India research lab

At India Research Lab, our mission is to contribute towards fundamental advances in computer science and apply our research to tackle big problems and deliver impact for India, Google, and the communities around the world.

About the team

Our impact and goals for the next 5 years are aligned along three dimensions:

Scientific impact

Advance the state of the art for every problem that we pursue.

Build models of human cognition and cognition-inspired AI algorithms.
Understand limitations of deep learning systems, improve their safety and robustness and address issues including calibration, fairness and explainability of ML solutions.
Combine computer vision based methods with personalised knowledge graphs to better understand images.
Pursue basic research in HCI, to ensure that our technologies touching end users are informed by an evolved understanding of human factors.

Societal impact

Demonstrate positive societal impact through our research in areas such as health, ecology and wildlife conservation.

Transform healthcare using ML: Our researchers have developed deep learning based solutions for more effective screening of diabetic retinopathy, which are being deployed at hospitals in India.
AI for Social Good program to address issues like public health, education and wildlife conservation, in partnership with NGOs and academic researchers, while making fundamental advances to the underlying scientific areas like multi-agent systems, ML and HCI.
Develop additional novel solutions focussed on the prevention and wellness for major diseases like CVD and diabetes; improve health outcomes at lower costs and solve for the acute shortage of doctors in countries like India.

Product impact

Make significant improvements to Google products to make them more helpful to our users.

Advance the state of the art and apply ML in areas like natural language understanding (NLU) and user understanding to address the unique challenges in the Indian context (e.g. code mixing in Search, diversity of languages, dialects and accents in Assistant) .
Enhance overall capabilities (e.g. ability of the Assistant to handle conversations requiring a combination of knowledge, reasoning and personalization), do better user modeling and to improve fraud detection in GPay.