Bhavyajeet Singh - Zuse School ELIZA

PhD Student
Foundations of ML
Foundations of ML: Natural Language Processing

Bhavyajeet Singh is a PhD student and doctoral researcher at the Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt, advised by Prof. Iryna Gurevych. His research focuses on developing NLP methods for cybersecurity applications like penetration testing and secure code generation. Previously, he was a Research Fellow at Microsoft Research India, where he worked on low-latency memory-augmented retrieval systems. This work has been published at ICML 2026 and was integrated into Bing Ads production pipelines. His broader research experience spans secure code generation, retrieval-augmented and memory-augmented NLP systems, and cross-lingual fact-to-text generation. One of his most influential earlier publications is XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages, Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, and Vasudeva Varma, 2022. The work introduced a large-scale cross-lingual fact-to-text dataset with 0.45M pairs across eight low-resource languages, supporting research on grounded multilingual generation and cross-lingual alignment. The paper has gathered numerous citations, with the dataset being used my multiple downstream projects and models.

Bhavyajeet Singh is a PhD student and doctoral researcher at the Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt, advised by Prof. Iryna Gurevych. His research focuses on developing NLP methods for cybersecurity applications like penetration testing and secure code generation.

Previously, he was a Research Fellow at Microsoft Research India, where he worked on low-latency memory-augmented retrieval systems. This work has been published at ICML 2026 and was integrated into Bing Ads production pipelines. His broader research experience spans secure code generation, retrieval-augmented and memory-augmented NLP systems, and cross-lingual fact-to-text generation.

One of his most influential earlier publications is XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages, Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, and Vasudeva Varma, 2022. The work introduced a large-scale cross-lingual fact-to-text dataset with 0.45M pairs across eight low-resource languages, supporting research on grounded multilingual generation and cross-lingual alignment. The paper has gathered numerous citations, with the dataset being used my multiple downstream projects and models.

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