🤖 Implement sim2sim training based on Unitree_rl_gym, enabling motion and policy modification for enhanced robotic behavior in Beyondmimic.

A project to implement a subset of the functionality of Ruby's Enumerable.

ALX Higher level programming projects are solutions to solve complex challenges

Classification (Pattern Recognition) algorithm development with Bayesian, Anti-Bayesian, Decision Tree and Dependence Tree concepts. [IEMIS 2020, Springer AISC]

Exploration of brain-inspired computing architectures through computational neuroscience models, focusing on point neuron dynamics and spiking neural networks (SNNs) using Nengo. Bridges concepts from neuroscience, computer science, and electrical engineering.

🎨 Explore random, Nvidia-inspired projects in this unique collection, showcasing creativity and innovation for developers and tech enthusiasts alike.

A project showcasing a scheduling algorithm to optimize warehouse robot coordination for efficient product delivery and order fulfillment.

This is a mock keypad simulation project where I simulated circuit in LTspice to use that data for the test bench. I developed a Tkinter UI to generate data, which are sent to Arduino for processing, then sent back to the UI. Possible work extension could be done by substituting the mock keypad with the real one.

Simple free-space communication system with visible light as part of my project during my master's first semester at the University of Jyväskylä

Quantitative Analytics Suite A hands-on Python project inspired by JPMorgan’s quantitative research challenges. It covers four core modules: natural gas price forecasting, storage contract pricing, credit risk modeling (PD & expected loss), and FICO score quantization using DP and likelihood optimization.

A curated collection of hands-on notebooks exploring core machine and deep learning concepts. Each notebook focuses on a specific topic - from linear models and foundational elements like broadcasting and autograd to advanced tasks such as custom layers, transfer learning, sequence modeling with RNNs, and representation learning with autoencoders.

Developed a custom clustering algorithm to analyze wine data without traditional machine learning. The project standardizes features and employs mathematical formulas using NumPy to identify distinct clusters, offering insights into wine sample groupings and their characteristics.

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Gaussian Mixture Model (GMM) trial coding

🛠️ Implement efficient deformable grid sampling operations with bilinear interpolation in PyTorch, enhancing deep learning models for dense vision tasks.

🌐 Simplify web development with chapel-bwu, a powerful Chapel framework for building fast and scalable web applications.

📚 Explore a curated collection of research on Label-Free Reinforcement Learning with Verifiable Rewards for enhancing Large Language Models.

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