Nicolas Schilling
- Artificial Intelligence top 5%
- Signal Processing top 2%
- Economics and Econometrics top 10%
- Computational Theory and Mathematics top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Lars Schmidt-ThiemeMartin WistubaJosif GrabockaNghia Duong‐TrungMit ShahM. JungheimM. PtokLucas Drumond
- Topics
- Advanced Multi-Objective Optimization Algorithms (7 papers)Machine Learning and Data Classification (7 papers)Metaheuristic Optimization Algorithms Research (5 papers)
- Partner nations
- Germany
In The Last Decade
Nicolas Schilling
15 papers receiving 480 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 308
- Signal Processing 289
- Economics and Econometrics 91
- Computational Theory and Mathematics 40
- Computer Vision and Pattern Recognition 39
Countries citing papers authored by Nicolas Schilling
This map shows the geographic impact of Nicolas Schilling's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicolas Schilling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Schilling more than expected).
Fields of papers citing papers by Nicolas Schilling
This network shows the impact of papers produced by Nicolas Schilling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicolas Schilling. The network helps show where Nicolas Schilling may publish in the future.
Co-authorship network of co-authors of Nicolas Schilling
This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Schilling. A scholar is included among the top collaborators of Nicolas Schilling based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nicolas Schilling. Nicolas Schilling is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | Finding Hierarchy of Topics from Twitter Data. | 1 |
| 4 | 50 | |
| 5 | 3 | |
| 6 | Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream. | 1 |
| 7 | 21 | |
| 8 | 20 | |
| 9 | 14 | |
| 10 | 22 | |
| 11 | 33 | |
| 12 | 13 | |
| 13 | 34 | |
| 14 | Learning data set similarities for hyperparameter optimization initializations | 2 |
| 15 | 4 | |
| 16 | Learning time-series shapeletsbreakdown → | 275 |
About Nicolas Schilling
Nicolas Schilling is a scholar working on Computational Theory and Mathematics, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 16 papers that have together received 494 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (7 papers), Machine Learning and Data Classification (7 papers) and Metaheuristic Optimization Algorithms Research (5 papers). The work is most often cited by research in Signal Processing (289 citations), Artificial Intelligence (308 citations) and Computational Mathematics (2 citations). Nicolas Schilling has collaborated with scholars based in Germany. Frequent co-authors include Lars Schmidt-Thieme, Martin Wistuba, Josif Grabocka, Nghia Duong‐Trung, Mit Shah, M. Jungheim, M. Ptok and Lucas Drumond. Their work appears in journals such as Physiology & Behavior, Machine Learning and ACM Transactions on Knowledge Discovery from Data.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.