Rajiv Ratn Shah currently works as an Assistant Professor in the Department of Computer Science and Engineering (joint appointment with the Department of Human-centered Design) at IIIT-Delhi. He is the founder of MIDAS lab at IIIT-Delhi. He received his Ph.D. in computer science from the National University of Singapore, Singapore. Before joining IIIT-Delhi, he worked as a Research Fellow in Living Analytics Research Center (LARC) at the Singapore Management University, Singapore. Prior to completing his Ph.D., he received his M.Tech. and M.C.A. degrees in Computer Applications from the Delhi Technological University, Delhi and Jawaharlal Nehru University, Delhi, respectively. He has also received his B.Sc. in Mathematics (Honors) from the Banaras Hindu University, Varanasi. Dr. Shah is the recipient of several awards, including the prestigious Heidelberg Laureate Forum (HLF) and European Research Consortium for Informatics and Mathematics (ERCIM) fellowships. He has also received the best paper award in the IWGS workshop at the ACM SIGSPATIAL conference 2016, San Francisco, USA and was runner-up in the Grand Challenge competition of ACM International Conference on Multimedia 2015, Brisbane, Austraila. He is involved in organizing and reviewing of many top-tier international conferences and journals. Recently, he has organized a workshop on Multimodal Representation, Retrieval, and Analysis of Multimedia Content (MR2AMC) in the conjunction of the first IEEE MIPR 2018 conference. His research interests include multimedia content processing, natural language processing, image processing, multimodal computing, data science, social media computing, and the internet of things.

Specifically, his current research interests include:

• Multimodal deep learning based healthcare solutions

• Multimodal fake news detection using deep learning techniques

• Multimodal semantic and sentiment analysis of user-generated social media content

• Event detection and recommendation on social media

• Multimodal multimedia search, retrieval, and recommendation

• Deep learning based multimedia systems