Animesh Garg
Animesh Garg develops foundations for generalizable robot autonomy. His research uses representations, reinforcement learning and structured models for cognitive and dexterous skills. Garg completed doctoral study in operations research at Berkeley from 2011 to 2016 and earned a computer-science master’s degree. He then held a computer-science postdoctoral appointment at Stanford from 2016 to 2018. Garg worked at Nvidia from 2018 to 2024 and served as faculty at Toronto and the Vector Institute from 2019 to 2023. These appointments overlap. He became an assistant professor at Georgia Tech in 2024. He was also chief scientist at Apptronik from 2024 to 2025.
- Works
- 182
- Citations
- 6,474
- h-index
- 42
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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2011–2018
Berkeley and Stanford research
Garg completed doctoral study in operations research at Berkeley from 2011 to 2016 and earned a computer-science master’s degree. He then held a computer-science postdoctoral appointment at Stanford from 2016 to 2018.
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2018–2024
Industrial and academic AI
Garg worked at Nvidia from 2018 to 2024 and served as faculty at Toronto and the Vector Institute from 2019 to 2023. These appointments overlap.
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2024
Georgia Tech faculty
He became an assistant professor at Georgia Tech in 2024. He was also chief scientist at Apptronik from 2024 to 2025.
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As of October 2026
Skills and generalization
His research studies object-centred and event representations, self-supervised pretraining for reinforcement learning and control, and efficient dexterous-skill learning. Applications include laboratories, surgery and manufacturing.
Sources and credits
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