Baseline Event-Driven Tracking & Fusion (Radar/EO). C++ core with Python sim/eval. Demonstrates EKF state estimation and GNN with Mahalanobis gating to manage asynchronous data, clutter, drops, and filter calibration trade-offs.
A project showcasing a scheduling algorithm to optimize warehouse robot coordination for efficient product delivery and order fulfillment.
Quantitative analytics and risk engine implementing seasonal commodity pricing, an event-driven asset storage simulator, credit risk valuation (EL), and an optimal FICO score quantizer utilizing dynamic programming log-likelihood optimization.
A project to implement a subset of the functionality of Ruby's Enumerable.
ALX Higher level programming projects are solutions to solve complex challenges
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.
Classification (Pattern Recognition) algorithm development with Bayesian, Anti-Bayesian, Decision Tree and Dependence Tree concepts. [IEMIS 2020, Springer AISC]
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.
Adaptive hierarchical EMS model for the 2012 Chevrolet Volt Plug‑In Hybrid Electric Vehicle using a Genetic Algorithm and a Deep Learning predictor trained on GA‑optimized control actions.
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.
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.
Simple free-space communication system with visible light as part of my project during my master's first semester at the University of Jyväskylä
Real-time radar perception pipeline for automotive systems with multi-target detection, velocity estimation, and spatial localization.
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.
A collection of Python assignments and algorithmic exercises from the Innopolis University programming course. Includes implementation of core data structures, basic algorithms, and weekly coding challenges.
Gaussian Mixture Model (GMM) trial coding
A focused fork of an anomaly detection library, dedicated to enhancing and extending the PatchCore model.
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