Research contributions and achievements
This work presents a hybrid training method for Probabilistic Circuits that combines the generalizability of EM with the speed of SGD. We implement a Hessian-based regularizer to guide models toward flatter optima, reducing overfitting. We prove that the Hessian trace (sharpness proxy) is efficiently computable for PCs, enabling closed-form updates.
This invention presents a novel method for optimizing feeder arrangements in SMT pick-and-place machines. The algorithm increases components-per-hour (CPH) while meeting all manufacturing constraints. The approach solves combinatorial optimisation problem using Genetic Algorithms, Mixed-Integer Linear Programming (MILP), and Nonlinear Programming techniques.
AIR 73 (2025) & AIR 155 (2026)
Data Science and Artificial Intelligence
Highest CGPA
Among Data Science students for 1st Year (2022-'23), 2nd Year (2023-'24), and 3rd Year (2024-'25) at IIT Palakkad
12th Place (out of 23 IITs)
Representing IIT Palakkad - Dynamic Agentic RAG for Information Retrieval