R&D Engineer | ML Engineer
Developing rigorous tools from physics, mathematics, and computer science to understand how large neural networks learn, compute, scale, reason, and imagine.
I instrument and develop rigorous tools from physics, mathematics, computer science, and theoretical neuroscience to understand how large neural networks learn, compute, scale, reason, and imagine. I concomitantly use machine learning tools to understand physical systems and their behavior, like particle physics and quantum mechanics.
Employing and developing tools from mathematics and physics in computational Physics, Large Neural Networks, and Pattern Recognition for scale.
Creating educational materials on Artificial Intelligence and Machine Learning, focused on breaking down complex technical concepts for millions of learners worldwide.
Teaching courses on Efficient Large Language Models Customization, Computer Vision for Industrial Inspection, Fundamentals of Machine Learning, and Building Transformer Based NLP systems.
Research in machine learning applications for materials science and manufacturing processes.
Research and development in multi-agent systems, AI agents, and distributed systems.
Jul 2025
Reinforced the power of Kolmogorov-Arnold approximators for interpretable machine learning by utilizing Gaussian B-splines. Demonstrated universality of KANs as a generalization converging to mathematical approximators like Fourier Series and Taylor Series.
Jan 2025 - May 2025
Proposed a quantum restricted Boltzmann machine (QBM) that creates accurate quantum deep memory representations via quantum annealing and quantum Boltzmann sampling. Addressed computational complexity limitations in high-dimensional Hilbert spaces.
Jan 2025
Python implementation of the Kolmogorov-Arnold Neural Networks Framework based on the Kolmogorov-Arnold Representation Theorem for regression and classification tasks.
Zenodo · Sep 2024
Introduced a novel approach combining Chain-of-Actions (CoA) and Retrieve-As-You-Go (RAYGo) to enhance LLM agent planning. Enabled Mistral-7B-v0.2 to outperform GPT-4 by over 29% in multi-tool calling, and elevated GPT-4's performance from 56% to 98% on action calling.
Zenodo · Jan 2023
Presented Mamba, a new architecture leveraging structured state space models that scales linearly with sequence length, achieving 5× better throughput than Transformers while maintaining state-of-the-art performance across languages, audio, and genomics.
Zenodo · Aug 2023
Comprehensive overview of the Transformer architecture, its self-attention mechanism, and its role in revolutionizing natural language processing and modern machine learning applications.
Engineering, Applied Sciences and Technology
Aug 2022 - Jun 2027
ACM, IEEE, GDSC, INSAT Press