Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tobias Newmiwaka
dblp:295/2444
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 50% Computational science and engineering · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering › dynamical systems
dynamical systems reconstruction |
0.5 | 1 | 2021 | SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology · Bioinform. 2021 |
Bioinformatics and computational biology
systems biology |
0.5 | 1 | 2021 | SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
ordinary differential equation modeling · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biologyabstractSUMMARY: Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data. AVAILABILITY AND IMPLEMENTATION: For the R-package seeds, see the CRAN server https://cran.r-project.org/package=seeds. Tobias Newmiwaka, Benjamin Engelhardt, Philipp Wendland, Dominik Kahl, Holger Fröhlich, Maik Kschischo |
Bioinform. | 1 |