Golnaz received her bachelor's degree in Computer Science at the University of Shahid Bahonar in 2005, and her master’s degree in Computer Science and Artificial intelligence at the University of Tabriz, Iran in 2014. She is currently a last year doctoral candidate and researcher at the Department of Computing, University of Turku, Finland. Her current research interests focus on solving high-dimensional multi-objective problems by applying meta-heuristic optimization methods, machine learning, deep learning, and parallel computing. She is the author of more than 15 peer-reviewed publications. In this period, she has been a teacher assistant for the “wearable computing” and “energy efficient embedded electronics” courses. She also supervised one master student on his thesis. She has also several years of experience working in the universities as a lecturer in computer science subjects (2007-2013). 

Nowadays,
due to ubiquitous health IoT and digital healthcare, enormous biomedical
datasets have been generated. In
the face of substantial digital information, an urgent challenge is how to
create good data for machine-learning algorithms. Biomedical datasets have
the characteristics of high-dimensionality, different sizes, data noises,
missing values, and imbalanced data. These complex raw data demote the
performance of the machine-learning algorithms in terms of reducing their
accuracy, causing overfitting, and taking more time to develop the model. One of the biggest challenges in
analyzing biomedical data for critical applications is the complexity of
designing an accurate and reliable decision-making algorithm, which avoids
overfitting in both small/medium-sized high-dimensional imbalanced
datasets. To tackle this challenge, we propose a generalized wrapper-based
feature and instance selection, called GeFICA, which is based on two meta-heuristic optimization approaches: a parallel new intelligent genetic algorithm (GA) and
a parallel imperialist competitive algorithm (ICA). Our proposed solution is
divided into two major categories: First, we present two efficient parallel
meta-heuristic optimization methods to solve both discrete and continuous complex optimization problems. Second, using our proposed optimization methods, we propose a feature
and instance selection algorithm to improve the high dimensionality and imbalance
problems in medical datasets using the proposed optimization methods. Our
proposed framework significantly improve the accuracy and efficiency of
high-dimensional imbalanced numeric datasets under different sizes with try to avoid of overfitting.

1. Meta-heuristic optimization solutions for solving complex problems

2. Feature selection in high-dimensional small/medium-sized datasets

3. Optimizing machine/deep learning algorithms by meta-heuristic approaches

4. Parallel Computing