Mina Norouzirad MEE-na no-ROO-zee-rad

Researcher

Center for Mathematics and Applications (NOVA Math)

Biography

Mina Norouzirad is a dedicated researcher and educator in the field of statistics, currently serving as a Junior Researcher at the Center for Mathematics and Applications (NovaMath) in Caparica, Portugal, since April 2024. Previously, she completed a post-doctoral research position at the same institution from March 2021 to March 2024.

Interests

Shrinkage Estimation Methods
Robust Regression Techniques
High-dimensional Statistical Analysis
Machine Learning Applications
Bayesian Statistics
Computational Statistics

Education

Ph.D. in Statistical Inference, 2018
Shahrood University of Technology
M.Sc. in Mathematical Statistics, 2011
Islamic Azad University, Mashhad Branch
B.Sc. in Pure Mathematics, 2008
Shahrood University of Technology

Skills

Technical

Python

Data Science

R Programming

Education

Ph.D. in Statistical Inference

Shahrood University of Technology

September 2014 – December 2018 Shahrood, Iran

Thesis: Improved Estimation Strategies in Some Penalized Regression Models

Supervisor: Prof Mohammad Arashi

Research Impact:

Presented papers at 7 conferences
Published contributions in 4 journals
Advanced research in penalized regression models

📖 Read Full Thesis

M.Sc. in Mathematical Statistics

Islamic Azad University, Mashhad Branch

September 2009 – June 2011 Mashhad, Iran

Thesis: On Estimation of Loss Function

Supervisor: Prof Mohammad Arashi Advisor: Prof. Hassan Sadeghi

Research Impact:

Presented papers at 2 conferences
Published contributions in 1 journal
Focus on statistical estimation theory

B.Sc. in Pure Mathematics

Shahrood University of Technology

September 2004 – June 2008 Shahrood, Iran

Major: Pure Mathematics

Strong foundation in mathematical analysis, algebra, and applied mathematics

Experience

Junior Researcher

Center for Mathematics and Applications (NOVA Math)

April 2024 – Present Caparica, Portugal

Research focus on statistical inference, machine learning applications, and computational statistics.

Post-doctoral Researcher

Center for Mathematics and Applications (NOVA Math)

March 2021 – April 2024 Caparica, Portugal

Conducted advanced research in shrinkage estimation methods and robust regression techniques.

Lecturer

Shahrood University of Technology

September 2017 – July 2019 Shahrood, Iran

Taught various courses in statistics, probability theory, and mathematical modeling.

Lecturer

Neyshabour University

September 2018 – July 2019 Neyshabour, Iran

Taught undergraduate and graduate courses in statistics and mathematics.

Lecturer

Semnan University

September 2018 – July 2019 Semnan, Iran

Delivered lectures in statistical methods and mathematical analysis.

Visiting Research Student

Carleton University

November 2016 – May 2017 Ottawa, Canada

Collaborative research in advanced statistical methodologies under international supervision.

Visiting Research Student

Brock University

May 2016 – October 2016 St. Catharines, Canada

Research collaboration focusing on penalized regression models and estimation techniques.

Lecturer

Shahrood University of Technology

September 2014 – July 2016 Shahrood, Iran

Taught various courses in statistics, probability theory, and mathematical modeling.

Lecturer

Islamic Azad University, Mashhad Branch

September 2011 – July 2013 Mashhad, Iran

Early career teaching position covering foundational mathematics and statistics courses.

Certificates

Continuous learning in data science and statistics

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Data Cleaning

Kaggle Jan 2023

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Applied Data Science Lab

World Quant University Jan 2023

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Data Analyst in R

DataQuest Jan 2022

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Predictive Modeling and Machine Learning in R

DataQuest Jan 2022

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Supervised Learning with scikit-learn

DataCamp Jan 2022

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Projects

Compare Penalized Estimation Methods

Interactive Shiny application for comparing different penalized estimation techniques in statistical modeling

CBPE: Correlation-Based Penalized Estimators

This R package provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method.

ntsDatasets: Neutrosophic Data Sets

This R package provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis.

PSinference: Inference for Released Plug-in Sampling Single Synthetic Dataset

An R package for statistical inference on synthetic datasets generated using plug-in sampling methods, providing robust analytical tools for researchers working with privacy-preserving synthetic data.

TestIndVars: Neutrosophic Distributions

This R package is statistical testing of independence between neutrosophic variables. It extends classical independence tests to handle uncertainty, indeterminacy, and imprecision inherent in neutrosophic data.

ImpShrinkage: Improved Shrinkage Estimations for Multiple Linear Regression

This R package provides a variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein estimations.

ntsDists: Neutrosophic Distributions

This R package computes the pdf, cdf, quantile function, and generates random numbers for neutrosophic distributions. This family of distributions has been developed by different authors in recent years.