A discrete-time Python-based solver for the Stochastic On-Time Arrival routing problem

Set of Jupyter (iPython) notebooks (and few pdf-presentations) about things that I am interested on, like Computer Science, Statistics and Machine-Learning, Artificial Intelligence (AI), Financial Engineering, Optimization, Stochastic Modelling, Time-Series forecasting, Science in general... and more.

Stochastic SIR models; adding age-structures and social contact data for the spread of covid-19. Lattice model for identifying and isolating hotspots. This has been further developed into a network(graph) of multiple clusters(lattices) and tracing the infection in such a population.

3rd Annual Undergraduate Quantitative Biology (UQ-bio) Summer School

Classical models implemented from a Markov operator's perspective

Weather Generators with Bayesian Networks

Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.

Code and data files necessary for reproducing cellular-automaton model of human spread across Sahul

C# pricer that allows users to price a wide range of financial products.

Adaptive Signal Processing (2020 Fall)

Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing

Bayesian inference of stochastic cellular processes with and without memory in Python.

🔢 Python module that calculates probabilities for a random walk in 1-dimensional discrete state space

Application of the ARIMA model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.

This script presents a simple stochastic description to model cell population distribution in the phases of the cell cycle

Application of the ETS model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahwalnagar District, Punjab, Pakistan.

Implementing Stochastic Models in Queuing Theory

Application of the ARIMA model to forecast PET patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.