As a Research Trainee at the Cognitive and Behavioural Neuroscience Laboratory, IIT Bombay, I am engaged in research at the intersection of Artificial Intelligence, Computational Neuroscience, and Cognitive Science. 

During this Traineeship, I worked on the research project titled “Why Adults with ADHD Are Inconsistent Rather Than Slow: Computational Phenotyping Using Drift Diffusion Modelling and Machine Learning,” analysing CPT-II data from 99 adults in the HYPERAKTIV dataset using drift diffusion modelling and interpretable machine learning. I conducted a detailed literature survey to identify existing gaps in DDM-based ADHD research, independently built the DDM analysis pipeline, and developed classification models achieving strong diagnostic discrimination (ROC-AUC up to 0.802). The objective is to develop interpretable computational models that can provide deeper insights into neurocognitive mechanisms while supporting data-driven approaches for behavioral phenotyping. This opportunity allows me to collaborate within a leading research environment and contribute to advancing AI applications in neuroscience and mental health.

Guide: Prof. Rashmi Gupta, CBN Lab