AIRC Team

I completed my B. Tech and M. Tech in Computer Science and Engineering from DIT University, Uttarakhand, India and Jaypee Institute of Information Technology, Noida, India, respectively. I also worked as a research intern at Remote Sensing Lab IIT Roorkee, India. I also served as Assistant Professor in the Department of Computer Science and Engineering & Information Technology at the College of Engineering Roorkee, India. With a dream to study further, gain in-depth knowledge of my subject and to be able to do research for the benefit of society, I further got an opportunity to pursue my PhD degree from Machine Learning labs (TU (Technological University) Dublin) with a full scholarship provided by the Science Foundation Ireland. My research interests are Explainable AI (Artificial Intelligence), AI for healthcare and Climate. I would like to draw your attention towards deep neural networks (DNNs), such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), that have gotten a lot of attention in recent years among these machine learning techniques. However, it is observed that these best performing models are way too complex and opaque due to their complex deep architecture and non-linearity. Henceforth, they lack explainability and are also often called ‘black-box’ models as they do not justify their decisions and predictions. Therefore, it becomes difficult for humans to trust them. To encounter this issue of interpretation of deep neural networks such as RNNs, for my PhD, I will be exploring Post hoc explanations for RNNs using state transition representations such as graphs, finite state machines and other state representations. This research will help the research scholars and people in the industry to provide a better interpretation of the applications built upon RNNs across various domains such as finance, healthcare & also build the trust of humans in AI applications.