Dr. Sanatan Sukhija is an Assistant Professor in the Department of Computer Science and Engineering at Mahindra University École Centrale School of Engineering. He earned his Doctorate from the Department of Computer Science and Engineering at Indian Institute of Technology Ropar in January 2020.
His broad area of research is Transfer Learning. Over the last 5 years, he developed three robust frameworks for learning in those domains where labeled data is scarce or absent. The research has led to multiple publications at top-tier venues (AI Journal, AAAI, IJCAI, ECML-PKDD, IJCNN, WCCI etc.).
Thesis Title: Leveraging Label Space Similarities for Transfer Learning
Thesis Advisor: Narayanan C Krishnan (http://cse.iitrpr.ac.in/ckn/people/ckn.html)
Indian Institute of Technology Ropar
July 2014 – Jan 2020
Malaviya National Institute of Technology Jaipur
July 2011 – May 2013
Computer Science and Engineering
Maharaja Agrasen Institute Of Technology, GGSIPU, New Delhi
Aug 2007 – May 2011
UGC-NET 2012 (JRF and Lectureship)
GATE 2013 (99.58 percentile)
Machine Learning (Theoretical and Applied), Transfer Learning and Deep Learning.
Given plentiful labeled training data, deep learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition. I have worked on specific machine learning problems, in particular, transfer learning and domain adaptation. This research area focuses on learning in those domains where the amount of labeled training data is scarce or absent. Preferably, for the next few years, I intend to extend my research to the deep transfer context where the ideas from shallow transfer algorithms can be leveraged to learn robust deep models for industry/healthcare related problems or applications inspired from multimedia and IoT. I am also open to working on interesting and challenging inter-disciplinary problems in other fields, including but not limited to, data mining, ubiquitous computing, and pervasive computing. Auto-ML, Adversarial Learning, Meta-Learning, Unsupervised Learning, Zero-Shot Learning, and Reinforcement Learning are some of the other areas that interest me. I am looking forward to working with students who are technically strong in Mathematics and Computer Science.
Member of ACM, AAAI and IEEE.
Reviewer for IEEE TKDE, International Journal of Intelligent Systems (Wiley), IEEE ICCV and IEEE ICIP.