Zakia Sultana Munmun is an accomplished professional and researcher with a strong academic background in Electrical & Electronic Engineering and Information Systems, specializing in data analytics and project management. She possesses diverse technical skills, including proficiency in Python, R, SQL, Tableau, and process modeling, complemented by certifications in Lean Six Sigma, data analysis, and process mining. Her professional experience spans roles in supply chain data analysis, manufacturing quality, and telecommunications, where she has demonstrated expertise in data collection, analysis, and cross-functional collaboration. Zakia’s research contributions include published works on network management technologies and predictive healthcare using IoT and machine learning, reflecting her ability to bridge theoretical innovation with practical application. Active in student associations and professional groups, she embodies a commitment to continuous learning and collaboration. Her combined technical expertise, research accomplishments, and industry experience position her as a valuable contributor to advancements in data-driven solutions and emerging technologies.

Professional Profile 

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Education

Zakia Sultana Munmun holds a Master of Science in Information Systems with a specialization in Data Analytics and Project Management from Central Michigan University, where she gained expertise in data-driven decision-making, systems analysis, and project execution. She also earned a Bachelor of Science in Electrical & Electronic Engineering from the International University of Business, Agriculture & Technology in Bangladesh, building a solid technical foundation in engineering principles, system design, and problem-solving. Her education has been complemented by industry-recognized certifications, including Lean Six Sigma Greenbelt, Data Analysis with R, Tableau Desktop Qualified Certification, and Process Mining credentials from Celonis. This combination of formal education and specialized training has equipped her with the ability to integrate technical, analytical, and managerial skills in tackling complex challenges. Her academic journey reflects a balance between engineering fundamentals and advanced data analytics, preparing her for impactful contributions in both research and professional environments.

Experience

Zakia Sultana Munmun’s professional journey spans diverse roles across data analytics, supply chain management, telecommunications, and quality control. As a Supply Chain Data Analysis Associate at NYX, LLC, she has applied advanced analytical tools to optimize processes, forecast demand, and enhance decision-making. Her internship at Manufacturing Quality Resources Group involved analyzing large datasets, building complex models, and improving data accuracy through validation and cleaning. Prior to that, she served as a Senior Specialist at GPI Asia Tel, where she engaged in RF system integration, troubleshooting, and project management in the telecommunications sector. She also gained early career experience as a Networking & Database Implementation Intern at Robi Axiata Limited, contributing to network configuration, monitoring, and optimization. Across her roles, Zakia has demonstrated adaptability, technical proficiency, and collaboration, working effectively with cross-functional teams and delivering solutions that bridge operational needs with data-driven insights in both corporate and technical environments.

Research Focus

Zakia Sultana Munmun’s research focuses on leveraging emerging technologies to solve complex, real-world problems in network management and healthcare. Her work on “A Critical Review of Network Management Tools and Technologies in the Digital Age” explores the challenges of managing heterogeneous networks, identifying innovative solutions for performance optimization, anomaly detection, and traffic control. In the healthcare domain, her study “Predictive Healthcare: An IoT-Based ANFIS Framework for Diabetes Diagnosis” presents a machine learning-driven approach that integrates IoT data with adaptive neuro-fuzzy inference systems for accurate, real-time health predictions. This interdisciplinary research bridges data analytics, artificial intelligence, and domain-specific knowledge, aiming to enhance decision-making and operational efficiency. Her approach blends theoretical rigor with practical applicability, ensuring that her findings can be implemented in industry and public health systems. By focusing on data-driven innovation, she contributes to the advancement of intelligent systems and their role in improving performance, accuracy, and societal impact.

Award and Honor