mehri aghdamigargari has a diverse professional background spanning research, teaching, and academic leadership. From January 2020 to August 2023, she served as a Research Assistant at Nazarbayev University in Nur-Sultan, Kazakhstan. In this role, she spearheaded various optimization projects aimed at enhancing industrial efficiency and reducing waste. Her work involved detailed data analysis and root cause investigation, which she skillfully communicated through comprehensive reports, presentations, and scientific publications—contributing to innovation in industrial systems and process reliability.

Prior to her research role, mehri spent over a decade teaching mathematics and statistics at the high school level in Tabriz, Iran, from September 2006 to January 2020. Her dedication to education helped students build strong foundations in quantitative subjects, preparing them for advanced studies and careers in STEM fields.

Additionally, from September 2011 to September 2016, mehri held a position as a Master & Lecturer at Payame Noor University in Jolfa, Iran, where she taught courses in statistics, operations research, and computer programming within the industrial engineering department. Her role not only involved teaching but also mentoring students in applied engineering practices, further honing her ability to convey complex concepts effectively. Through these experiences, mehri has developed a versatile skill set that integrates academic research, teaching, and industry applications, making her a valuable contributor to both educational and professional fields.

mehri aghdamigargari’s research interests encompass a range of advanced analytical techniques and methodologies, including operations research, optimization, statistical and computational modeling, and data analysis with a focus on machine learning. Her work in operations research allows her to apply mathematical and analytical methods to make better decisions and solve complex problems in industries such as mining and manufacturing. Her expertise in optimization involves developing and implementing methods to improve efficiency and productivity, often balancing multiple objectives to maximize benefits while minimizing costs or environmental impacts.

In the field of statistical and computational modeling, mehri uses quantitative models to represent real-world processes, providing valuable insights into system behavior and enabling predictive analytics. Additionally, her skills in data analysis and machine learning allow her to process and analyze large datasets to uncover patterns and relationships, driving data-driven decision-making. Her combined expertise across these areas positions her at the forefront of research aimed at improving industrial systems through rigorous, data-informed approaches.