mello, Gustavo; Lind, Pedro (Ed.).
Nordic Artificial Intelligence Research and Development: 4th Symposium of the Norwegian AI Society, NAIS 2022, Oslo, Norway, May 31-June 1, 2022, Revised Selected Papers.
Springer Nature.
https://doi.org/10.1007/978-3-031-17030-0_9
Boeker, Matthias; Jakobsen, Petter;
Riegler, Michael
; Stabell, Lena Antonsen; Fasmer, Ole Bernt;
Halvorsen, Pål
;
Hammer, Hugo Lewi
(2023).
Affect Recognition in Muscular Response Signals.
14 p.
IEEE Access.
Vol. 11.
https://doi.org/10.1109/ACCESS.2023.3279720
Sharma, Akriti; Ansari, Ayaz Z.; Kakulavarapu, Radhika; Stensen, Mette Haug;
Riegler, Michael
;
Hammer, Hugo Lewi
(2023).
Predicting Cell Cleavage Timings from Time-Lapse Videos of Human Embryos.
21 p.
Big Data and Cognitive Computing.
Vol. 7.
https://doi.org/10.3390/bdcc7020091
Hammer, Hugo Lewi
;
Riegler, Michael
; Tjelmeland, Håkon
(2023).
Approximate Bayesian Inference Based on Expected Evaluations.
22 p.
Bayesian Analysis.
https://doi.org/10.1214/23-BA1368
Sheshkal, Sajad Amouei;
Riegler, Michael
;
Hammer, Hugo Lewi
(2023).
ML-Peaks: Chip-seq peak detection pipeline using machine learning techniques.
Placidi, Giuseppe; González, Alejandro Rodríguez; Sicilia, Rosa; Spiliopoulou, Myra; Almeida, João Rafael; Andrades, José Alberto Benítez (Ed.).
Proceedings of the 2023 36th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS).
IEEE conference proceedings.
https://hdl.handle.net/10037/31697
Thambawita, Vajira L B; Hicks, Steven; Storås, Andrea; Nguyen, Thu;
Andersen, Jorunn Marie
;
Witczak, Oliwia
;
Haugen, Trine B.
;
Hammer, Hugo Lewi
;
Halvorsen, Pål
;
Riegler, Michael Alexander
(2023).
VISEM-Tracking, a human spermatozoa tracking dataset.
Scientific Data.
https://doi.org/10.1038/s41597-023-02173-4
Nguyen, Thu; Thien Ly, Hoang;
Riegler, Michael Alexander
;
Halvorsen, Pål
;
Hammer, Hugo Lewi
(2023).
Principal Components Analysis Based Frameworks for Efficient Missing Data Imputation Algorithms.
Nguyen, Ngoc-Thanh; Boonsang, Siridech; Fujita, Hamido; Hnatkowska, Bogumila; Hong, Tzung Pei; Pasupa, Kitsuchart (Ed.).
Recent Challenges in Intelligent Information and Database Systems.
Springer.
https://doi.org/10.1007/978-3-031-42430-4_21
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
