Demo Data from SIGIR 2026 / reviewers in the wild / expert
Lakesh Kansakar
dblp:133/1936
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0003-0511-4202ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 1
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
2 papers |
Kernel, tree and ensemble methods · 38% Deep learning architectures and training · 38% Learning paradigms · 25% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Environmental and earth informatics · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Kernel, tree and ensemble methods
ensemble learning |
0.2 | 1 | 2016 | Semi-supervised combination of experts for aerosol optical depth estimation · Artif. Intell. 2016 |
Machine learning › Deep learning architectures and training
mixture of experts |
0.2 | 1 | 2016 | Semi-supervised combination of experts for aerosol optical depth estimation · Artif. Intell. 2016 |
Machine learning › Learning paradigms
semi-supervised learning |
0.2 | 1 | 2013 | Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments · IJCAI 2013 |
Environmental and earth informatics
remote sensing |
0.2 | 1 | 2013 | Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments · IJCAI 2013 |
Environmental and earth informatics › remote sensing
atmospheric remote sensing |
0.1 | 1 | 2016 | Semi-supervised combination of experts for aerosol optical depth estimation · Artif. Intell. 2016 |
Methods — techniques the papers use, named apart from their topics
semi-supervised learning · 0.3satellite data fusion · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Semi-supervised combination of experts for aerosol optical depth estimation
Nemanja Djuric, Lakesh Kansakar, Slobodan Vucetic |
Artif. Intell. | 2 |
| 2013 | Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments
Nemanja Djuric, Lakesh Kansakar, Slobodan Vucetic |
IJCAI | 2 |