Krishnamurthy Dvijotham
Also known as Dj Dvijotham
Krishnamurthy Dvijotham is a research scientist at Google DeepMind whose work aims at secure and trustworthy AI systems, from verifying neural networks to testing AI agents. After a postdoctoral fellowship at Caltech's Center for Mathematics of Information, he worked at Pacific Northwest National Laboratory in 2016 and 2017, then joined Google DeepMind in 2017 and co-founded its robust and verified AI team. In 2018, he coauthored a paper on scalable verification of deep networks, and in 2024 two papers with him as a coauthor won best paper awards: the ICML paper on stealing part of a production language model and the CVPR paper on rich human feedback for text-to-image generation. He led AI safety and security research at ServiceNow Research from 2024 to 2025. He is a senior staff research scientist at Google DeepMind, where he leads work on secure and trustworthy AI agents.
- Now at
- Google DeepMind
- Badges
Timeline
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2016–2017
Caltech postdoc, then PNNL
After a postdoctoral fellowship at the Center for Mathematics of Information at Caltech, he works as a researcher at Pacific Northwest National Laboratory from 2016 to 2017.
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2017–2024
Researcher at Google DeepMind
He works as a researcher at Google DeepMind from 2017 to 2024. He co-founds the robust and verified AI team at DeepMind, and his research is deployed in several Google products, including the Android Play Store, YouTube and Gemini.
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2018
Scalable verification of deep networks
In 2018, he coauthors a paper with Robert Stanforth, Sven Gowal, Timothy Mann and Pushmeet Kohli on verifying properties of neural networks. The method formulates verification as an optimization problem and solves a Lagrangian relaxation to bound the worst-case violation of a specification.
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2024
ICML best paper on model stealing
In 2024, he is a coauthor of Stealing Part of a Production Language Model, an ICML 2024 Best Paper with Nicholas Carlini, Daniel Paleka and others. The attack recovers the embedding projection layer of black-box production models such as OpenAI's ChatGPT or Google's PaLM-2, and extracts the entire projection matrix of OpenAI's Ada and Babbage models for under $20.
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2024
CVPR best paper on human feedback
In 2024, he is a coauthor of Rich Human Feedback for Text-to-Image Generation, one of the CVPR 2024 Best Papers. The paper presents the first rich human feedback dataset for image generation and a multimodal Transformer trained to predict that feedback.
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2024–2025
AI security at ServiceNow
He serves as Research Lead for AI Safety and Security at ServiceNow Research from 2024 to 2025 and founds its AI security research team. His research leads to the framework used for AI security testing at ServiceNow.
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As of October 2026
Securing AI agents at DeepMind
He is a senior staff research scientist at Google DeepMind, where he leads efforts on the development of secure and trustworthy AI agents. He also mentors a Summer 2026 research stream in the MATS program.
Career
- Since 2017Google DeepMindResearcher, Co-founder
- 2024–2025ServiceNowResearch Lead for AI Safety and Security
- 2016–2017Office of ScienceResearcher
- California Institute of TechnologyPostdoctoral Fellow
- ServiceNow ResearchFounder
Sources and credits
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