Andreas Damianou

2.5k total citations · 1 hit paper
18 papers, 645 citations indexed

About

Andreas Damianou is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Andreas Damianou has authored 18 papers receiving a total of 645 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 7 papers in Control and Systems Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Andreas Damianou's work include Gaussian Processes and Bayesian Inference (8 papers), Control Systems and Identification (5 papers) and Recommender Systems and Techniques (3 papers). Andreas Damianou is often cited by papers focused on Gaussian Processes and Bayesian Inference (8 papers), Control Systems and Identification (5 papers) and Recommender Systems and Techniques (3 papers). Andreas Damianou collaborates with scholars based in United Kingdom, United States and Netherlands. Andreas Damianou's co-authors include Neil D. Lawrence, Paris Perdikaris, George Em Karniadakis, Maziar Raissi, Michalis K. Titsias, Manik Varma, Ashish Kapoor, Deepak Vasisht, Tony J. Prescott and Zhenwen Dai and has published in prestigious journals such as Philosophical Transactions of the Royal Society B Biological Sciences, Journal of Machine Learning Research and Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences.

In The Last Decade

Andreas Damianou

17 papers receiving 627 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling

2017 279 citations Paris Perdikaris, Maziar Raissi et al. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences profile →

Peers

Countries citing papers authored by Andreas Damianou

Since Specialization

Citations

This map shows the geographic impact of Andreas Damianou'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 Andreas Damianou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Damianou more than expected).

Fields of papers citing papers by Andreas Damianou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andreas Damianou. 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 Andreas Damianou. The network helps show where Andreas Damianou may publish in the future.

Co-authorship network of co-authors of Andreas Damianou

This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Damianou. A scholar is included among the top collaborators of Andreas Damianou 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 Andreas Damianou. Andreas Damianou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

#	Work	Indexed citations
1	Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models 2025 ·Konstantina Palla, (unknown), Claudia Hauff, (unknown), Andreas Damianou, (unknown), (unknown), Mounia Lalmas	1
2	Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks 2024 ·Marco De Nadai, (unknown), (unknown), Alice Wang, (unknown), Fabrizio Silvestri, (unknown), (unknown), Vladan Radosavljević, (unknown), (unknown), Hugues Bouchard, Mounia Lalmas, Andreas Damianou	7
3	Towards Graph Foundation Models for Personalization 2024 ·Andreas Damianou, (unknown), (unknown), Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas	1
4	Graph Learning for Exploratory Query Suggestions in an Instant Search System 2023 ·IRIS Research product catalog (Sapienza University of Rome) ·Enrico Palumbo, Andreas Damianou, Alice Wang, (unknown), (unknown), (unknown), (unknown), Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, (unknown)	1
5	Online Constrained Model-based Reinforcement Learning 2020 ·arXiv (Cornell University) ·(unknown), Andreas Damianou, Benjamin Rosman	3
6	Memory and mental time travel in humans and social robots 2019 ·Philosophical Transactions of the Royal Society B Biological Sciences ·Tony J. Prescott, (unknown), Uriel Martínez-Hernández, Andreas Damianou, Neil D. Lawrence	25
7	DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self 2017 ·IEEE Transactions on Cognitive and Developmental Systems ·Clément Moulin-Frier, Tobias Fischer, (unknown), (unknown), (unknown), Ugo Pattacini, (unknown), (unknown), (unknown), Matej Hoffmann, Hyung Jin Chang, (unknown), (unknown), Andreas Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure	52
8	Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling breakdown → 2017 ·Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences ·Paris Perdikaris, Maziar Raissi, Andreas Damianou, Neil D. Lawrence, George Em Karniadakis	279
9	Deep recurrent Gaussian processes for outlier-robust system identification 2017 ·Journal of Process Control ·César Mattos, Zhenwen Dai, Andreas Damianou, Guilherme A. Barreto, Neil D. Lawrence	20
10	Variational inference for latent variables and uncertain inputs in Gaussian processes 2016 ·Journal of Machine Learning Research ·Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence	66
11	Probabilistic consolidation of grasp experience 2016 ·Explore Bristol Research ·Yasemin Bekiroglu, Andreas Damianou, Renaud Detry, Johannes A. Stork, Danica Kragić, Carl Henrik Ek	9
12	Variational auto-encoded deep Gaussian processes 2016 ·Apollo (University of Cambridge) ·Zhenwen Dai, Andreas Damianou, Javier González, Neil D. Lawrence	26
13	An integrated probabilistic framework for robot perception, learning and memory 2016 ·Pure (University of Bath) ·Uriel Martínez-Hernández, Andreas Damianou, (unknown), Luke Boorman, Neil D. Lawrence, Tony J. Prescott	23
14	Latent Autoregressive Gaussian Processes Models for Robust System Identification 2016 ·IFAC-PapersOnLine ·César Mattos, Andreas Damianou, Guilherme A. Barreto, Neil D. Lawrence	11
15	Active learning for sparse bayesian multilabel classification 2014 ·Deepak Vasisht, Andreas Damianou, Manik Varma, Ashish Kapoor	52
16	Factorized topic models 2013 ·KTH Publication Database DiVA (KTH Royal Institute of Technology) ·Cheng Zhang, Carl Henrik Ek, Andreas Damianou, Hedvig Kjellström	2
17	Manifold Relevance Determination 2012 ·arXiv (Cornell University) ·Andreas Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence	36
18	Variational Gaussian Process Dynamical Systems 2011 ·arXiv (Cornell University) ·Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence	31

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.

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