Projects

Currently, I am working on following two Ph.D. projects. To view all the projects I and my group are working, please visit HMI site.

Robot-Assisted Diagnosis of Autism Spectrum Disorder


Early detection of Autism Spectrum Disorder (ASD) is crucial for deciding the appropriate educational and behavioral intervention at the most suitable time. However, there are no absolute biological markers for autism and accurate diagnosis of ASD requires extensive training and experience acquired over the years and such expertise is limited to few individuals centered in metropolitans and is beyond the reach of most of the affected population. robot-assisted interventions have found increasing acceptance as a support tool for therapy and education for children with autism (CwA). CwA prefers to interact with technological tools rather than human beings and hence, robot assisted diagnosis systems can be employed to improve the early detection of ASD in an automated assessment manner making the ASD diagnosis more objective. The overarching goal of this project is to develop a robot-assisted system for the diagnosis of the ASD suitable to the children of Indian ethnicity. Upon validation, the benefits of this project can be made available to the unreachable children masses of India.

Ph.D. Candidate: Ashwini B (IIIT-Delhi)

Supervisor: Dr. Jainendra Shukla (IIIT-Delhi)

Funding: Supported by Start-up Research Grant of Science and Engineering Research Board, Government of India (Ref: SRG/2020/002454)

 

Deep Learning to understand the user behavior for marketing


Quantitative and qualitative research is often inhibited by the fact that the users have to articulate exactly how they feel about the products in the form of focus groups and surveys, which can be plagued by issues like participant bias. Neuromarketing is a way to supplement these methods with an understanding of how users actually respond emotionally when they use a product, instead of them having to remember and describe what they were feeling afterward. With the ultimate goal of obtaining information on the underlying cognitive processes and emotions that influence user behavior, this research aims to develop novel artificial intelligence and machine learning methods using facial coding and physiological signals including heart-rate variability (HRV), galvanic skin response (GSR), electroencephalography (EEG) for understanding the user behavior and to apply these learning to improve the product in a wide range of application areas including advertisements, automobile, video games, movies. 

Ph.D. Candidate: Milon Bhattacharya (IIIT-Delhi)

Supervisor: Dr. Jainendra Shukla (IIIT-Delhi)

Co-Supervisor: Dr. Abhinav Dhall (Monash University, Australia)

Funding: Supported by Intramural Start-up Grant from IIIT-Delhi