Farshid Hesami

Farshid Hesami
  • Master of Engineering
  • Lead Design Engineer at AIRIC - Automotive Industries Research & Innovation Center

Data Scientist specializing in ML & DL, seeking collaborators and new roles in innovative tech fields.

About

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Introduction
As Senior Automotive Engineer at AIRIC, I lead the technical direction of our engineering team specializing in body in white (BIW) and vehicle packaging. With over 24 years in the automotive industry, I am deeply involved in a range of projects from design to data science. To see more details of my computational projects, including code and collaboration on machine learning and deep learning projects, visit my GitHub page at : https://github.com/farshidhesami
Skills and Expertise
Lead Design Engineer | BIW structure | Machine Learning | Deep Learning | Computer Vision | NLP | Statistics | Tableau | PowerBI | Science engineer | Researcher| Mathematics Computational | Artificial Neural Networks | Reverse logistics|
Current institution
AIRIC - Automotive Industries Research & Innovation Center
  • Vehicle Engineering
Current position
  • Lead Design Engineer
Education
January 2021 - September 2023
Islamic Azad University Central Tehran Branch
Islamic Azad University Central Tehran Branch
Field of study
  • industrial engineering
January 2018 - September 2020
Shahid Rajaee Teacher Training University
Field of study
  • Manufacturing engineering
January 1997 - August 1999
Shahid Rajaee Teacher Training University
Field of study
  • Manufacturing and production

Publications

Publication (1)
Evaluating and selecting strategic alliance suppliers in reverse logistics using the ANP- TOPSIS hybrid method in a rough uncertainty environment
Thesis
Full-text available
  • Apr 2024
In recent years, the evolving domain of reverse logistics (RL) has garnered substantial attention for its role in facilitating companies to recognize potential resources while addressing the operational and strategic challenges associated with return processes. This research foregrounds the criticality of environmental sustainability and the optimi...
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Questions

Questions (2)
Is Rough Set Theory or Neutrosophic Logic better for machine Learning healthcare models handling discrete data and needing simplicity?
Question
  • Apr 2024
In the context of machine learning models for healthcare that predominantly handle discrete data and require high interpretability and simplicity, which approach offers more advantages:
Rough Set Theory or Neutrosophic Logic?
I invite experts to share their insights or experiences regarding the effectiveness, challenges, and suitability of these methodologies in managing uncertainties within health applications.
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How can the integration of Rough Set, Neutrosophic, and Fuzzy theories enhance decision-making processes in reverse logistics supplier selection?
Question
  • Apr 2024
In my thesis on reverse logistics, I explore the use of the ANP-TOPSIS hybrid method in handling uncertainty. I'm interested in hearing from researchers about the effectiveness of 'Rough Set,' 'Neutrosophic,' or 'Fuzzy' theories in addressing uncertainty within supplier selection processes. Any insights on these approaches would be greatly appreciated!
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