Ashish Vaswani
Ashish Vaswani | |
|---|---|
| Born | 1986 (age 39–40) India |
| Education | |
| Known for | Transformer (deep learning architecture) |
| Scientific career | |
| Fields | |
| Workplaces |
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| Thesis | Smaller, Faster, and Accurate Models for Statistical Machine Translation (2014) |
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Ashish Vaswani is an Indian computer scientist and entrepreneur. He conducted research at Google Brain, co-founded Adept AI, and co-founder and chief executive officer of Essential AI.[2][3][4] As of June 2026, Vaswani, along with Essential AI, was acqui-hired by Nvidia.[1]
Vaswani is a co-author of the 2017 paper "Attention Is All You Need", which introduced the Transformer neural network architecture.[5] The Transformer model has been used in the development of subsequent NLP models BERT, ChatGPT, and their successors.
Career
[edit]Vaswani completed his engineering in Computer Science from Birla Institute of Technology, Mesra in 2002. In 2004, he enrolled at the University of Southern California for graduate studies.[6] He earned his PhD in Computer Science at the University of Southern California supervised by David Chiang.[7][8] During his research career at Google,[9] Vaswani was part of the Google Brain team, where he conducted the work leading to the 'Attention Is All You Need' publication. Prior to joining Google, he was affiliated with the Information Sciences Institute at the University of Southern California.
After Google, Vaswani co-founded Adept AI, a machine learning-focused startup that developed AI agents and tools for software automation. He later left the company.[2] Vaswani co-founded Essential AI with Niki Parmar in December 2022.[3] He was chief executive officer of Essential AI[4][10][11] until June 2026 when Essential AI, including Vaswani, was acqui-hired by Nvidia.[1]
Notable works
[edit]Vaswani's most notable paper, "Attention Is All You Need", was published in 2017.[12] The paper introduced the Transformer model, which uses self-attention mechanisms instead of recurrence for sequence-to-sequence tasks.
The Transformer architecture has become foundational to modern language models and NLP systems, including BERT (2018),[13] GPT-2, GPT-3 (2019–2020) and many more recent models. The "Attention Is All You Need" paper is among the most cited papers in machine learning.
References
[edit]- 1 2 3 Fried, Ina (2026-06-23). "Google takes the hit in AI's talent war". Axios. Retrieved 2026-06-25.
- 1 2 Rajesh, Ananya Mariam; Hu, Krystal (2023-03-16). "AI startup Adept raises $350 mln in fresh funding". Reuters. Retrieved 2026-05-05.
- 1 2 Tong, Anna; Hu, Krystal (2023-05-04). "Top ex-Google AI researchers raise funding from Thrive Capital". Reuters. Retrieved 2026-05-05.
- 1 2 "We would like to be DeepSeek in the West, says Essential AI cofounder". The Economic Times. 2025-06-17. Retrieved 2026-05-05.
- ↑ Vaswani, Ashish; Noam Shazeer; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan; Kaiser, Łukasz Mieczysław; Illia Polosukhin (2017). "Attention is All you Need" (PDF). Advances in Neural Information Processing Systems 30. Advances in Neural Information Processing Systems. arXiv:1706.03762. Wikidata Q30249683.
- ↑ Team, OfficeChai (February 4, 2023). "The Indian Researchers Whose Work Led To The Creation Of ChatGPT". OfficeChai.
- ↑ "8 Indian-origin AI and ML experts in the US: Where did they study". The Times of India. 2025-07-30. ISSN 0971-8257. Retrieved 2025-12-31.
- ↑ "Ashish Vaswani's webpage at ISI". www.isi.edu.
- ↑ "Transformer: A Novel Neural Network Architecture for Language Understanding". ai.googleblog.com. August 31, 2017.
- ↑ "Ashish Vaswani's Essential AI wants to use powerful AI to solve humanity's biggest challenges". forbesindia.com. Retrieved 2026-07-30.
- ↑ Love, Julia (3 September 2025). "What If We're Doing AI All Wrong?". Bloomberg.
- ↑ Dawson, Caitlin (March 9, 2023). "USC Alumni Paved Path for ChatGPT". USC Viterbi | School of Engineering.
- ↑ Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (May 24, 2019). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". arXiv:1810.04805 [cs.CL].
