Theodoros Damoulas
- Ecological Modeling top 5%
- Species Distribution and Climate Change 4
- Atmospheric Science top 5%
- Developmental Biology top 10%
- Global and Planetary Change top 10%
- Ecology top 10%
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- Gaussian Processes and Bayesian Inference 8
- Data Stream Mining Techniques 4
- Bayesian Methods and Mixture Models 3
- Machine Learning and Algorithms 2
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- Machine Learning in Bioinformatics 3
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- Data Management and Algorithms 3
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- Mobile Crowdsensing and Crowdsourcing 3
- Co-authors
- Mark GirolamiSarah KnightRichard G. PearsonSteven J. PhillipsS. J. GoetzPieter S. A. BeckM. M. LorantyIoannis Psorakis
- Journals
- Applied Energy (2 papers)AI Magazine (2 papers)IEEE Transactions on Visualization and Computer Graphics (1 paper)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
Theodoros Damoulas
35 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 125
- Ecological Modeling 167
- Atmospheric Science 451
- Developmental Biology 26
- Global and Planetary Change 232
- Ecology 268
Countries citing papers authored by Theodoros Damoulas
This map shows the geographic impact of Theodoros Damoulas'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 Theodoros Damoulas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Theodoros Damoulas more than expected).
Fields of papers citing papers by Theodoros Damoulas
This network shows the impact of papers produced by Theodoros Damoulas. 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 Theodoros Damoulas. The network helps show where Theodoros Damoulas may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Theodoros Damoulas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 9 | |
| 2 | 2023 | 1 | |
| 3 | 2023 | 9 | |
| 4 | 2022 | 30 | |
| 5 | Distribution Regression for Sequential Data | 2021 | 0 |
| 6 | Generalised Bayesian Filtering via Sequential Monte Carlo | 2020 | 3 |
| 7 | Non-separable non-stationary random fields | 2020 | 2 |
| 8 | Multi-task causal learning with Gaussian processes | 2020 | 1 |
| 9 | Generalized Variational Inference. | 2019 | 8 |
| 10 | Online transfer learning for concept drifting data streams | 2019 | 3 |
| 11 | Multi-resolution Multi-task Gaussian Processes | 2019 | 1 |
| 12 | Spatio-temporal Bayesian on-line changepoint detection with model selection | 2018 | 2 |
| 13 | 2018 | 10 | |
| 14 | Combining heterogeneous user generated data to sense well-being | 2016 | 10 |
| 15 | Mining 911 Calls in New York City: Temporal Patterns, Detection and Forecasting | 2015 | 11 |
| 16 | 2014 | 38 | |
| 17 | 2012 | 21 | |
| 18 | 2011 | 11 | |
| 19 | 2010 | 102 | |
| 20 | 2008 | 26 |
About Theodoros Damoulas
Theodoros Damoulas is a scholar working on Ecological Modeling, Statistics and Probability, Signal Processing, Computer Science Applications and Artificial Intelligence, having authored 37 papers that have together received 1.3k indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (8 papers), Species Distribution and Climate Change (4 papers), Data Stream Mining Techniques (4 papers), Machine Learning in Bioinformatics (3 papers), Bayesian Methods and Mixture Models (3 papers), Data Management and Algorithms (3 papers), Mobile Crowdsensing and Crowdsourcing (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Ecological Modeling (167 citations), Atmospheric Science (451 citations), Developmental Biology (26 citations), Global and Planetary Change (232 citations) and Ecology (268 citations). Theodoros Damoulas has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Mark Girolami, Sarah Knight, Richard G. Pearson, Steven J. Phillips, S. J. Goetz, Pieter S. A. Beck, M. M. Loranty, Ioannis Psorakis, Daniel Fink and Harish Doraiswamy. Their work appears in journals such as Applied Energy, AI Magazine, IEEE Transactions on Visualization and Computer Graphics, Nature Climate Change and IEEE Access.
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.