Tom Sercu

11.6k total citations · 2 hit papers
16 papers, 3.6k citations indexed

About

Tom Sercu is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Tom Sercu has authored 16 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Molecular Biology and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Tom Sercu's work include Machine Learning in Bioinformatics (5 papers), Speech and Audio Processing (3 papers) and Speech Recognition and Synthesis (3 papers). Tom Sercu is often cited by papers focused on Machine Learning in Bioinformatics (5 papers), Speech and Audio Processing (3 papers) and Speech Recognition and Synthesis (3 papers). Tom Sercu collaborates with scholars based in United States, Israel and France. Tom Sercu's co-authors include Alexander Rives, Zeming Lin, Robert Verkuil, Roshan Rao, Joshua Meier, Zhongkai Zhu, Halil Akin, Ori Kabeli, Allan dos Santos Costa and Salvatore Candido and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Biomedical Engineering.

In The Last Decade

Tom Sercu

16 papers receiving 3.5k 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.

Evolutionary-scale prediction of atomic-level protein structure with a language model

2023 2.0k citations Zeming Lin, Halil Akin et al. Science profile →
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

2021 1.4k citations Alexander Rives, Joshua Meier et al. Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tom Sercu United States	7	2.9k	586	388	329	257	16	3.6k
Gianluca Pollastri Ireland	30	3.5k 1.2×	831 1.4×	1.0k 2.6×	248 0.8×	94 0.4×	73	4.4k
Jianzhu Ma United States	27	2.2k 0.8×	489 0.8×	337 0.9×	309 0.9×	203 0.8×	69	3.1k
Jijun Tang China	42	4.7k 1.6×	976 1.7×	267 0.7×	503 1.5×	147 0.6×	231	5.8k
Alexander Rives United States	5	3.4k 1.2×	692 1.2×	388 1.0×	185 0.6×	256 1.0×	8	4.0k
Kimmen Sjölander United States	27	3.9k 1.4×	225 0.4×	409 1.1×	568 1.7×	56 0.2×	41	5.0k
Roshan Rao United States	6	1.7k 0.6×	346 0.6×	271 0.7×	121 0.4×	161 0.6×	11	2.1k
Hui Ding China	51	7.5k 2.6×	759 1.3×	161 0.4×	224 0.7×	157 0.6×	135	8.2k
Juergen Haas Germany	33	2.3k 0.8×	312 0.5×	410 1.1×	85 0.3×	133 0.5×	72	4.2k
Dong Xu China	26	2.4k 0.8×	368 0.6×	592 1.5×	78 0.2×	167 0.6×	89	3.8k
Maryam Fazel-Zarandi Canada	7	1.6k 0.6×	339 0.6×	261 0.7×	157 0.5×	139 0.5×	14	2.1k

Countries citing papers authored by Tom Sercu

Since Specialization

Citations

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

Fields of papers citing papers by Tom Sercu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tom Sercu

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

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

1.

Lin, Zeming, Halil Akin, Roshan Rao, et al.. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 379(6637). 1123–1130. 1968 indexed citations breakdown →

2.

Rives, Alexander, Joshua Meier, Tom Sercu, et al.. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences. 118(15). 1429 indexed citations breakdown →

3.

Das, Payel, Tom Sercu, Inkit Padhi, et al.. (2021). Author Correction: Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nature Biomedical Engineering. 5(8). 942–942. 6 indexed citations

4.

Rao, Roshan, Jason Liu, Robert Verkuil, et al.. (2021). MSA Transformer. 3 indexed citations

5.

Mroueh, Youssef, et al.. (2021). Improved Mutual Information Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(10). 9009–9017. 5 indexed citations

6.

Rao, Roshan, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, & Alexander Rives. (2021). Transformer protein language models are unsupervised structure learners. 2 indexed citations

7.

Melnyk, Igor, et al.. (2019). Improved Adversarial Image Captioning. International Conference on Learning Representations. 1 indexed citations

8.

Sercu, Tom, Sebastian Gehrmann, Hendrik Strobelt, et al.. (2019). Interactive Visual Exploration of Latent Space (IVELS) for Peptide Auto-Encoder Model Selection. International Conference on Learning Representations. 3 indexed citations

9.

Melnyk, Igor, et al.. (2019). Wasserstein Barycenter Model Ensembling. arXiv (Cornell University). 3 indexed citations

10.

Chen, Chun-Fu, et al.. (2018). Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition. arXiv (Cornell University). 15 indexed citations

11.

Melnyk, Igor, et al.. (2018). Improved Image Captioning with Adversarial Semantic Alignment.. arXiv (Cornell University). 6 indexed citations

12.

Mroueh, Youssef, et al.. (2018). Sobolev GAN. 6 indexed citations

13.

Mroueh, Youssef & Tom Sercu. (2017). Fisher GAN. Neural Information Processing Systems. 30. 2513–2523. 9 indexed citations

14.

Sercu, Tom, George Saon, Jia Cui, et al.. (2017). Network architectures for multilingual speech representation learning. 5295–5299. 16 indexed citations

15.

Cui, Jia, Brian Kingsbury, Bhuvana Ramabhadran, et al.. (2017). Knowledge distillation across ensembles of multilingual models for low-resource languages. 4825–4829. 29 indexed citations

16.

Sercu, Tom, Christian Puhrsch, Brian Kingsbury, & Yann LeCun. (2016). Very deep multilingual convolutional neural networks for LVCSR. 4955–4959. 139 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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