Research
Research groups
Current research
My doctoral research highlighted the possibility of deploying deep learning models for improving the classification performance of state of the art kernel methods like support vector machines. The research showed how such a hybrid approach can combine the best of both the paradigms for computer vision problems. The research experience gained from my doctoral program cultivated and nurtured lifelong skills of working on daunting ideas that can create a difference. From the last six years, I have extended this research by focussing on developing Fisher kernel methods that can bridge the gap between the two popular frameworks: Deep learning and Kernel methods. This research has helped me in winning several national and international research grants from the industry and academia. I am recipient of a Startup Research Grant, a National Grassroots ICT Research Initiative Fund, a National ICT Research and Development Grant, and an have received the Best Paper Award at ICPRAM, in 2017.
I have also worked as a technical reviewer of the following journals, conferences and organisations: IEEE Transactions on Neural Networks, IEEE Access, IET Electronics letters, Neural Processing letters, Journal of Information Sciences. Journal of Pattern Recognition, IGNITE National ICT R&D, Pakistan, IEEE International Conference on Emerging Technologies (ICET) and IEEE International Conference on Industrial and Information Systems (ICIIS).
My current research interests include deep learning methods for graphs in NLP. Im also interested in self-supervised learning methods for deep models to develop intelligent chat bots. Such learning techniques are useful for online lifelong and continual learning, where most of the encountered real world data is unlabelled. Zero shot and one shot learning techniques are also of significant interest to me in this regard.