English version
Hugo Lewi Hammer

Hugo Lewi Hammer

Kort om

Hammer har en master i industriell matematikk fra 2003 og en doktorgrad i beregningsorientert statistikk fra 2008. Hans forskningsinteresser er blant annet innen forbedring av pålitelighet og transparens av maskinlæring-, forsterkende læring- og dype lærings-modeller ved utvikling av metoder innen modelltolkning, usikkerhetskvantifisering, robust statistikk og kausal inferens.

Fagområder

Vitenskapsdisipliner

Statistikk

Emner

Bayesiansk statistikk   Regresjon   Kunstig intelligens   Maskinlæring   Anvendt statistikk   Stokastisk simulering   Big Data

Land

Norge

Forskningsprosjekter

Vitenskapelige publikasjoner

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; Mello, Gustavo Borges Moreno E; Lind, Pedro (Red.). 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

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

Thambawita, Vajira L B; Hicks, Steven; Storås, Andrea; Andersen, Jorunn Marie ; Witczak, Oliwia ; Haugen, Trine B. ; Hammer, Hugo Lewi ; Nguyen, Thu; Halvorsen, Pål ; Riegler, Michael Alexander (2023). Medico Multimedia Task at MediaEval 2022: Transparent Tracking of Spermatozoa. CEUR Workshop Proceedings.
https://2022.multimediaeval.com/paper5501.pdf

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 (Red.). Recent Challenges in Intelligent Information and Database Systems. Springer.
https://doi.org/10.1007/978-3-031-42430-4_21

Hammer, Hugo Lewi ; Riegler, Michael ; Tjelmeland, Håkon (2023). Approximate Bayesian Inference Based on Expected Evaluations. 22 s. Bayesian Analysis.
https://doi.org/10.1214/23-BA1368

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 s. Big Data and Cognitive Computing. Vol. 7.
https://doi.org/10.3390/bdcc7020091

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 s. IEEE Access. Vol. 11.
https://doi.org/10.1109/ACCESS.2023.3279720

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 (Red.). Proceedings of the 2023 36th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS). IEEE conference proceedings.
https://hdl.handle.net/10037/31697

Thunold, Håvard Horgen; Riegler, Michael ; Yazidi, Anis ; Hammer, Hugo Lewi (2023). A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering. Diagnostics (Basel). Vol. 13.
https://doi.org/10.3390/diagnostics13223413

Tveitstøl, Thomas; Tveter, Mats; Pérez T, Ana S.; Hatlestad-Hall, Christoffer; Yazidi, Anis ; Hammer, Hugo Lewi ; Hebold Haraldsen, Ira R. J. (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





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