Dr. Mojtaba Ahmadieh khanesar | Metrology | Best Researcher Award

Dr. Mojtaba Ahmadieh khanesar | Metrology | Best Researcher Award 

Dr. Mojtaba Ahmadieh khanesar | Metrology | University of Nottingham | United Kingdom

Dr. Mojtaba Ahmadieh Khanesar is a distinguished research fellow in optical metrology and machine learning at the Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham. He holds a Ph.D. in Control Engineering from K. N. Toosi University of Technology and has extensive experience in metrology, robotics, control systems, artificial intelligence, and machine learning. Throughout his career, Dr. Khanesar has contributed to internationally recognized projects funded by EPSRC, including Robodome imaging for high-performance aerostructures, HARISOM for precise industrial robot manipulation, and Chattyfactories for next-generation industrial systems, demonstrating proficiency in experimental design, data acquisition, and real-time control using advanced robotics platforms such as UR5, Baxter, Sawyer, and laser tracking systems. He has also supervised Ph.D. and undergraduate students, providing mentorship in control, robotics, and machine learning projects, and delivered lectures on Bayesian learning and reinforcement learning at the University of Nottingham. Dr. Khanesar has held research and teaching positions across Denmark, Turkey, Iran, and the United Kingdom, reflecting his global research engagement and collaborative approach. His research has been widely published, with 112 documents, 2,377 citations, and an h-index of 25, including publications in high-impact journals such as IEEE Transactions, Robotics, Mechanism and Machine Theory, and Sensors. His professional affiliations include SMIEEE, MIET, and MASME, highlighting his recognized standing in international technical communities.

Professional Profile: ORCID | Scopus

Selected Publications 

  1. Ahmadieh Khanesar, M. (2025). Inkjet printing of ZIF-67 based-polymer composite membranes. Separation and Purification Technology. 0 citations.

  2. Ahmadieh Khanesar, M. (2025). Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches. Robotics. 0 citations.

  3. Ahmadieh Khanesar, M. (2025). Virtual Instrument for a Multi-illumination Dome System. Conference Paper. 0 citations.

  4. Ahmadieh Khanesar, M. (2023). Precision Denavit–Hartenberg Parameter Calibration for Industrial Robots Using a Laser Tracker System and Intelligent Optimization Approaches. Sensors, Basel, Switzerland. 25 citations.

  5. Ahmadieh Khanesar, M. (2023). A Neural Network Separation Approach for the Inclusion of Static Friction in Nonlinear Static Models of Industrial Robots. IEEE ASME Transactions on Mechatronics. 9 citations.

Mr. Luis Guillen | Precision Measurement Award | Young Scientist Award

Mr. Luis Guillen | Precision Measurement Award | Young Scientist Award 

Mr. Luis Guillen,  Tecnologico Nacional de Mexico, Campus Tuxtla Gutierrez, Mexico

Luis Enrique Guillen Ruiz is a 32-year-old Mexican national with a robust academic background in engineering and mathematics education. He holds a Master of Science in Mechatronic Engineering, which he earned with honors from the Tecnológico Nacional de México, Campus Tuxtla Gutiérrez, in March 2022. His specialization in optomechatronic systems reflects his dedication to the field. Prior to this, he completed a Master of Science with a specialization in Mathematics Education from the Universidad Autónoma de Chiapas in June 2018, also with honors. His academic journey began with a Bachelor’s Degree in Mechanical Engineering from the Tecnológico Nacional de México, where he graduated in June 2015. Luis is committed to advancing knowledge in his areas of expertise and is actively involved in research and educational initiatives.

Professional Profile:

SCOPUS

Summary of Suitability for the Young Scientist Award

Luis Enrique Guillen Ruiz, a 32-year-old researcher from Mexico, stands out as a strong candidate for the Young Scientist Award, particularly in the fields of Mechatronic Engineering and Optical Fiber Technology. His academic achievements and research contributions demonstrate a commitment to innovation and excellence in scientific inquiry.

Educational Background

  1. Master of Science in Mechatronic Engineering
    • Institution: Tecnológico Nacional de México, Campus Tuxtla Gutiérrez
    • Duration: January 2020 – March 2022
    • Specialization: Optomechatronic systems
    • Professional Examination Act Date: March 18, 2022 (with Honors)
    • Certificates:
      • Professional Certificate: 11440718
      • Professional Certificate: 13090741
  2. Master of Science with Specialization in Mathematics Education
    • Institution: Universidad Autónoma de Chiapas (UNACH)
    • Duration: January 2016 – December 2017
    • Professional Examination Act Date: June 15, 2018 (with Honors)
    • Certificate: Degree and Professional Certificate.
  3. Bachelor’s Degree in Mechanical Engineering
    • Institution: Tecnológico Nacional de México, Campus Tuxtla Gutiérrez
    • Duration: August 2010 – June 2015
    • Certificate: Degree and Professional Certificate.

Work Experience

Luis Enrique Guillen Ruiz has engaged in academic and practical applications of his studies through various projects and roles during and after his educational pursuits, though specific work experience details beyond his academic qualifications were not provided. Given his advanced degrees, he is likely involved in engineering, research, or educational roles related to mechatronics and mathematics education.

Publication top Notes:

An approach to enhance the sensitivity of a Mach–Zehnder displacement fiber sensor using the spectrum differential integration method and the filter in a double-pass configuration

Demonstration of Improving the Performance of a Fibre Optic Displacement Sensor Using the Optical Harmonic Vernier Effect by Cascading Tapered Optical Single-Mode Fibres

Experimental demonstration of optical Vernier effect by cascading tapered single-mode optical fibres

Towards an Approach for Filtration Efficiency Estimation of Consumer-Grade Face Masks Using Thermography