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.

Research areas

Algorithms and Theory

Economics and Electronic Commerce

General Science

Health & Bioscience

Human-Computer Interaction and Visualization

Information Retrieval and the Web

Machine Intelligence

Machine Perception

Mobile Systems

Natural Language Processing

Team focus summaries

Advertising Sciences

The next billion users (NBU) present a unique set of challenges to the search and the advertising business. We aim to solve the research challenges around NBU ads first and extend these solutions broadly to the FBU market as well.

Key Technical Areas

Machine Learning
Natural Language Processing
Predictive Modeling

Group members

Aravindan Raghuveer, Engineering Manager (Team Lead)
Abhirut Gupta, Research Software Engineer
Anand Brahmbhatt, Pre-doctoral Researcher
Anirban Laha, Research Software Engineer
Anubhav Jangra, Pre-doctoral Researcher
Navodita Sharma, Software Engineer
Preksha Nema, Research Scientist
Rishi Saket, Senior Research Scientist
Shreyas Havaldar, Pre-doctoral Researcher
Sneha Mondal, Research Software Engineer

Cognitive Modeling and Machine Learning (CogML)

We build expressive, robust machine learning systems, drawing functional and algorithmic inspiration from human cognition. We are also working to develop a deeper understanding of human cognition, with applications in ML systems design, user modeling, and personalisation.

Key Technical Areas

Multi-task and continual learning
Meta-learning
Neuro-symbolic architectures
Interpretability & attribution
User modeling & personalization
Computational cognitive modeling

Group members

Pradeep Shenoy, Research Scientist (Team Lead)
Kushal Chauhan, Pre-doctoral Researcher
Nishant Jain, Pre-doctoral Researcher
Rishabh Tiwari, Pre-doctoral Researcher
Rishubh Singh, Pre-doctoral Researcher
Shubham Mittal, Pre-doctoral Researcher
Tarun Verma, Software Engineer

Earth Observation Science (EOS)

We use machine learning and earth observation imagery to solve geoscience research questions, motivated by applications in climate adaptation and social impact.

Key Technical Areas

Computer Vision
Machine Learning
Remote Sensing

Group members

Varun Gulshan, Research Scientist (Team Lead)
Alex Wilson, Software Engineer
Manan Sharma, Pre-doctoral Researcher
Shubhika Garg, Pre-doctoral Researcher
Tejan Karmali, Pre-doctoral Researcher
Umangi Jain, Pre-doctoral Researcher
Vishal Batchu, Research Engineer

Health

Our health research aims to create a mobile platform to collect vital healthcare data in novel ways and use them to predict medical conditions and subsequently suggest behavioral changes as preventive measures.

Key Technical Areas

Behaviour Sciences
Natural Language Processing
Machine Learning
Computer Vision

Group members

Pooja Rao, Research Scientist (Team Lead)
Narayan Hegde, Software Engineer (Team Lead)
Abhimanyu Singh, Product Manager
Jatin Alla, Pre-doctoral Researcher
Pradeep Kumar S, Technical Program Manager
Sriram Lakshminarasimhan, Software Engineer

Machine Learning and Optimization

We conduct fundamental research in ML. The ML algorithms, and deep learning in particular, are effective at optimizing accuracy on a given training-test dataset, but the generalization ability of the algorithms can be very fragile, typically requiring multiple rounds of tuning and iteration for real-world deployment. Our goal is to design robust, rigorous ML algorithms that work (almost) out of the box.

Key Technical Areas

Algorithm robustness
ML Optimization

Group members

Prateek Jain, Research Scientist (Team Lead)
Aishwarya P S, Software Engineer
Aniket Das, Pre-doctoral Researcher
Anirudh GP, Software Engineer
Anshul Nasery, Pre-doctoral Researcher
Arun Suggala, Research Scientist
Dheeraj Nagaraj, Research Scientist
Gaurav Srivastava, Software Engineer
Karthikeyan Shanmugam, Research Scientist
Kushal Majmundar, Pre-doctoral Researcher
Lovish Madaan, Pre-doctoral Researcher
Praneeth Netrapalli, Research Scientist
Ramnath Kumar, Pre-doctoral Researcher
Sandeep Juneja, Visiting Researcher
Soumyabrata Pal, Visiting Researcher
Yashas Samaga B L, Pre-doctoral Researcher

Mixed-Mode User Understanding (M2U2)

We work on fundamental computer vision and machine learning research problems with a focus on creating impact in the areas of healthcare, agriculture and accessibility.

