Apoorva Chawla is a Postdoctoral Researcher at the Department of Electronics Systems, Norwegian University of Science and Technology (NTNU), Norway, under the supervision of Prof. Pierluigi Salvo Rossi. She is associated with the SIGNIFY project, a joint NTNU–SINTEF initiative focused on sensor validation for digital twins of safety-critical systems. Her research involves developing model-based, data-driven, and hybrid-analytics methodologies for sensor-fault detection, isolation, and accommodation in natural gas, hydrogen blended natural gas and carbon dioxide transport infrastructure, supporting carbon capture and storage (CCS) and low-carbon energy infrastructure. Current work within SIGNIFY extends these efforts toward hybrid-analytics solutions that combine signal processing and machine learning techniques. Recently, she contributed to the HYDROGENi project, where a robust multimodal data-imputation framework is developed to ensure accurate and semantically complete contextual information for efficient monitoring and decision-making in hydrogen-related industries.
Prior to joining NTNU, she earned her Ph.D. in Electrical Engineering from the Indian Institute of Technology (IIT) Kanpur, India, under Prof. Aditya K. Jagannatham, focusing on designing fusion rules for distributed detection in millimeter-wave and massive MIMO systems. She received her M.Tech. in Electrical Engineering from IIT Kanpur, India, with a thesis on training-based frequency-selective MIMO channel estimation in multi-user MIMO MC-CDMA systems, supervised by Prof. Aditya K. Jagannatham. She obtained her B.Tech. in Electronics and Communication Engineering from Inderprastha Engineering College, Gautam Buddh Technical University, India, with a major project on the design and characterization of high gain microstrip antenna on EBG substrates, advised by Dr. Pramod Kumar.
Her research falls within statistical signal processing, wireless communication, machine learning and information fusion.