Adil Khan is a Professor of Machine Learning (ML) and Artificial Intelligence (AI). He has more than sixteen years of research, development, and teaching experience in AI and ML. His work comprises both traditional machine learning methods and deep learning techniques.
He has developed industrial solutions for Action and Expression Recognition, Remote Sensing, Medical Image Analysis, Natural Language Processing, Crime Detection, and Accident Detection problems. As for theoretical research, his work aims to help ML find answers to some of the most critical questions. For example, how to train ML models in the absence of large amounts of training data? How to improve the generalization of deep neural networks? How to enable ML models to adapt and generalize to new target domains? How to protect such models from adversarial attacks? How to ensure that these models would make fair decisions? What causes catastrophic forgetting in deep neural networks, and how can we overcome it?