Arjun Nitin Bhagoji

6.2k citations
18 papers · 249 indexed · h-index 7

Arjun Nitin Bhagoji

16 papers receiving 235 citations

Peers

Arjun Nitin Bhagoji
Comparison fields: 5 of 46
  • Artificial Intelligence 218
  • Signal Processing 54
  • Computer Science Applications 18
  • Computer Networks and Communications 52
  • Computational Mathematics 1
Replace Mohammad Reza Nosouhi with:
Mohammad Reza Nosouhi Australia
Jiazhu Dai China
Ali Shahin Shamsabadi United Kingdom
Kazuma Ohara Japan
Xixiang Lyu China
Lior Malka United States
Oleksandr Tkachenko Germany
Avinava Dubey United States
Xiangyu Wang China
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Citations per field
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Citations per year

Countries citing papers authored by Arjun Nitin Bhagoji

Since Specialization
Citations

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

Fields of papers citing papers by Arjun Nitin Bhagoji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arjun Nitin Bhagoji. 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 Arjun Nitin Bhagoji. The network helps show where Arjun Nitin Bhagoji may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Arjun Nitin Bhagoji, 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 Arjun Nitin Bhagoji Line = papers co-authored together Arjun Nitin Bhagoji links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 20250
2 202417
3 20241
4 20246
5 20244
6 20240
7 20232
8 20237
9
PatchGuard: A provably robust defense against adversarial patches via small receptive fields and masking
20212
10 202119
11
The Role of Data Geometry in Adversarial Machine Learning
20201
12
Analyzing Federated Learning through an Adversarial Lens
201986
13 20195
14 201929
15
PAC-learning in the presence of adversaries
201813
16
Black-box attacks on deep neural networks via gradient estimation
20182
17 20184
18
Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers.
201751

About Arjun Nitin Bhagoji

Arjun Nitin Bhagoji is a scholar working on Artificial Intelligence, Computer Networks and Communications and Toxicology, having authored 18 papers that have together received 249 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (11 papers), Anomaly Detection Techniques and Applications (8 papers), Network Security and Intrusion Detection (7 papers), Internet Traffic Analysis and Secure E-voting (6 papers), Privacy-Preserving Technologies in Data (2 papers), Hate Speech and Cyberbullying Detection (1 paper), Spam and Phishing Detection (1 paper) and Bacillus and Francisella bacterial research (1 paper). The work is most often cited by research in Artificial Intelligence (218 citations), Signal Processing (54 citations) and Computer Science Applications (18 citations). Arjun Nitin Bhagoji has collaborated with scholars based in United States, France and China. Frequent co-authors include Prateek Mittal, Daniel Cullina, Seraphin Calo, Supriyo Chakraborty, Vikash Sehwag, Shinan Liu, Hai-Tao Zheng, Mung Chiang, Paul Schmitt and Nick Feamster. Their work appears in journals such as ACM SIGMETRICS Performance Evaluation Review, Proceedings of the ACM on Measurement and Analysis of Computing Systems and USENIX Security Symposium.

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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