Nir Baram

971 citations
12 papers · 716 indexed · h-index 9

Nir Baram

12 papers receiving 702 citations

Peers

Nir Baram
Comparison fields: 5 of 84
  • Renewable Energy, Sustainability and the Environment 252
  • Automotive Engineering 148
  • Electrical and Electronic Engineering 363
  • Electronic, Optical and Magnetic Materials 73
  • Materials Chemistry 151
Replace Parag Nijhawan with:
Parag Nijhawan India
Ali Radwan Egypt
Tianjiao Zhu China
Vipin Das India
Fei Peng China
Yang Lei China
Shuyuan Zhang China
A.U. Chávez-Ramírez Mexico
Rongjie Wang China
Nir Baram relative to Parag Nijhawan India Parag Nijhawan's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nir Baram

Since Specialization
Citations

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

Fields of papers citing papers by Nir Baram

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 18 scholars most cited alongside Nir Baram, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nir Baram Line = papers co-authored together Nir Baram links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
End-to-End Differentiable Adversarial Imitation Learning
201730
2
Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding
20171
3
Deep Reinforcement Learning with Averaged Target DQN.
20168
4 201682
5 2014202
6 20141
7 2013115
8 2010114
9 201048
10 20093
11 200874
12 200738

About Nir Baram

Nir Baram is a scholar working on Renewable Energy, Sustainability and the Environment, Electrochemistry and Water Science and Technology, having authored 12 papers that have together received 716 indexed citations. Recurring topics across this work include Advanced Photocatalysis Techniques (4 papers), TiO2 Photocatalysis and Solar Cells (4 papers), Advanced Battery Materials and Technologies (3 papers), Reinforcement Learning in Robotics (3 papers), Evolutionary Algorithms and Applications (2 papers), Advanced battery technologies research (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Advanced oxidation water treatment (2 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (252 citations), Automotive Engineering (148 citations) and Electrical and Electronic Engineering (363 citations). Nir Baram has collaborated with scholars based in Israel and United States. Frequent co-authors include Yair Ein‐Eli, Oron Anschel, Frank Y. Fan, W. Craig Carter, Zheng Li, Kyle C. Smith, Yet‐Ming Chiang, Nahum Shimkin, David Starosvetsky and Robert Armon. Their work appears in journals such as Applied Catalysis B: Environmental, Electrochemistry Communications, The Journal of Physical Chemistry C, Electrochimica Acta and Physical Chemistry Chemical Physics.

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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