Research that moves from theory to impact

We believe in conducting research that not only advances the frontiers of knowledge but also has tangible impact on society. Our interdisciplinary approach brings together experts from diverse fields to tackle complex real-world challenges.

  • Interdisciplinary teams spanning AI, engineering, and the sciences
  • Strong balance between foundational breakthroughs and deployable systems
  • Ethics-forward mindset across every centre and lab

Research Pillars

We bring balance across foundational ideas, applied systems, and ethics-first thinking to keep our research rigorous and relevant.

Fundamental AI Research

Advancing the theoretical foundations of artificial intelligence and machine learning.

Applied AI Solutions

Developing AI applications for healthcare, finance, agriculture, and social good.

Responsible AI

Ensuring AI systems are fair, transparent, and beneficial for all of humanity.

Fundamental Research

Our fundamental research advances the theoretical foundations of artificial intelligence, machine learning, and data science, pushing the boundaries of what's possible.

Network Analytics & Graphical Models

Responsible AI

Reinforcement Learning & Multi-armed Bandits

Deep Learning

Applied Research & Application Areas

Our applied research translates theoretical advances into real-world solutions across diverse domains, creating meaningful impact for society.

Healthcare

Agriculture

Smart Cities & Transportation

Financial Analytics

Manufacturing

Energy & Environment

Defence

Education

Systems Biology

Research Themes

Explore our fundamental and applied research themes, and filter by focus areas that matter to you.

Filter by Tag

Attention Mechanisms in Deep Neural Networks Fundamental

Attention Mechanisms in Deep Neural Networks

Recently the Deep Learning community has shown great interest in attention mechanisms to train neural networks - the network pays attention to only certain …

neural networks reinforcement learning attention mechanism
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Domain Agnostic Methods for Integration of Prior Knowledge in Learning Algorithms Fundamental

Domain Agnostic Methods for Integration of Prior Knowledge in Learning Algorithms

While data analysis tools are used across various problem areas, the question of importance of domain knowledge in improving the performance of these algorithms …

domain knowledge data analysis network
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Financial Analytics Applied

Financial Analytics

There is an active collaboration between the faculty and financial service providers who work in previously underserved areas. Research is being actively …

data analytics finance
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Learning With Limited and Partial Data Fundamental

Learning With Limited and Partial Data

When data science solutions are deployed in practice, seldom do the conditions on the ground match the theoretical assumptions of the algorithms. In this …

data science semi supervised learning
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Manufacturing Analytics Applied

Manufacturing Analytics

Traditionally, manufacturing facilities (particularly chemical manufacturing) have been in the forefront in terms of using advanced computational techniques in …

large scale data manufacturing data analytics
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Network Representation Learning Fundamental

Network Representation Learning

Learning deep representations from raw input data has revolutionised the field of Machine Learning in the past few years. While deep representation learning has …

network hypergraphs multilayer networks
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Safety and Stability Preserving Reinforcement Learning Fundamental

Safety and Stability Preserving Reinforcement Learning

While reinforcement algorithms have achieved notable successes recently, the use of such approaches in controlling real physical systems is not really …

reinforcement learning deep learning control theory
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Smart Cities Applied

Smart Cities

The centre carries out research in many aspects of Smart Cities. We work closely with the Centre of Excellence in Urban Transportation in developing solutions …

smart mobility traffic modelling
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Systems Biology and Health Care Applied

Systems Biology and Health Care

In the domain of systems biology, the centre functions closely with the Initiative for Biological Systems Engineering (IBSE). We work primarily on cancer …

network medical research genomics
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Our Research Centres

The School is home to several world-class research centres, each established as a forerunner in various key areas of AI and data science.

AI4Bharat

Open-source datasets, tools, and models that make Indian languages AI-ready.

AlphaGrep Quantitative Research Lab

AI for quantitative finance: market microstructure, risk, and next-gen trading strategies.

Centre for Integrative Biology and Systems Medicine

AI/ML for clinical and biological data to advance precision and systems medicine.

CeRAI

Standards, policy, and tooling to make AI accountable, explainable, and responsible.

RBCDSAI

Flagship centre spanning networks, RL, NLP, and industrial AI in manufacturing, cities, and health.

The Mint

Mobility digital infrastructure and R&D to accelerate sustainable, multimodal transport.

Walmart Centre for Tech Excellence

AI and IoT solutions to modernize MSMEs and drive efficient, sustainable growth.