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
Sahana Ramnath, Pre-doctoral Researcher
Preksha Nema, Research Scientist
Sneha Mondal, Research Software Engineer
Anubhav Jangra, Pre-doctoral Researcher
Jatin Chauhan, 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
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
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

Narayan Hegde, Software Engineer (Team Lead)
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 marginalised 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
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

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)
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

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

"Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI

AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data...

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"Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI

Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Kumar Paritosh, Lora Mois Aroyo

SIGCHI, ACM (2021)

Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health

The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work to assist non-profits that employ automated messaging programs to deliver timely preventive care information to beneficiaries (new and expecting mothers) during pregnancy and after delivery. Unfortunately, a key...

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Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health

Aditya Mate, Lovish Madaan, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, Milind Tambe

AAAI Conference on Artificial Intelligence (2022) (to appear)

Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks

We perform a rigorous evaluation of recent self and semi-supervised ML techniques that leverage unlabeled data for improving downstream task performance, on three remote sensing tasks of riverbed segmentation, land cover mapping and flood mapping. These methods are especially valuable for remote sensing tasks since there is easy access to unlabeled imagery and getting ground truth labels can...

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Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks

Chaitanya Pankajkumar Patel, Shashank Sharma, Varun Gulshan

Arxiv (2021)

Understanding Convolutions on Graphs

Many systems and interactions - social networks, molecules, organisations, citations, physical models, transactions - can be represented quite naturally as graphs. How can we reason about and make predictions within these systems? One idea is to look at tools that have worked well in other domains: neural networks have shown immense predictive power in a variety of learning tasks. However,...

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Understanding Convolutions on Graphs

Ameya Shrikant Daigavane, Balaraman Ravindran, Gaurav Aggarwal

Distill (2021)

HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints

Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve the effectiveness of the available BT data, we introduce HintedBT -- a family of techniques which provides hints (through tags) to the encoder and decoder. First, we propose a novel method of using...

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HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints

Sahana Ramnath, Melvin Johnson, Abhirut Gupta, Aravindan Raghuveer

EMNLP 2021 (to appear)

MergeDistill: Merging Pre-trained Language Models using Distillation

Pre-trained multilingual language models (LMs) have achieved state-of-the-art results in cross-lingual transfer, but they often lead to an inequitable representation of languages due to limited capacity, skewed pre-training data, and sub-optimal vocabularies. This has prompted the creation of an ever-growing pre-trained model universe, where each model is trained on large amounts of language or...

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MergeDistill: Merging Pre-trained Language Models using Distillation

Simran Khanuja, Melvin Johnson, Partha Pratim Talukdar

Findings of ACL 2021 (to appear)

Model-agnostic fits for understanding information seeking patterns in humans

In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task. Here, we build upon carefully designed experiments, and data collected at scale (Hunt et al. 2016), that measured and catalogued these biases in aggregate form. We design deep learning models that replicate these biases in aggregate, while...

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Model-agnostic fits for understanding information seeking patterns in humans

Soumya Chatterjee, Pradeep Shenoy

AAAI (2021)

On Combining Bags to Better Learn from Label Proportions

In the framework of learning from label proportions (LLP) the goal is to learn a good instance-level label predictor from the observed label proportions of bags of instances. Most of the LLP algorithms either explicitly or implicitly assume the nature of bag distributions with respect to the actual labels and instances, or cleverly adapt supervised learning techniques to suit LLP. In practical...

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On Combining Bags to Better Learn from Label Proportions

Rishi Saket, Aravindan Raghuveer, Balaraman Ravindran

Proc. AISTATS'22 (2022) (to appear)

Differentially Private Model Personalization

We study personalization of supervised learning with user-level differential privacy. Consider a setting with many users, each of whom has a training data set drawn from their own distribution $P_i$. Assuming some shared structure among the problems $P_i$, can users collectively learn the shared structure---and solve their tasks better than they could individually---while preserving the privacy...

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Differentially Private Model Personalization

Abhradeep Guha Thakurta, Adam Smith, Keith Rush, Prateek Jain, Shuang Song

(2021)

LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes

Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional neural representations to low-dimensional binary codes is a challenging task and often require high-dimensions to be accurate. In this work, we propose a novel method for \textbf{L}earning \textbf{L}ow-dimensional...

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LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes

Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi

Advances in Neural Information Processing Systems 34 (2021)

Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems

We study the problem of learning vector valued non-linear dynamical systems from a single trajectory of {\em dependent or correlated} points. We assume a known link function $\phi : \mathbb{R} \to \mathbb{R}$ that satisfy a certain {\em expansivity property}. While the problem is well-studied in the linear case with strong learning guarantees even for non-mixing systems, the results in non-...

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Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems

Dheeraj Nagaraj, Praneeth Netrapalli, Prateek Jain, Suhas Kowshik

(2021)

Sample Efficient Linear Meta-Learning

Meta-learning algorithms synthesizes and leverages the knowledge from a given set of tasks to rapidly learn new tasks using very little data. While methods like ANIL \cite{raghu2019rapid} have been demonstrated to be effective in practical meta-learning problems, their statistical and computational properties are ill-understood. Recent theoretical studies of meta-learning problem in a simple...

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Sample Efficient Linear Meta-Learning

Kiran Koshy Thekumparampil, Praneeth Netrapalli, Prateek Jain, Sewoong Oh

(2021)

Streaming Linear System Identification with Reverse Experience Replay

We consider the problem of estimating a stochastic linear time-invariant (LTI) dynamical system from a single trajectory via streaming algorithms. The problem is equivalent to estimating the parameters of vector auto-regressive ($\var$) models encountered in time series analysis (\cite{hamilton2020time}). A recent sequence of papers...

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Streaming Linear System Identification with Reverse Experience Replay

Dheeraj Nagaraj, Praneeth Netrapalli, Prateek Jain, Suhas Kowshik

(2021)

Measuring Data Collection Diligence for Community Healthcare

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate...

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Measuring Data Collection Diligence for Community Healthcare

Ramesha Karunasena, Md Sarfrazul Ambiya, Arunesh Sinha, Ruchit Nagar, Divy Hasmukhbhai Thakkar, Dhyanesh Narayanan, Milind Shashikant Tambe

ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization 2021 (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

Sahana Ramnath

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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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Pre-doctoral Researcher Program

The Pre-Doctoral Researcher role is a 24-month role designed to give you direct experience working on challenging problems together with research scientists and engineers in Bangalore before continuing your Computer Science research education. As a Pre-Doctoral Researcher, you will collaborate with scientists and engineers at Google Research working on fundamental research and applied problems in a variety of areas in Computer Science.

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