Mohammad Mirzehi

I am a highly motivated researcher with four years of experience in Mine Planning, Operations Research, Data Science, and Machine Learning. I possess a strong ability to think creatively and have a track record of producing impactful results. I am always eager to discover and grow, and I approach challenges with a positive attitude.

Living in : Iran

Gender : MaleRace : Middle Eastern

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Academic Profile

Posts

Contact Information

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live:.cid.48a2c10033a402e6Skype ID

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Educations

Mining EngineeringMaster'sTarbiat Modares University2021-09-15 Iran

Supervisor's name :Dr. Mojtaba Rezakhah

Title :

Short-term Planning in Open-Pit Mines Considering Machine Performance

Mining EngineeringBachelor'sAmirkabir University of Technology2018-09-11 Iran

Supervisor's name :Prof. Morteza Osanloo

Title :

Sustainable Development and Environmental assessment of Mining Activities in Sangan Iron Mine

Work Experiences

Research Project

at Dr. Ali Moradi Afrapoli,

I am currently working in this role

Research Assistance

at Department of Mining Engi

I am currently working in this role

Teaching Assistant

at Department of Mining Engi

I am currently working in this role

Research Development

at Vira Pasargad Company

Start : 31-May-2020

End : 30-Jul-2022

Journal Publications

Publication Title :

Incorporating grade uncertainty into open-pit long-term production planning using loss and profit functionsThird author

Journal :

International Journal of Mining and Geo-Engineering

Link :

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Publication Title :

A hybrid model for Backbreak prediction using XGBoost machine learning and metaheuristic algorithms in Chadormalu iron mineSecond author

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Publication Title :

Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP): A case study from an iron ore mineFirst author

Link :

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Publication Title :

Application of XGB-based metaheuristic techniques for prediction time-to-failure of mining machineryFirst author

Link :

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Publication Title :

A novel hybrid XGBoost methodology in predicting penetration rate of rotary based on rock-mass and material propertiesFirst author

Journal :

Arabian Journal for Science and Engineering

Link :

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Research Keywords

Machine Learning

operation research

Data Analysis

Sustainable Development

mine planning

environmental assessment

Awards

First Rank Student in UniversityMaster

Tuition-Waiver Bachelor Degree2014-08-31

Tuition-Waiver Master degree2018-09-01

References

Amin Mousavi

mousavi@modares.ac.ir

Mojtaba Rezakhah

rezakhah@modares.ac.ir

Ali Moradi Afrapoli

ali.moradi@gmn.ulaval.ca

Ahmadreza Sayadi

sayadi@modares.ac.ir