Bayesian Reasoning And INference (BRAIN)

Bayesian Deep Learning

Deep learning brought a lot of advancements in artificial intelligence, but have limitations in terms of learning from small data, handling uncertainty and model selection. We develop Bayesian deep learning models which could overcome these limitations using Bayesian learning idea. We work on developing efficient inference techniques for Bayesian deep learning models. On the applied side, we work on developing Bayesian deep learning models for problem arising in autonomous driving vehicles.

Neural Differential Equations

Research focuses on designing continuous depth deep learning models inspired from differential equations. Treating the computation of intermediate feature representation in deep learning models as a solution to differential equations has tremendous advantage in terms of model selection and reduced number of parameters. This field is in an early stage with lot of promises, we focus on developing various differential equation based neural networks for machine learning problems.

Continual learning

Humans learn continuously over time, adapting and learning new tasks. However, deep learning models exhibit catastrophic forgetting when trained on a sequence of tasks. We work on developing deep learning models, which can overcome catastrophic forgetting through generative replay and Bayesian learning techniques such as variational continual learning. We work on continual learning problems in both vision and natural language processing.

Generative Models for language and vision

Research focuses on developing generative models such as generative adversarial networks , variational autoencoders and normalizing flows , which can model the distribution of data and generate samples from them. We work on overcoming the limitations of the existing generative models and develop them for problems in vision and language.

Bayesian Non-parametrics

Bayesian non-parametric models allow one to learn rich and flexible models due to their non-parametric nature and allow the model complexity to be determined by the data. This helps to overcome the problem of model selection to a great extent and helps to model uncertainty better. Current work focusses on developing Bayesian non-parametric models such as Gaussian processes and efficient inference algorithms for them for various learning problems. We work on developing effective GP based algorithms for global optimization techniques such as Bayesian optimization. Another major line of work is on Bayesian non-parametric deep learning models such as deep Gaussian processes, and developing efficient inference algorithms for them. They can generalize from small data and can automatically determine model architecture to a great extent. We develop novel deep Gaussian process models for problems in vision and language processing.

Social Network Analysis

Online social networks provide a platform for sharing information at a massive scale. In order to leverage this data, we work on various problems arising in social media like Twitter and location based social networks such as information diffusion, user activity modeling , semantic annotation etc. We develop models based on point processes such as Hawkes process and graph neural networks to address these problems. We have also worked on problems like rumour detection, disaster management, and topic detection and tracking in social media. We also work on analyzing and solving problems arising in community question answering sites such as stack exchange.

Machine Learning for Astrophysics

With the widespread use of machine learning across various domains, we use machine learning and deep learning models to solve various problems related to astrophysics such as Redshift prediction, and Galaxy morphology classification.

Data Science

We can use machine learning to solve various real-world problems. One such problem is traffic data management. We use various machine learning and deep learning algorithms to understand and model traffic congestion and hence manage it efficiently. We also work on analyzing and developing models for solving problems arising in several other domains such as Telemetry data, ESG data, and time-series data.

Funded Projects


  • Sony research grant and SERB core research grants for Continual learning.

  • Unrestricted research grant from Accenture Technology for learning from alternative data sources.

  • Nvidia Project on Bayesian deep learning for computer vision and autonomous driving.

  • DST funding for the project Towards Developing Next-generation Deep Learning

  • DST funding for the project Machine Learning for Astrophysical Data Analysis

  • JICA CKP funding for Bayesian Deep Learning in collaboration with Dr. Emtiyaz Khan from RIKEN Centre for Advanced Intelligence Project, Tokyo.

  • DST SERB funding for rumour detection in social media

  • Smart Cities for Emerging Countries Based on Sensing, Network, and Big Data Analysis of Multimodal Regional Transport System (M2Smart) (Funded by JICA / JST SATREPS, 2017-2022)

  • Projects under the Technology Innovation Hub for Autonomous Navigation (TIHAAN)