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)
Rishi Saket, Senior Research Scientist
Abhirut Gupta, Research Software Engineer
Preksha Nema, Research Scientist
Sneha Mondal, Research Software Engineer
Anubhav Jangra, Pre-doctoral Researcher
Jatin Chauhan, Pre-doctoral Researcher

AnthroKrishi - Agriculture Research

Build sustainable agriculture solutions for the Global South.

Key Technical Areas

Computer Vision
Remote Sensing
Anomaly detection
Soil Health

Group members

Alok Talekar, Software Engineer (Lead)
Dinesh Tewari, Research Program Manager
Amandeep Kaur, Pre-doctoral Researcher

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
Rishubh Singh, Pre-doctoral Researcher
Tarun Verma, Software Engineer
Soumya Chatterjee, Pre-doctoral Researcher

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
Vishal Batchu, Software Engineer
Shubhika Garg, Pre-doctoral Researcher
Tejan Karmali, Pre-doctoral Researcher
Umangi Jain, Pre-doctoral Researcher

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
Sriram Lakshminarasimha, Software Engineer
Ashwin Vaswani, Pre-doctoral Researcher
Abhimanyu Singh, Product Manager
Pradeep Kumar S, Technical Program Manager

Human-Computer Interaction (HCI)

We conduct research at the intersection of HCI, AI, and human development. Their goal is to foster a cutting-edge HCI research agenda to shift AI from power in algorithms to power in people, especially the marginalized communities.

Key Technical Areas

ML Fairness
Ethical AI

Group members

Ding Wang, Researcher
Shivani Kapania, Pre-doctoral Researcher
Alex Taylor, Visiting Researcher
Rajesh Veeraraghavan, Visiting Researcher

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)
Praneeth Netrapalli, Research Scientist
Arun Suggala, Research Scientist
Dheeraj Nagaraj, Research Scientist
Karthikeyan Shanmugam, Research Scientist
Piyush Rai, Visiting Faculty Researcher
Arvind Ganesh Balasubramanian, Software Engineer
Anirudh GP, Software Engineer
Gaurav Srivastava, Software Engineer
Anshul Nasery, Pre-doctoral Researcher
Gourav Wadhwa, Pre-doctoral Researcher
Lovish Madaan, Pre-doctoral Researcher
Kushal Majmundar, Pre-doctoral Researcher

Mixed-Mode User Understanding (M2U2)

We are working on fundamental research problems in computer vision and machine learning, collaborating with product teams on various problems like user modeling, improving reliability of transactions, recommendation and improving user delight.

Key Technical Areas

Computer Vision
Machine Learning
Graph Neural Networks

Group members

Gaurav Aggarwal, Research Scientist (Team Lead)
Ashish Tendulkar, Software Engineer
Anirban Santara, Research Software Engineer
Gagan Madan, Software Engineer
Sujoy Paul, Research Scientist
Ameya Daigavane, Pre-doctoral Researcher
Ansh Khurana, Pre-doctoral Researcher
Sohan Rudra, Pre-doctoral Researcher

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)
Divy Thakkar, Program Manager
Aparna Taneja, Software Engineer
Manish Jain, Software Engineer
Shresth Verma, Pre-doctoral Researcher
Gargi Singh, 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)
Nitish Gupta, Research Scientist
Shikhar Vashishth, Research Scientist
Pushpak Bhattacharyya, Visiting Researcher
Shachi Dave, Software Engineer
Simran Khanuja, Pre-doctoral Researcher
Palak Jain, Software Engineer
Bidisha Samanta, Research Engineer
Shaily Bhatt, Pre-doctoral Researcher
Deepak Nathani, Pre-doctoral Researcher
Shikhar Bharadwaj, Pre-doctoral Researcher

Highlighted projects

Announcing Google India AI/ML Research Awardees 2020

Announcement for the recipients of Google India AI/ML Awards 2020.

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.

How we’re supporting 30 new AI for Social Good projects

Projects covering seventeen countries across Asia-Pacific and Sub-Saharan Africa, which will all receive funding, technical contributions from Google and access to computational resources.

Featured publications

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)

Robust outlier detection by de-biasing VAE likelihoods

Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input...

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Robust outlier detection by de-biasing VAE likelihoods

Kushal Chauhan, Barath Mohan U, Pradeep Shenoy, Manish Gupta, Devarajan Sridharan

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

GCR: Gradient coreset based replay buffer selection for continual learning

Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learnings across tasks in a resource-efficient manner. A major challenge for CL systems is catastrophic forgetting, where earlier tasks are forgotten while learning a new task. To address this, replay-based CL approaches...

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GCR: Gradient coreset based replay buffer selection for continual learning

Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, Pradeep Shenoy

CVPR 2022 (2022) (to appear)

Learnability of Linear Thresholds from Label Proportions

We study the problem of properly learning linear threshold functions (LTFs) in the learning from label proportions (LLP) framework. In this, the learning is on a collection of bags of feature-vectors with only the proportion of labels available for each bag. First, we provide an algorithm that, given a collection of such bags each of size at most two whose label proportions are consistent with...

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Learnability of Linear Thresholds from Label Proportions

Rishi Saket

Proc. NeurIPS'21 (2021)

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

Deepak Nathani

Shivani Kapania

Prateek Jain

Gaurav Aggarwal

Anirban Santara

Ashish Vijay Tendulkar

Sujoy Paul

Partha Talukdar

Shachi Dave

Simran Khanuja

Shresth Verma

Lovish Madaan

Ameya Daigavane

Preksha Nema

Praneeth Netrapalli

Soumya Chatterjee

Rishi Saket

Sriram Lakshminarasimhan

Kushal Chauhan

Rahul Singh

Gargi Singh

Ding Wang

Jatin Chauhan

Madhurima Vardhan

Jay Nandy

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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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