Faculty
Faculty
Associate Professor
Department of Computer Science and Engineering
Affiliated to Department of Artificial Intelligence
My research interest lies in developing machine learning and artificial intelligence algorithms inspired by the way human learning works. Towards this end, we use deep learning, Bayesian learning, continual learning, causal learning, multimodal learning, neural networks, stochastic processes, and differential equations to develop novel artificial intelligent algorithms. Through these techniques, we aim to bridge the gap between human learning and machine learning, towards a more responsible and general Artificial Intelligence.
PhD
Department of Artificial Intelligence, IIT Hyderabad
Thesis : TBD
Research Interests:
Responsible AI
Generative AI
IIT Hyderabad - Deakin University Joint Doctoral Program
Thesis : On Continual Learning and Domain Generalisation
Research Interests:
Continual Learning
Domain Generalisation
Machine Unlearning
Computer Vision
Center for Interdisciplinary Programs, IIT Hyderabad
Thesis : Deep Learning for Astrophysics
Research Interests:
Astrophysics
Bayesian Statistics
External PhD from Honeywell at Dept. of Artificial Intelligence, IIT Hyderabad
Thesis : Bayesian Causal Learning
Research Interests:
Causal Discovery
Bayesian Learning
Computer Vision
Machine Learning/Deep Learning
IIT Hyderabad - Swinburne University of Technology Joint Doctoral Program
Thesis : Continual learning in deep neural networks
Research Interests:
Continual Learning
Domain Generalisation
Computer Vision
Deep Learning
Department of Artificial Intelligence
Thesis : Image Captioning for Autonomous Navigation and Data Acquisition Systems
Research Interests:
Image Captioning
Natural Language Generation
Multi-task learning
Natural Language Processing
Department of Artificial Intelligence, IIT Hyderabad
Thesis : Uncertainty Modelling in. Natural Language Processing
Research Interests:
Continual Learning
Image Captioning
Computer Vision
Deep Learning
M Tech
Department of Artificial Intelligence
Research Interests:
Bayesian Deep Learning
Time Series Analysis
Natural Language Processing
Department of Artificial Intelligence
Research Interests:
Continual Learning
Domain Adaptation
Deep Learning
Department of Artificial Intelligence
Research Interests:
Implicit layer neural networks
Non linear dynamical systems
State Space Models
Neural Architecture Search
Department of Artificial Intelligence
Research Interests:
Temporal Point Processes
Large Language Models
Domain generalization
Deep Learning
Department of Artificial Intelligence
Research Interests:
Continual Learning
Machine Unlearning
Computer Vision
Explainable AI
Department of Artificial Intelligence
Research Interests:
Causal Learning
Bayesian Inference
Time Series Modelling
Deep Learning
Department of Artificial Intelligence
Research Interests:
Natural Language Processing
Continual Learning
Information Retrieval
Neural Machine Translation
Alumni
PhD
Postdoc at University of Manchester, United Kingdom
Thesis : Spatial and temporal modeling of online social networks.
Research Interests:
Probabilistic Social Network Analysis
Machine Learning / Deep Learning
Information Retrieval
Natural Language Processing
Postdoc at National University of Singapore
Thesis : Neural Differential Equations : Continuous Deep learning from Differential Equations.
Research Interests:
Inference Techniques
Deep Gaussian Process
Neural Ordinary Differential Equations
Postdoc at Alto University, Finland
Thesis : Advancing Generative Adversarial Networks for Modeling Data
Research Interests:
Deep Learning.
Computer Vision.
Generative Modelling.
Continual learning.
Suvodip Dey
PostDoc at University of Illinois Urbana-Champaign, USA
Thesis : Scalable and interpretable models for dialogue generation
Research Interests
Natural Language Processing
Dialogue Systems
M Tech
Past Students
Vaibhav Singh (M.Tech, currently at Goldman Sachs, Bangalore)
Worked on convolutional Deep Gaussian Processes
Vinayak Kumar, Vaibhav Singh, P. K. Srijith, Andreas Damianou, Deep Gaussian Processes with Convolutional Kernels, Uncertainty in Deep Learning workshop at Uncertainty in Artificial Intelligence (UAI), 2018
Sherin Thomas (M.Tech, currently at Schlumberger, Pune)
Worked on Bayesian point processes for recommendation systems
Sherin Thomas, P. K. Srijith, and Michal Lukasik. 2018. A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems. In User Modeling, Adaptation and Personalization (UMAP), 2018
Shamik Kundu (M. Tech, currently at SMS data tech, Japan)
Worked on deep learnign for social media analysis
Shamik Kundu, P.K. Srijith, M. S. Desarkar. Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach. In FOSINT-SI at Advances in Social Networks Analysis and Mining (ASONAM) 2018.