Thomas Heinis

2.7k total citations · 1 hit paper
69 papers, 644 citations indexed

About

Thomas Heinis is a scholar working on Computer Networks and Communications, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Thomas Heinis has authored 69 papers receiving a total of 644 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Networks and Communications, 26 papers in Signal Processing and 21 papers in Artificial Intelligence. Recurrent topics in Thomas Heinis's work include Data Management and Algorithms (25 papers), Advanced Database Systems and Queries (13 papers) and Distributed and Parallel Computing Systems (10 papers). Thomas Heinis is often cited by papers focused on Data Management and Algorithms (25 papers), Advanced Database Systems and Queries (13 papers) and Distributed and Parallel Computing Systems (10 papers). Thomas Heinis collaborates with scholars based in United Kingdom, Switzerland and France. Thomas Heinis's co-authors include Gustavo Alonso, Cesare Pautasso, Anastasia Ailamaki, Farhan Tauheed, Jian Pei, Felix Schürmann, Ioannis Alagiannis, Henry Markram, Panagiotis Karras and Stéphane Bressan and has published in prestigious journals such as Nature Communications, PLoS ONE and IEEE Transactions on Communications.

In The Last Decade

Thomas Heinis

62 papers receiving 605 citations

Hit Papers

The prospect of artificial intelligence to personalize as... 2024 2026 2025 2024 10 20 30 40

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thomas Heinis United Kingdom 14 301 261 186 147 138 69 644
Verena Kantere Greece 11 327 1.1× 271 1.0× 133 0.7× 54 0.4× 123 0.9× 69 523
Brian McBride United States 5 240 0.8× 319 1.2× 60 0.3× 64 0.4× 430 3.1× 11 628
Martín Rezk Italy 10 209 0.7× 165 0.6× 82 0.4× 56 0.4× 464 3.4× 16 561
Nodira Khoussainova United States 10 315 1.0× 234 0.9× 196 1.1× 57 0.4× 216 1.6× 13 569
Mariano Rodríguez-Muro Italy 12 363 1.2× 270 1.0× 127 0.7× 60 0.4× 667 4.8× 25 738
Ana Carolina Salgado Brazil 13 188 0.6× 157 0.6× 107 0.6× 20 0.1× 179 1.3× 75 423
Anastasia Dimou Belgium 10 132 0.4× 170 0.7× 49 0.3× 96 0.7× 363 2.6× 67 534
Panos Constantopoulos Greece 14 127 0.4× 225 0.9× 62 0.3× 44 0.3× 263 1.9× 57 577
Manasi Vartak United States 12 180 0.6× 212 0.8× 175 0.9× 102 0.7× 366 2.7× 19 679
Francesco Guerra Italy 13 210 0.7× 215 0.8× 127 0.7× 30 0.2× 349 2.5× 76 535

Countries citing papers authored by Thomas Heinis

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Heinis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Heinis

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

All Works

20 of 20 papers shown
1.
Nelson, Scott M., Artur Akbarov, Rehan Salim, et al.. (2025). Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception. Nature Communications. 16(1). 296–296. 4 indexed citations
2.
Juzėnas, Simonas, Gediminas Alzbutas, Pierre‐Yves Burgi, et al.. (2025). High-performance protocol for ultra-short DNA sequencing using Oxford Nanopore Technology (ONT). PLoS ONE. 20(4). e0318040–e0318040. 1 indexed citations
3.
Heinis, Thomas, et al.. (2025). A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach. Proceedings of the ACM on Management of Data. 3(3). 1–26.
4.
Laponogov, Ivan, Michael M. Bronstein, Alexander Comninos, et al.. (2025). Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning. PubMed. 3(1). 68–68.
6.
Abbara, Ali, Margaritis Voliotis, Krasimira Tsaneva‐Atanasova, et al.. (2024). The prospect of artificial intelligence to personalize assisted reproductive technology. npj Digital Medicine. 7(1). 55–55. 44 indexed citations breakdown →
7.
Abbara, Ali, Sophie Adams, Maria Phylactou, et al.. (2023). Quantifying the variability in the assessment of reproductive hormone levels. Fertility and Sterility. 121(2). 334–345. 2 indexed citations
8.
Heinis, Thomas, et al.. (2023). Optimization towards Efficiency and Stateful of dispel4py. St Andrews Research Repository (St Andrews Research Repository). 2021–2032. 1 indexed citations
9.
Steed, Anthony, et al.. (2022). VR Toolkit for Identifying Group Characteristics. University of Birmingham Research Portal (University of Birmingham). 6. 1–1.
10.
Voliotis, Margaritis, et al.. (2022). Quantitative approaches in clinical reproductive endocrinology. Current Opinion in Endocrine and Metabolic Research. 27. 100421–100421. 4 indexed citations
11.
Heinis, Thomas, et al.. (2020). MADEX: Learning-augmented Algorithmic Index Structures.. Very Large Data Bases. 3 indexed citations
12.
Kumar, Ankit, et al.. (2020). Hands-off Model Integration in Spatial Index Structures.. Very Large Data Bases. 2 indexed citations
13.
Heinis, Thomas, et al.. (2018). Towards Batch-Processing on Cold Storage Devices. 134–139. 1 indexed citations
14.
Heinis, Thomas & Anastasia Ailamaki. (2017). Data Infrastructure for Medical Research. Spiral (Imperial College London). 8(3). 131–238. 2 indexed citations
15.
Guitton, Florian, et al.. (2017). eTRIKS analytical environment: A modular high performance framework for medical data analysis. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1. 353–360. 6 indexed citations
16.
Karpathiotakis, Manos, Ioannis Alagiannis, Thomas Heinis, Miguel Castelo‐Branco, & Anastasia Ailamaki. (2015). Just-In-Time Data Virtualization: Lightweight Data Management with ViDa. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 29 indexed citations
17.
Heinis, Thomas, Miguel Castelo‐Branco, Ioannis Alagiannis, et al.. (2011). Challenges and Opportunities in Self-Managing Scientific Databases. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 34(4). 44–52. 4 indexed citations
18.
Heinis, Thomas & Gustavo Alonso. (2008). Efficient lineage tracking for scientific workflows. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1007–1018. 90 indexed citations
19.
Pautasso, Cesare, Thomas Heinis, & Gustavo Alonso. (2006). Autonomic resource provisioning for software business processes. Information and Software Technology. 49(1). 65–80. 17 indexed citations
20.
Lüthy, Christoph, et al.. (2001). 7‐(4,6‐Dimethoxypyrimidinyl)oxy‐ and ‐thiophthalides as novel herbicides: Part 1. CGA 279 233: a new grass‐killer for rice. Pest Management Science. 57(3). 205–224. 23 indexed citations

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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