Key Technical Areas

Computer Vision
Machine Learning
Graph Neural Networks

Group members

Gaurav Aggarwal, Research Scientist (Team Lead)
Alok Talekar, Software Engineer
Amandeep Kaur, Pre-doctoral Researcher
Anirban Santara, Research Software Engineer
Debapriya Tula, Pre-doctoral Researcher
Gagan Madan, Software Engineer
Nidhi Hegde, Pre-doctoral Researcher
Radhika Dua, Pre-doctoral Researcher
Sharad Shriram, Pre-doctoral Researcher
Sohan Rudra, Pre-doctoral Researcher
Sujoy Paul, Research Scientist

Multi-agent Systems for Societal Impact (MASSI)

We apply AI methodologies, such as reinforcement learning and multiagent systems, to big problems in public health, education, disaster prevention and conservation, by supporting projects led by social organizations spanning seventeen countries across Asia-Pacific and Sub-Saharan Africa.

Key Technical Areas

Public Health
Conservation
Agriculture

Group members

Milind Tambe, Principal Scientist (Team Lead)
Aparna Taneja, Software Engineer
Arshika Lalan, Pre-doctoral Researcher
Divy Thakkar, Program Manager
Gargi Singh, Pre-doctoral Researcher
Manish Jain, Software Engineer
Shresth Verma, Pre-doctoral Researcher

Natural Language Understanding (NLU)

We aim to democratize information access and make Google products awesome for Indian language users. At a global level, we aim to make time a horizontal in natural language problems and enable reasoning with it.

Key Technical Areas

Multilingual Learning
Representation Learning
Learning from Low Resources
Conversational AI
Knowledge Graphs
Temporal Reasoning

Group members

Partha Talukdar, Research Scientist (Team Lead)
Bidisha Samanta, Research Engineer
Dinesh Tewari, Research Program Manager
Kartikeya Badola, Software Engineer
Megh Thakkar, Pre-doctoral Researcher
Nitish Gupta, Research Scientist
Palak Jain, Software Engineer
Rachit Bansal, Pre-doctoral Researcher
Sagar Gubbi, Visiting Researcher
Shachi Dave, Software Engineer
Shaily Bhatt, Pre-doctoral Researcher
Shikhar Bharadwaj, Pre-doctoral Researcher
Shikhar Vashishth, Research Scientist
Siddhesh Pawar, Pre-doctoral Researcher
Sriram (Sri) Ganapathy, Visiting Faculty Researcher

Highlighted projects

A Glance at Past Three Years of Google Research in India

Google Research India was founded with two key principles: (1) to advance fundamental computer science and AI research by building a strong team while partnering with India’s research community and (2) to apply that research to some of the country’s biggest healthcare, agriculture and education problems while continuing to develop services that benefit people everywhere. The journey has been deeply fulfilling, from working with local NGOs as they use AI approaches, scaling the impact of their efforts, to developing and sharing multilingual language models that better understand Indian languages than ever before.

Using ML to Boost Engagement with a Maternal and Child Health Program in India

In a recent 23,000-participant study, we demonstrate how #MachineLearning can achieve ~30% improved engagement with an automated voice messaging program for maternal and child health in India, operated by our partners @armmanindia.

Google Research India: an AI lab in Bangalore

Announcing the launch of Google Research India - an AI Lab focused on advancing fundamental computer science and AI research, and applying it to tackle big problems in the field of healthcare, agriculture and education.

Progress from a year of AI for Social Good at Google Research India

Highlighting successes and challenges of some of the projects under AI for Social Good.

Featured publications

Re-contextualizing Fairness in NLP: The Case of India

Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus of social disparities in West, and are not directly portable to other geo-cultural contexts. In this paper, we focus on NLP fair-ness in the context of India. We start with a brief account of the prominent axes of social disparities in India. We build resources for fairness evaluation in the...

