Somesh Jha

29.7k citations
185 papers · 13.7k indexed · 8 hit papers · h-index 50
Topics
Advanced Malware Detection Techniques (58 papers)Security and Verification in Computing (45 papers)Adversarial Robustness in Machine Learning (35 papers)

In The Last Decade

Somesh Jha

176 papers receiving 12.8k citations

Hit Papers

The Limitations of Deep Learning in A...20032026201020182016201720152005200550010001.5k2.0k

Peers

Somesh Jha
Comparison fields: 5 of 145
  • Artificial Intelligence 9.2k
  • Signal Processing 4.6k
  • Computer Networks and Communications 4.2k
  • Information Systems 3.1k
  • Software 1.8k
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Countries citing papers authored by Somesh Jha

Since Specialization
Citations

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

Fields of papers citing papers by Somesh Jha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Somesh Jha

This figure shows the co-authorship network connecting the top 25 collaborators of Somesh Jha. A scholar is included among the top collaborators of Somesh Jha 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 Somesh Jha. Somesh Jha 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
#WorkIndexed citations
1 4
2 0
3 0
4 5
5 20
6
Robust Out-of-distribution Detection via Informative Outlier Mining.
3
7
Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification.
5
8
Concise Explanations of Neural Networks using Adversarial Training
9
9
Outis: Crypto-Assisted Differential Privacy on Untrusted Servers
3
10
Practical and Robust Privacy Amplification with Multi-Party Differential Privacy.
3
11
Improving Adversarial Robustness by Data-Specific Discretization.
4
12
Model Extraction and Active Learning.
5
13
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
3
14
Locally Differentially Private Protocols for Frequency Estimation
162
15
Practical {DIFC} Enforcement on Android
26
16 117
17 110
18 17
19 90
20
Model checking algorithms for the m-calculus
4

About Somesh Jha

Somesh Jha is a scholar working on Software, Signal Processing and Artificial Intelligence, having authored 185 papers that have together received 13.7k indexed citations. Recurring topics across this work include Advanced Malware Detection Techniques (58 papers), Security and Verification in Computing (45 papers) and Adversarial Robustness in Machine Learning (35 papers). The work is most often cited by research in Software (1.8k citations), Signal Processing (4.6k citations) and Artificial Intelligence (9.2k citations). Somesh Jha has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Matt Fredrikson, Patrick McDaniel, Z. Berkay Celik, Ananthram Swami, Nicolas Papernot, Thomas Ristenpart, Mihai Christodorescu, Ian Goodfellow, Oleg Sheyner and J.M. Wing. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Communications Magazine and IEEE Transactions on Software Engineering.

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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