Welcome to my world
I am an industrial engineer focused on turning operational uncertainty into measurable decision confidence. My work blends reliability engineering, machine learning, and optimization so operations teams can move from reactive firefighting to evidence-backed planning.
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About
I specialize in converting high-variance operational environments into structured decision systems. Instead of treating data as a reporting artifact, I build analytical workflows that clarify root causes, quantify trade-offs, and support faster reliability actions at the point of decision.
My technical focus sits at the intersection of stochastic modeling, maintenance analytics, and optimization. I routinely map messy maintenance histories, sensor streams, and production context into models that remain interpretable for engineers, supervisors, and leadership.
Current priorities include predictive maintenance governance, robust KPI architecture, and optimization-backed planning methods for complex industrial systems where uptime, quality, and labor constraints must be balanced simultaneously.
Experience
2023 — Present
Oklahoma State University
Advancing research in reliability engineering, stochastic modeling, and data-driven decision systems for maintenance and operations planning.
2020 — 2023
Farandish Company (Fish Feed Production)
Supported maintenance analytics and fault-detection work using data mining, benchmarking, and reliability-focused diagnostics.
2019 — 2020
Karin Engineering Company (Crane Design & Manufacturing)
Led process and quality documentation efforts, including operation process charts and inspection form standardization for project delivery.
Education Quality
Oklahoma State University
Expected 2028
Reliability Engineering, Stochastic Modeling, Data-Driven Decision Making
Advisor: Dr. Akash Deep
K.N. Toosi University of Technology
2019 - 2022
Thesis: Predictive Maintenance of Advanced Industrial Machines Using AI Techniques
Advisor: Dr. Abdollah Aghaie
Islamic Azad University, Qazvin Branch
2015 - 2019
Industrial Systems & Operations
Professional Skills
Professional Certifications
Blog / Publications
A practical guide to using supervised, unsupervised, and reinforcement learning in reliability-centric operations without overpromising black-box AI outcomes.
Read Full PaperA practical, engineering-first explanation of integer programming formulations and classical solution approaches for planning, allocation, and scheduling under constraints.
Read Full PaperA structured playbook for building maintenance systems around service level, work-order quality, and CMMS-enabled execution.
Read Full PaperLooking for applied research collaboration in reliability engineering or operational analytics? Let's discuss your use case.