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Re-contextualizing Fairness in NLP: The Case of India

Shaily Bhatt, Sunipa Dev, Partha Pratim Talukdar, Shachi Dave, Vinodkumar Prabhakaran

In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (AACL-IJCNLP) (2022) (to appear)

Domain-Agnostic Contrastive Representations for Learning from Label Proportions

We study the weak supervision learning problem of Learning from Label Proportions (LLP) where the goal is to learn an instance-level classifier using proportions of various class labels in a bag – a collection of input instances that often can be highly correlated. While representation learning for weakly-supervised tasks is found to be effective, they often require domain knowledge. To the...

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Domain-Agnostic Contrastive Representations for Learning from Label Proportions

Jay Nandy, Rishi Saket, Prateek Jain, Jatin Chauhan, Balaraman Ravindran, Aravindan Raghuveer

Proc. CIKM 2022 (to appear)

Few-shot Controllable Style Transfer for Low-Resource Languages

Few-shot style transfer is the task of rewriting an input sentence using the stylistic properties extracted from a few (3-10) exemplar sentences, while approximately preserving the input content. This is especially useful in low resource settings where no large style-transfer datasets are available. We push the state-of-the-art for few-shot style transfer with a new method that models the...

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Few-shot Controllable Style Transfer for Low-Resource Languages

Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, Partha Pratim Talukdar

Few-shot Controllable Style Transfer for Low-Resource Languages (2022)

Novel Class Discovery without Forgetting

Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. Importantly, they achieve this without deteriorating the performance on their earlier learning. Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without...

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Novel Class Discovery without Forgetting

Joseph K J, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian

European Conference on Computer Vision (ECCV) (2022)

A Machine Learning Data Fusion Model for Soil Moisture Retrieval

We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moisture fields from GLDAS. The model was trained and evaluated on data from ~1300 in-situ...

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A Machine Learning Data Fusion Model for Soil Moisture Retrieval

Vishal Venkat Batchu, Grey Nearing, Varun Gulshan

Arxiv (2022)

What can we do with just the model? A simple knowledge extraction framework

We consider the problem of adapting semantic segmentation models to new target domains, only from the trained source model, without the source data. Not only is this setting much harder than if one had access to the source data, this is necessary in many practical situations where source data is not available due to privacy and storage reasons. Our algorithm has two parts - first, we update...

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What can we do with just the model? A simple knowledge extraction framework

Sujoy Paul, Ansh Khurana, Gaurav Aggarwal

International Conference on Machine Learning. Principles of Distribution Shift Workshop (2022) (to appear)

Multi-Variate Time Series Forecasting on Variable Subsets

We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a subset of the variables are available during inference. Variables are absent during inference because of intermittent data collection issues (eg. sensor failures) or domain shift between train / test. To the best of our knowledge, robustness of MSTF...

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Multi-Variate Time Series Forecasting on Variable Subsets

Jatin Chauhan, Aravindan Raghuveer, Jay Nandy, Rishi Saket, Balaraman Ravindran

KDD 2022 (to appear)

Meta-learning of dynamic policy adjustments in inhibitory control tasks

Simple perceptual decision-making tasks such as the Stroop and flanker tasks are popular as a method of measuring individual variation in the processing of conflicting visual stimuli--for instance, the difference in accuracy on stimuli with and without conflict. A major challenge in applying these tasks, for instance to compare two different populations of subjects, is the low reliability of...

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Meta-learning of dynamic policy adjustments in inhibitory control tasks

Soumya Chatterjee, Aakriti Kumar, Pradeep Shenoy

CogSci 2022 (2022) (to appear)

Spacing Loss for Discovering Novel Categories

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while...

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Spacing Loss for Discovering Novel Categories

Joseph K J, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian

Computer Vision and Pattern Recognition (CVPR) Workshop on Continual Learning in Computer Vision (2022)

Minimax optimization with Smooth Algorithmic Adversaries

We consider two-player zero sum games or minimax optimization $\min_x \max_y f(x,y)$ in the Stackelberg/sequential setting where the $x$-player goes first and $y$-player goes second, and where $f$ is nonconvex in $x$ and nonconcave in $y$. While such problems are encountered in several popular machine learning paradigms such as in training generative adversarial networks (GANs) and...

