Mohamed Hesham Marei

Experience

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  University of Sheffield

  Apr 2013 - Jun 2014

  This role was concerned with discussing matters arising among students at the various academic levels, from first-year undergraduates to MSc students, from timetable conflicts to requesting extra resources to be made available for the students, and any feedback the students needed to relay to the department. My role as ACSE Society representative related to bringing society-related matters, including requesting additional funding for the society from the department. I successfully chaired and directed some of the meetings on this committee, and remained an active participant throughout the role. Show less The role entailed meeting regularly with the committee to contribute to matters pertaining to the degree programs at the department. Items such as feedback throughout the department, as well as certain departmental policy decisions and updates, were frequently discussed on this committee, where I participated and actively engaged with the students to get their insights and opinions regarding teaching matters. In addition, I personally recommended an action plan for late international student arrivals, having faced similar problems in my first year at university. Show less

  • ACSE Society Representative on Student Staff Committee

    Nov 2013 - Jun 2014

  • Member of ACSE Departmental Learning and Teaching Committee

    Nov 2013 - Jun 2014

  • Vice President at ACSE Society

    Apr 2013 - Jun 2014
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  The University of Sheffield

  Feb 2016 - Sept 2016

  Completed two internship projects at Sheffield Robotics (Summer 2015 and 2016)Project: Kinect enabled low-cost robotic arm tracker (Summer 2015)•Developed a C# WPF (Windows Presentation Foundation) app to leverage Kinect for Windows motion tracking capabilities for tracking human arm movement. The aim was to track arm motion and stream the calculated arm angle positions to an Arduino-controlled robot arm.Prototyped arm by selecting servo motor specs based on stringent kinematic requirements, in order to improve robot arm performance.•Conducted research into existing inverse kinematic algorithms to determine the most suitable method of integrating skeletal tracking with Arduino control.•Examined similar Kinect-based projects to learn the appropriate programming skills.Project: Modular robotic vision and teleoperation add-on (Summer 2016)• Developed robot add-on code in Linux and C++ utilising open-source libraries for video streaming • Developed C++ code for robot teleoperation Show less This role was carried out with the Department of Automatic Control and Systems Engineering at the University of Sheffield. In this role, I was responsible for creating recommendations to improve student satisfaction with the third-year group design project. I reported to the Director of Learning and Teaching for the department.For this role, independent research into student satisfaction was carried out. This included self-run workshops, one-on-one consultations, and research surveys to identify the key problems with the module, across three different academic years (2014, 2015 and 2016).Following this feedback assessment, I prepared a report to deliver recommendations to the department regarding the project, including peer marking and schedule of deliverables.The report contained a template to track student progress throughout the project and improve transparency for peer-marking. Show less

  • Student Researcher at department of Automatic Control and Systems Engineering

    Jun 2015 - Sept 2016

  • Student Engagement Project Coordinator (Part-Time)

    Feb 2016 - Jul 2016
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  Institute for Advanced Manufacturing and Engineering

  Oct 2017 - Jan 2022

  PhD Researcher

  *Passed viva with minor corrections*PhD project on real-time monitoring and optimization of manufacturing lines and predictive plans. The focus of the project is to develop an intelligent predictive maintenance solution for deployment in a manufacturing environment, using heterogeneous data as input for machine health and condition monitoring, and component Remaining Useful Life (RUL) prediction. •Developed Deep Learning driven method for CNC cutting tool wear prediction from visual inspection images, enabled by Convolutional Neural Network (CNN) transfer learning.•Developed Deep Learning driven method for CNC machine thermal error prediction based on long short-term memory. •Developed multimodal Deep Learning approach to combine CNC machining parameters with detected tool wear from visual inspection images to estimate the Remaining Useful Life (RUL) of CNC cutting tools.•Current project stage: viva success, corrections pending (October 2021). Show less

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  De Gruyter

  May 2018 - May 2019

  Language Editor, De Gruyter Open Computer Science

  Reviewing academic articles for publication in De Gruyer Open Computer Science, focusing on proofreading the documents for grammar, style, punctuation, and suggesting changes where appropriate to improve the communication of ideas.

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  CU Coventry

  Feb 2019 - Nov 2022

  University Tutor (Electromechanical Engineering)

  •Led teaching of second-year CAD engineering module on Smart Manufacturing Technology.•Led teaching of final-year engineering modules in robotics, embedded systems engineering, and systems operation and control.•Developed teaching material and skills-oriented summative assessments for all above models.•Supervised final year student projects in autonomous robotics, mechatronic mobility assistance devices, and machine learning for various applications.• Achieved 90% student satisfaction on all taught modules with cohorts of 5-70 students. Show less

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  WMG, University of Warwick

  Nov 2022 - now

  Project Engineer - Automation Systems

  Project: Airbus LANDOne (Automation of Maintenance Processes/Simplification of Aircraft Panels)Contributions: - Leading research and technical implementation through solution development and integration to deliver digitalised maintenance innovations using AI, with particular emphasis on automation goals of the work package and project figures of merit (FOM).- Overseeing full development lifecycle and agile project delivery: eliciting and documenting requirements and specifications; managing client data; leading technical solution physical prototyping and implementation; delegating software development and integration tasks in sprints; and supporting implementation and testing of assistive digitalised smart maintenance technology with stakeholders and domain experts.- Collecting, visualising, and annotating large computer vision datasets related to defect inspection and maintenance, training and evaluating deep learning models, and balancing data-efficient vision models with real-time detection performance.- Leading conceptualisation and technical implementation of computer vision solution for smart inspection enabled by state-of-the-art deep learning models, e.g. YOLOv8-11, Segment Anything, with data constraints.- Successfully developing, prototyping and operating bespoke hardware combining custom integrated electronics, mechanical components, and commercial off-the-shelf (COTS) products and developing software in C++, Python, and Swift (iOS).- Leading research publication and dissemination of the project deliverables.- Supporting development and delivery of teaching and training materials to disseminate the research findings and discipline topics in a format that will encourage successful implementation at undergraduate, apprenticeship, and postgraduate taught degree levels.- Successfully progressing the developed solution up to Technology Readiness Levels (TRLs) 1-3 towards target TRLs of 5-6 by project close and overseeing project delivery roadmap. Show less