Demo Data from EDBT 2026 / reviewers in the wild / expert
Øystein Sørensen
dblp:220/0764
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-0724-3542ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Reinforcement learning · 50% Probabilistic and Bayesian machine learning · 50% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › structured prediction › ranking model
mallows model |
0.3 | 1 | 2017 | Probabilistic preference learning with the Mallows rank model · J. Mach. Learn. Res. 2017 |
Machine learning › Reinforcement learning
preference learning |
0.3 | 1 | 2017 | Probabilistic preference learning with the Mallows rank model · J. Mach. Learn. Res. 2017 |
Algorithmic game theory and mechanism design › social choice
rank aggregation |
0.1 | 1 | 2017 | Probabilistic preference learning with the Mallows rank model · J. Mach. Learn. Res. 2017 |
Methods — techniques the papers use, named apart from their topics
mallows model · 0.6bayesian inference · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progressionabstractOur understanding of how speed and persistence of cell migration affects the growth rate and size of tumors remains incomplete. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation, a phenomenon often linked to alterations of cell migration. Golgi fragmentation was induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the experimentally obtained migration and invasion traits of Giantin depleted breast cancer cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression. Salim Ghannoum, Damiano Fantini, Muhammad Zahoor, Veronika Reiterer, Santosh Phuyal, Waldir Leoncio Netto, Øystein Sørensen, Arvind Iyer, Debarka Sengupta, Lina Prasmickaite, Gunhild Mari Mælandsmo, Alvaro Köhn-Luque, Hesso Farhan |
PLoS Comput. Biol. | 7 |
| 2017 | Probabilistic preference learning with the Mallows rank model
Valeria Vitelli, Øystein Sørensen, Marta Crispino, Arnoldo Frigessi, Elja Arjas |
J. Mach. Learn. Res. | 2 |