I am an Associate Professor at the department of Computer Science and Engineering, and affiliated to the department of Artificial Intelligence at Indian Institute of Technology (IIT) Hyderabad. Previously, I was a post-doctoral researcher at the Department of Computer Science, University of Sheffield and at the Computing and Information Systems, University of Melbourne. I completed my Ph.D. at the Department of Computer Science and Automation, Indian Institute of Science, Bangalore, Masters at Computer Science and Engineering, IIT Bombay and Bachelors at Computer Science and Engineering, NIT Calicut.

Research Area : Artificial Intelligence, Machine Learning, Deep Learning, Bayesian Learning, Continual Learning.

Specific Interests : Bayesian deep learning, Continual deep learning, Continuous deep learning, Neural differential Equations, Bayesian non-parametrics, Stochastic processes, Gaussian processes, Point processes, Inference algorithms, uncertainty quantification, spatio-temporal and generative modelling.

Applications : Computer vision, natural language processing, social network analysis, astrophysics, autonomous navigation

Teaching machines to learn human techniques!

My research interest lies in developing machine learning and artificial intelligence algorithms inspired by the way human learning works. Towards this end, we use Bayesian learning, deep learning, continual learning, neural networks, stochastic processes, and differential equations to develop novel machine learning and deep learning algorithms. We develop probabilistic machine learning and deep learning models and algorithms for problems from varied domains of artificial intelligence such as computer vision and natural language processing and application areas such as social networks, web, astrophysics, autonomous navigation, smart mobility etc.

Parametric and non-parametric Bayesian models allow the incorporation of prior information and domain knowledge. Non-parametric Bayesian models such as Gaussian processes additionally 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. On the other hand, deep learning models help in effective representation learning and good generalization performance. I work on developing learning algorithms which combine the best from both worlds for e.g. Bayesian deep learning and deep Gaussian processes. Another line of research is developing continually evolving models based on point process and neural differential equations. I am also interested in analyzing time series and textual data arising from various application domains. Current research applies Bayesian reasoning and probabilistic modeling to diverse problem domains such as computer vision, language processing, social networks, traffic data, astrophysical data etc. We intend to develop learning algorithms and models which are useful not only for artificial intelligence and computer science but also in general for problems arising in other areas of science and engineering.