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Minimax optimization with Smooth Algorithmic Adversaries

Chi Jin, Lillian Ratliff, Praneeth Netrapalli, Tanner Fiez

International Conference on Learning Representations (ICLR) 2022

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs

Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation \citep{mnih2015human}. In contrast, existing theoretical results are pessimistic about Q-learning. For example, \citep{baird1995residual} shows that Q-learning does not converge even with linear function approximation for linear MDPs. Furthermore, even for tabular MDPs with...

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Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs

Dheeraj Nagaraj, Naman Agarwal, Praneeth Netrapalli, Prateek Jain, Syomantak Chaudhari

Symbolic Binding in Neural Networks through Factorized Memory Systems

In this blog post, we describe the paper 'Emergent Symbols through Binding in External Memory', which introduces the Emergent Symbol Binding Network (ESBN), a recurrent neural network with an augmented factorized memory system, to solve several logical reasoning tasks. We describe the design choices of the ESBN in great detail to compare with other sequence models, and perform a qualitative...

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Symbolic Binding in Neural Networks through Factorized Memory Systems

Ameya Shrikant Daigavane, Ansh Khurana, Gaurav Aggarwal, Shweta Bhardwaj

(2022)

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer

While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer. Analyses involving pairs of natural languages are often inconclusive and contradictory since languages simultaneously differ in many...

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When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer

Ameet Deshpande, Partha Pratim Talukdar, Karthik Narasimhan

NAACL 2022 (2022) (to appear)

Walking with PACE — Personalized and Automated Coaching Engine

Fitness coaching is effective in helping individuals to develop and maintain healthy lifestyle habits. However, there is a significant shortage of fitness coaches, particularly in low resource communities. Automated coaching assistants may help to improve the accessibility of personalized fitness coaching. Although a variety of automated nudge systems have been developed, few make use of formal...

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Walking with PACE — Personalized and Automated Coaching Engine

Aravindan Raghuveer, Deepak Nathani, Eshan Motwani, Karina Lorenzana Livingston, Madhurima Vardhan, Manish Gupta, Martin Gamunu Seneviratne, Narayan G Hegde, Nur Muhammad, Partha Pratim Talukdar, Rahul Singh, Shantanu Prabhat, Sriram Lakshminarasimhan, Srujana Merugu

UMAP: 30th ACM Conference on User Modeling, Adaptation and Personalization (2022)

FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing

Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independent dense prediction of object category and part attributes which increases...

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FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing

Pradeep Shenoy, Pranav Gupta, Ravi Kiran Sarvadevabhatla, Rishubh Singh

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022) (to appear)

Some of our locations

India

Some of our people

Manish Gupta

Aravindan Raghuveer

Abhirut Gupta

Milind Tambe

Alok Talekar

Divy Thakkar

Pradeep Shenoy

Rishubh Singh

Varun Gulshan

Aparna Taneja

Vishal Venkat Batchu

Narayan Hegde

Shivani Kapania

Prateek Jain

Gaurav Aggarwal

Anirban Santara

Sujoy Paul

Partha Talukdar

Shachi Dave

Shresth Verma

Lovish Madaan

Preksha Nema

Praneeth Netrapalli

Rishi Saket

Sriram Lakshminarasimhan

Kushal Chauhan

Rahul Singh

Gargi Singh

Jay Nandy

Join our team

Research Scientist, Google Research

As a Research Scientist, you'll actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.The research lab in Bangalore is a part of Google’s global network of researchers: participating in conferences, publishing research in scientific papers, and collaborating closely with one another.

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Visiting Researcher

The Visiting Researcher Program is available to both postdoctoral and university faculty candidates/applicants. Positions are flexible as part-time or full-time arrangements to accommodate a variety of schedules and commitments. Visiting Researchers typically collaborate with research and engineering teams at Google for 3 - 12 months.

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Research Intern (PhD, 2023)

Research Interns work closely with Research Scientists and Software Engineers to discover, invent, and build at scale. From creating experiments and prototyping implementations to designing architectures, Research Interns work on challenges in artificial intelligence, machine perception, data mining, machine learning, natural language understanding, privacy, computer architecture, networking, operating systems, storage and data management, and more. You are also expected to contribute to the wider research community by publishing papers.

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