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 …


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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 …

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Structural Identifiability Toolbox

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

Introduction

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 …


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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 …


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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 …


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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

Organization

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

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

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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.

Introduction

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 …


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Linear Regression as the Simplest Classifier

Posted on Mon 24 February 2020 in Posts • Tagged with machine learning, linear regression, python, scikit-learn, statistical learning

In this post I wanted to describe a simple application of a linear least squares method to a problem of data classification. It is a naive approach and is unlikely to beat more sophisticated techniques like Logistic Regression, for instance.

Imports

Some imports we are going to need for this …


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How to write a decent training loop with enough flexibility.

Posted on Sat 15 June 2019 in Posts • Tagged with deep learning

In this post, I briefly describe my experience in setting up training with PyTorch.

Introduction

PyTorch is an extremely useful and convenient framework for deep learning. When it comes to working on a deep learning project, I am more comfortable with PyTorch rather than TensorFlow.

In this quick post, I …


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RiCNN and Rotation Robustness of ConvNets. A Paper Review

Posted on Sat 15 June 2019 in Posts • Tagged with review series, deep learning, computer vision

Lately, I have been reading more papers on modern advances in deep learning in order to get a clear view of what problem I want to focus on during my PhD research.

There is a lot of information to process and an incredible amount of papers are being published from …


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