Ruben Glatt

447 total citations
22 papers, 213 citations indexed

About

Ruben Glatt is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, Ruben Glatt has authored 22 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Electrical and Electronic Engineering and 3 papers in Control and Systems Engineering. Recurrent topics in Ruben Glatt's work include Reinforcement Learning in Robotics (12 papers), Data Stream Mining Techniques (5 papers) and Evolutionary Algorithms and Applications (5 papers). Ruben Glatt is often cited by papers focused on Reinforcement Learning in Robotics (12 papers), Data Stream Mining Techniques (5 papers) and Evolutionary Algorithms and Applications (5 papers). Ruben Glatt collaborates with scholars based in United States, Brazil and China. Ruben Glatt's co-authors include Felipe Leno da Silva, Anna Helena Reali Costa, Reinaldo A. C. Bianchi, Wencong Su, Mert Korkali, Zeyu Liang, Qinran Hu, Van‐Hai Bui, Mengqi Wang and Lingxiao Xue and has published in prestigious journals such as Applied Energy, Expert Systems with Applications and IEEE Access.

In The Last Decade

Ruben Glatt

21 papers receiving 207 citations

Peers

Countries citing papers authored by Ruben Glatt

Since Specialization

Citations

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

Fields of papers citing papers by Ruben Glatt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruben Glatt

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement 2024 ·Applied Energy ·Zeyu Liang, (unknown), Mert Korkali, Ruben Glatt, Qinran Hu	20
2	Language model-accelerated deep symbolic optimization 2023 ·Neural Computing and Applications ·Felipe Leno da Silva, André Gonçalves, (unknown), (unknown), Ruben Glatt, Thomas Desautels, Mikel Landajuela, Daniel Faissol, Brenden K. Petersen	1
3	Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters 2023 ·OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) ·Van‐Hai Bui, (unknown), Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno da Silva, Can Huang, Lingxiao Xue, Ruben Glatt	1
4	An RMRAC With Deep Symbolic Optimization for DC–AC Converters Under Less-Inertia Power Grids 2023 ·IEEE Open Access Journal of Power and Energy ·Guilherme Vieira Hollweg, Van‐Hai Bui, Felipe Leno da Silva, Ruben Glatt, Shivam Chaturvedi, Wencong Su	4
5	AutoTG: Reinforcement Learning-Based Symbolic Optimization for AI-Assisted Power Converter Design 2023 ·IEEE Journal of Emerging and Selected Topics in Industrial Electronics ·Felipe Leno da Silva, Ruben Glatt, Wencong Su, Van‐Hai Bui, (unknown), Shivam Chaturvedi, Mengqi Wang, Yi Lu Murphey, Can Huang, Lingxiao Xue, Rong Zeng	5
6	A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation 2023 ·IEEE Transactions on Intelligent Transportation Systems ·(unknown), Felipe Leno da Silva, Wencong Su, Ruben Glatt	9
7	Deep Neural Network-Based Surrogate Model for Optimal Component Sizing of Power Converters Using Deep Reinforcement Learning 2022 ·IEEE Access ·Van‐Hai Bui, (unknown), Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno da Silva, Can Huang, Lingxiao Xue, Ruben Glatt	14
8	Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning 2021 ·The Journal of Open Source Software ·(unknown), Ruben Glatt	1
9	Collaborative energy demand response with decentralized actor and centralized critic 2021 ·OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) ·Ruben Glatt, Felipe Leno da Silva, Braden Soper, William A. Dawson, (unknown), Ryan Goldhahn	6
10	DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning 2020 ·Expert Systems with Applications ·Ruben Glatt, Felipe Leno da Silva, Reinaldo A. C. Bianchi, Anna Helena Reali Costa	20
11	A Framework to Discover and Reuse Object-Oriented Options in Reinforcement Learning 2018 ·(unknown), Felipe Leno da Silva, Ruben Glatt, (unknown), Anna Helena Reali Costa	1
12	Simultaneously Learning and Advising in Multiagent Reinforcement Learning 2017 ·Adaptive Agents and Multi-Agents Systems ·Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa	56
13	Simultaneously Learning and Advising in Multiagent Reinforcement Learning 2017 ·Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa	0
14	Policy Reuse in Deep Reinforcement Learning 2017 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Ruben Glatt, Anna Helena Reali Costa	3
15	An Advising Framework for Multiagent Reinforcement Learning Systems 2017 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Ruben Glatt, Anna Helena Reali Costa	2
16	MOO-MDP: An Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning 2017 ·IEEE Transactions on Cybernetics ·Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa	33
17	Towards Knowledge Transfer in Deep Reinforcement Learning 2016 ·Ruben Glatt, Felipe Leno da Silva, Anna Helena Reali Costa	23
18	Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains 2016 ·Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa	3
19	Deep learning architecture for gesture recognition 2014 ·Americanae (AECID Library) ·Ruben Glatt	1
20	Thermographic inspection using a microcontroller-based camera positioning system 2014 ·UNESP Institutional Repository (São Paulo State University) ·(unknown), (unknown), Ruben Glatt, (unknown), (unknown)	1

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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