Simulating Textbook Probability Problems in Julia

Posted on Mon 07 February 2022 in Posts • Tagged with Julia, Probability, Simulation


I am fascinated by the concept of Monte-Carlo simulation: you can, in principle, simulate random events and see how these simulations differ from reality (on a small scale, at least!)

Here I would like to show a couple of simple probability theory textbook problems and my attempt at simulating …

Continue reading

Installing Julia

Posted on Sun 06 February 2022 in Posts • Tagged with Julia


In this quick tutorial, I will share how to install Julia language on a Mac computer, setup symlinks to run it from command line and then will go through a couple of problems in probability and their Julia language simulations.

Installing Julia on macos

We are going to install …

Continue reading

Parameter identifiability in Julia

Posted on Mon 06 December 2021 in Posts • Tagged with Julia, Parameter Identifiability

In this post I will present a tutorial on a Julia package called StructuralIdentifiability.jl which I help maintain and contribute to. In the next post I will present a similar introduction to another package I help develop called SIAN.jl, but that's for another time.

This tutorial will be …

Continue reading

Google Summer of Code: Final Results

Posted on Tue 17 August 2021 in Posts • Tagged with Julia, GSoC

GitHub release GitHub stars

Over the course of the summer, my work resulted in successfully ported code of StructuralIdentifiability.jl, a package that can help researchers who use ordinary differential equations in their work. The package allows answering queries about individual identifiability of parameters and their combinations.

The StructuralIdentifiability.jl package is ready to …

Continue reading

First Month In GSoC

Posted on Sun 25 July 2021 in Posts • Tagged with Julia, GSoC

Project Updates

We slightly churned our project idea from the original algorithm implementation into an inclusion of a StructuralIdentifiability.jl package develop by my colleague Gleb Pogudin. The package is currently part of SciML with more updates to come!

Here is a list of things it can do:

  • check local …

Continue reading

Structural Identifiability Toolbox

Posted on Sun 25 July 2021 in Posts • Tagged with Maple, Symbolic Computing


In this repository, I will describe our recently-released Structural Identifiability Toolbox, a web-based application for assessing parameter identifiability of differential models.

Click here to checkout the application! Read on to learn more.

Why is it better?

The program is fast, free, and is available in any web-browser, including mobile …

Continue reading

My Google Summer of Code Project

Posted on Tue 08 June 2021 in Posts • Tagged with Julia, GSOC

About The Project

Problem Formulation

The problem of parameter identifiability is one of the most crucial issues arising in systems biology. To take a look at a problem of identifiability, we must first describe a setting in which it arises. Systems biology deals with biological processes that are described by …

Continue reading

How I had to translate Matlab code into Maple

Posted on Tue 18 August 2020 in Posts • Tagged with python, regular expressions, matlab, maple

In this short post, I wanted to point out one interesting application of regular expressions I had to work on for my PhD research project. The code was meant as a technical tool to help tranlate some ordingary differential equation models from numerical (Matlab) to symbolic (Maple) code.

The original …

Continue reading

NumPy-Learn, A Homemade Machine Learning Library

Posted on Sun 14 June 2020 in Posts • Tagged with machine learning, python, numpy, deep learning

In this post, I expand on a little class/self-teaching project that I did during the Spring 2020 semester.

NumPy-Learn: A Homemade Machine Learning Library


In this section we will discuss the main organization of the library:

  • How the layers are built
  • How loss functions work
  • How a stochastic …

Continue reading

Three Ways to Deal With Imbalance

Posted on Mon 02 March 2020 in Posts • Tagged with machine learning, logistic regression, python, scikit-learn, statistical learning

In this post, I put together an interesting example of what to do with imbalanced datasets and why precision and recall matter.


The following is part of a Machine learning assignment I had to do while at CUNY. This particular example illustrates quite well the importance of understanding various …

Continue reading