👨🏻‍💻 I am a final-year student pursuing a BTech (Materials Science) + MTech in Healthcare Informatics at IIT Bombay.

🔬 I am passionate about applying AI in healthcare, focusing on psycholinguistics, NeuroAI, and Language Processing.

📚 I’ve completed my Master’s Thesis at the junction of natural language processing (NLP), machine learning (ML), and computational social science.

Research Experience

Mental Disorder Identification through Linguistic Markers | Master’s Thesis, IIT Bombay

Guide: Prof. Pushpak Bhattacharyya, CFILT Lab

• Proposed a unique method to convert social media text into time series data for post‑level analysis of mental disorders
• Developed a novel framework for mental disorder identification via foundational deep learning models which surpasses the performance of BERT‑based approaches by 5% in the F1 score on mental conditions: Depression, Self‑harm, and Anorexia
• Explored semantic overlaps among these disorders, underscoring the value of cross‑domain data in mental health research

Cognitively Inspired Hallucination Detection | Research Project, UT Austin & IIT Bombay

Guide: Prof. Abhijit Mishra & Prof. Pushpak Bhattacharyya

• Curated eye‑tracking data with 500 instances for the task of hallucination detection and developed a BERT‑based framework
• Proposed a novel attention bias framework inspired by human behavior for detecting hallucinated texts

Multimodal Mixup Contrastive Learning for Multimodal Classification | Research Project, Monash University & IIT Bombay

Guide: Prof. Kshitij Jadhav & Dr. Deval Mehta

• Implemented a multimodal contrastive learning objective for image‑text classification using the extension of mixup strategy
• Experimented using unimodality supervision, cross‑attention on diverse datasets (N24News, ROSMAP, BRCA, and Food‑101)

Professional Experience

I have professional experience working as an AI Student Researcher at Assert AI.
There, I deployed customized YOLOv4 models for surveillance tasks leveraging the Nvidia Jetson series GPU accelerators, generated tailored datasets and trained YOLOv4 models for diverse object detection and classification scenarios.