I'm a Research Intern at Microsoft Research Bangalore (from November 2026), working on embeddings, and a Data Science student at IIT Madras.
Most of my work comes back to one question: what is actually going on inside a model, both when it works and when it doesn't? At Precog, IIIT Hyderabad, with Prof. Ponnurangam Kumaraguru, I studied interpretability in multimodal LLMs, looking at how grounded conversational models connect what they say to what they see. Before that, at VISTA Lab, IISc Bangalore with Prof. Punit Rathore, I worked on self-supervised learning for 3D medical imaging, which became our IJCAI-ECAI 2026 paper. I got to present it in Bremen in August.
I learn best by building things from scratch. I've re-implemented 18+ deep learning papers in NumPy and PyTorch, trained a small Mixtral-style mixture of experts, and wandered into side quests like getting LLMs to be funny on purpose. That same habit is why there's a neural network training on this page.
Outside research I'm usually reading, playing the blues on my electric guitar, or watching shows and anime. I'm always happy to talk about interpretability, representation learning, or anything else, so feel free to reach out.
Official code for our IJCAI-ECAI 2026 paper: a simple, lightweight self-supervised pre-training framework for 3D medical images. Patches are perturbed two ways, and one shared encoder learns to undo and explain the damage, giving spatially aware, transferable representations while using substantially less memory, compute and training time than state-of-the-art pipelines.
A simplified re-implementation of the Mixtral of Experts (MoE) architecture.
A personal deep dive into 18+ foundational and cutting-edge deep learning papers, implemented from scratch in NumPy and PyTorch. The goal: build intuition by understanding the "why" behind each methodβnot just the code.
Can LLMs be funny on purpose? This project explores controlled joke generation using Plan-Search, and ranks results via LLM judges with diverse humor styles.
Ranked Top 25 out of 1,200+ participants in a Kaggle competition to predict client subscription to term deposits based on telemarketing campaign data.
Reach out to work on projects or collaborate on ML research: kabir25.dl@gmail.com