Alvaro Velasquez

1.3k total citations
94 papers, 760 citations indexed

About

Alvaro Velasquez is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Alvaro Velasquez has authored 94 papers receiving a total of 760 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 18 papers in Electrical and Electronic Engineering and 11 papers in Computational Theory and Mathematics. Recurrent topics in Alvaro Velasquez's work include Reinforcement Learning in Robotics (13 papers), Advanced Memory and Neural Computing (12 papers) and Adversarial Robustness in Machine Learning (11 papers). Alvaro Velasquez is often cited by papers focused on Reinforcement Learning in Robotics (13 papers), Advanced Memory and Neural Computing (12 papers) and Adversarial Robustness in Machine Learning (11 papers). Alvaro Velasquez collaborates with scholars based in United States, Colombia and Mexico. Alvaro Velasquez's co-authors include Sumit Kumar Jha, Susmit Jha, Mark E. Mullins, Karima Benameur, Sara C. Auld, William T. Hu, Leda Bassit, Houbing Song, Tuğba Öztürk and J. Christina Howell and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and American Journal of Respiratory and Critical Care Medicine.

In The Last Decade

Alvaro Velasquez

81 papers receiving 741 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Alvaro Velasquez 136 131 127 95 84 94 760
Jiong Liu 152 1.1× 270 2.1× 176 1.4× 43 0.5× 45 0.5× 100 1.4k
Yitong Huang 25 0.2× 33 0.3× 121 1.0× 90 0.9× 106 1.3× 45 949
Hyun-Seok Kim 87 0.6× 79 0.6× 37 0.3× 52 0.5× 262 3.1× 62 1.2k
Sung 33 0.2× 28 0.2× 55 0.4× 31 0.3× 25 0.3× 94 825
Bing Leng 61 0.4× 129 1.0× 58 0.5× 15 0.2× 27 0.3× 88 741
Kai Gao 10 0.1× 39 0.3× 119 0.9× 73 0.8× 38 0.5× 36 475
Yu Ma 154 1.1× 35 0.3× 27 0.2× 194 2.0× 66 0.8× 50 956

Countries citing papers authored by Alvaro Velasquez

Since Specialization
Citations

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

Fields of papers citing papers by Alvaro Velasquez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alvaro Velasquez

This figure shows the co-authorship network connecting the top 25 collaborators of Alvaro Velasquez. A scholar is included among the top collaborators of Alvaro Velasquez 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 Alvaro Velasquez. Alvaro Velasquez 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.
Subramani, K., et al.. (2025). Advancing discrete optimization: novel approaches with dataless neural networks. Journal of Combinatorial Optimization. 50(4).
2.
Gorodetsky, Alex, et al.. (2025). Automatic biomarker discovery and enrichment with BRAD. Bioinformatics. 41(5). 3 indexed citations
3.
Subramani, K., et al.. (2025). Exploring cycle cover variants: A dataless neural networks approach. Neurocomputing. 656. 131361–131361.
4.
Velasquez, Alvaro, Ufuk Topcu, Zhangyang Wang, et al.. (2025). Neurosymbolic AI as an antithesis to scaling laws. PNAS Nexus. 4(5). pgaf117–pgaf117. 1 indexed citations
5.
6.
Jha, Sumit Kumar, et al.. (2024). Exploring the Predictive Capabilities of AlphaFold Using Adversarial Protein Sequences. IEEE Transactions on Artificial Intelligence. 5(7). 3384–3392. 4 indexed citations
7.
Acharya, K., Alvaro Velasquez, & Houbing Song. (2024). A Survey on Symbolic Knowledge Distillation of Large Language Models. IEEE Transactions on Artificial Intelligence. 5(12). 5928–5948. 12 indexed citations
8.
Feng, Ke, et al.. (2024). A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence. IEEE Transactions on Artificial Intelligence. 5(8). 3765–3779. 6 indexed citations
9.
10.
Velasquez, Alvaro, et al.. (2023). Optimal Deterministic Controller Synthesis from Steady-State Distributions. Journal of Automated Reasoning. 67(1).
11.
Velasquez, Alvaro, et al.. (2023). Formation, Transformation, and Electrical Performance of Magnéli Phases Obtained by Flame Spraying from TiO2 Particles. Journal of Materials Engineering and Performance. 33(5). 2562–2571. 1 indexed citations
12.
Subramani, K., et al.. (2023). Priority-based bin packing with subset constraints. Discrete Applied Mathematics. 342. 64–75.
13.
Aoki, Nobuyuki, et al.. (2023). An investigation of the background potential in quantum constrictions using scanning gate microscopy and a swarming algorithm. Physica A Statistical Mechanics and its Applications. 614. 128550–128550. 1 indexed citations
14.
Shukla, Yash, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, & Jivko Sinapov. (2023). Automaton-Guided Curriculum Generation for Reinforcement Learning Agents. Proceedings of the International Conference on Automated Planning and Scheduling. 33(1). 605–613. 1 indexed citations
15.
Acharya, K., et al.. (2023). Neurosymbolic Reinforcement Learning and Planning: A Survey. IEEE Transactions on Artificial Intelligence. 5(5). 1939–1953. 18 indexed citations
16.
Velasquez, Alvaro, et al.. (2020). Verification-Guided Tree Search. Adaptive Agents and Multi-Agents Systems. 2026–2028. 1 indexed citations
17.
Fernandes, Steven Lawrence, et al.. (2019). On the Susceptibility of Deep Neural Networks to Natural Perturbations.. Journal of International Crisis and Risk Communication Research. 2 indexed citations
18.
19.
Guarín‐Zapata, Nicolás & Alvaro Velasquez. (2010). Caracterización de Imanes para aplicación en sistemas de sensado de posición. El Repositorio Institucional de la Universidad EAFIT (Universidad EAFIT). 42(1). 5.
20.
Lechner, Andrew J., et al.. (1998). Cholestatic Liver Injury Increases Circulating TNF- α and IL-6 and Mortality after Escherichia coli Endotoxemia. American Journal of Respiratory and Critical Care Medicine. 157(5). 1550–1558. 60 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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