Social Inference from Relational Visual Information
Advisors: Dr. Leyla Isik [Link]
We hypothesize that humans rely on relational visual information in particular, which is lacking from standard neural networks, and develop a new relational, graph neural network model, SocialGNN. We find that SocialGNN accurately predicts human interaction judgments across both animated and natural videos, suggesting that humans can make complex social interaction judgments without explicit simulation or inference about agents’ mental states, and that structured, relational visual representations are key to this behavior.
BCQ4DCA: Budget Constrained Deep Q-Network for Dynamic Campaign Allocation in Computational Advertising
TCS Research and Innovation [Link]
We developed a deep reinforcement learning model for dynamic optimization of budget-constrained campaign allocation.
Network Analysis of Neuro-Cognitive Processes: Studying McGurk Effect using EEG Data
Advisors: Dr. Arpan Banerjee, Dr. Ganesh Bagler, Dr. Dipanjan Roy [Link]
The research work involved understanding multi-sensory perception involving auditory and visual cues using the McGurk effect. My focus in this research was to understand the network properties of the brain using EEG data obtained from multiple subjects.
Manasi Malik, and Leyla Isik. Relational Visual Information Explains Human Social Inference: A Graph Neural Network Model for Social Interaction Recognition. PsyArXiv, 3 Nov. 2022. (submitted for peer review)
Manasi Malik, Leyla Isik. Social Inference from Relational Visual Information. Journal of Vision 2022 (abstract only)
M. Malik, G. Gupta, L. Vig, and G. Shroff, BCQ4DCA: Budget Constrained Deep Q-Network for Dynamic Campaign Allocation in Computational Advertising, IEEE International Joint Conference on Neural Networks, 2021 (IJCNN ’21).
Yashaswi Rauthan, Vatsala Singh, Rishabh Agrawal, Satej Kadlay, Niranjan Pedanekar, Shirish Karande, Manasi Malik, and Iaphi Tariang, Avoid Crowding in the Battlefield: Semantic Placement of Social Messages in Entertainment Programs, International Workshop on AI for Smart TV Content Production, Access and Delivery (AI4TV ’20)
Manasi Malik, Ganesh Bagler, and Arpan Banerjee. Network analysis of neuro-cognitive processes: studying mcgurk effect using EEG data, IIITD, 2019.
Talk: Manasi Malik, Leyla Isik. Social Inference from Relational Visual Information, Vision Sciences Society (VSS ’22), Florida, USA
Poster: Manasi Malik, Leyla Isik, Social Inference from Relational Visual Information: An Investigation with Graph Neural Network Models, Conference on Cognitive Computational Neuroscience (CCN’22), SanFrancisco, USA (poster).