Shima Mahinnezhad

I am currently pursuing the Foundations of Site Reliability Engineering (SRE) training from mthree Academy, a comprehensive program designed to build expertise in ensuring the reliability, scalability, and efficiency of IT systems. This ongoing training is equipping me with both theoretical knowledge and practical skills to excel in modern technology environments.

Key Topics Covered So Far:

SRE Principles: Understanding core concepts of reliability engineering, monitoring, and incident response.
Linux Systems & Automation: Learning to manage Linux systems effectively and automate tasks to enhance operational efficiency.
DevOps Foundations: Developing skills in DevOps practices to foster collaboration between development and operations teams.
Database Management with MySQL: Gaining hands-on experience with data retrieval, manipulation, and management.
Python for System Monitoring: Building Python programming capabilities for automating processes and optimizing system performance.
This program also covers foundational topics such as computer architecture, IT service management, and financial concepts, providing a well-rounded understanding of technical and business aspects of SRE.

By the end of this training, I aim to leverage these skills to contribute to projects requiring robust system reliability and efficient operations.

During my time as a Research Assistant at École de Technologie Supérieure, I contributed to cutting-edge advancements in sensor technology and data-driven solutions.

Collaborative Research in Sensor Systems
I actively participated in a research lab focused on addressing complex challenges in the development of new sensor systems. Collaborating with three Ph.D. students, we explored innovative approaches to optimize sensor functionality and reliability using data-driven methodologies.

Dataset Development for Printed Sensors
I played a key role in collecting and curating datasets for newly developed printed sensors. This involved designing efficient data collection strategies and ensuring datasets were comprehensive and accurately represented sensor performance, laying the groundwork for further research and development.

Published Research on Data Augmentation
I authored and presented a paper titled "Data Augmentation and Class Imbalance Compensation Using CTGAN" at the IEEE Instrumentation and Measurement Conference (I2MTC). This research highlighted novel techniques for addressing class imbalance in datasets using generative adversarial networks (GANs), providing actionable solutions for improving data quality in machine learning applications.

• Tutored new international students at various competency levels to assist them with university registration and the process of settling into Montreal.
• Managed to assist four students with varying needs from different countries
• Used strong interpersonal communication skills to convey information to others.