Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Assoc. Prof. Dr. Mahmoud Bayat, Research Institute of Forests and Rangelands, Iran

Mahmoud Bayat is an Assistant Professor at the Research Institute of Forests and Rangelands, part of the Agricultural Research, Education, and Extension Organization (AREEO) in Tehran, Iran. He earned his B.A., M.Sc., and Ph.D. degrees from the University of Tehran, specializing in forestry science. Mahmoud has collaborated with renowned researchers, including Dr. Charles P.-A. Bourque, Dr. Pete Bettinger, Dr. Eric Zenner, Dr. Aaron Weiskittel, Dr. Harold Burkhart, and Dr. Timo Pukkala. His research focuses on forest modeling and inventory, with particular interest in applying artificial intelligence and machine learning techniques in forestry. Currently, he is working on projects related to growth and yield models for uneven-aged and mixed broadleaf forests using neural networks and the monitoring and mapping of tree species richness in northern Iran’s forests through symbolic regression and artificial neural networks. Mahmoud is proficient in statistical tools such as SPSS and MATLAB, and he is eager to share his expertise and discuss potential collaborations. For more information, his profiles can be found on ResearchGate, Google Scholar, and Scopus.

Professional Profile:

SCOPUS

Mahmoud Bayat’s Suitability for the Research for Best Researcher Award

Based on the provided details, Mahmoud Bayat demonstrates a strong candidacy for the Research for Best Researcher Award due to his extensive academic and professional contributions. Below is a summary supporting his suitability

Education 🎓

Ph.D. in Forestry Science
University of Tehran, Iran
M.Sc. in Forestry Science
University of Tehran, Iran
B.A. in Forestry Science
University of Tehran, Iran

Work Experience 🏢

Assistant Professor
Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO)
Tehran, Iran
Year: [Specify Year] – Present
Research Collaborator
Worked with:
- Dr. Charles P.-A. Bourque
- Dr. Pete Bettinger
- Dr. Eric Zenner
- Dr. Aaron Weiskittel
- Dr. Harold Burkhart
- Dr. Timo Pukkala

Research Interests 🔍

Forest modeling and inventory
Application of artificial intelligence and machine learning in forestry

Current Projects 📊

Growth and Yield Models for Uneven-Aged and Mixed Broadleaf Forest
- Method: Neural Network
Monitoring, Mapping, and Modeling Variation in Tree Species Richness
- Method: Symbolic Regression and Artificial Neural Networks
- Location: Northern Iran Forests

Publication Top Notes:

Comparison of Random Forest Models, Support Vector Machine and Multivariate Linear Regression for Biodiversity Assessment in the Hyrcanian Forests

Projected biodiversity in the Hyrcanian Mountain Forest of Iran: an investigation based on two climate scenarios

Recreation Potential Assessment at Tamarix Forest Reserves: A Method Based on Multicriteria Evaluation Approach and Landscape Metrics

Comparison between graph theory connectivity indices and landscape connectivity metrics for modeling river water quality in the southern Caspian sea basin

Development of multiclass alternating decision trees based models for landslide susceptibility mapping

Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests

Ms. Rachel Stephen Mollel, University of Strathclyde, United Kingdom

Rachel Stephen Mollel is a Ph.D. student in Electrical and Electronic Engineering at the University of Strathclyde, UK. Her research focuses on machine learning, explainable AI, energy demand-side management, smart metering, and non-intrusive load monitoring (NILM). She holds a Master of Engineering from Arkansas Tech University, USA, and a Bachelor’s degree in Telecommunication Engineering from Visvesvaraya Technological University, India. Rachel has contributed significantly to the energy sector, exploring the role of smart meters in reducing energy costs and enhancing communication between energy providers and consumers. Her recent work, which investigates the potential of NILM to reveal hidden demand flexibility in residential energy consumption, has been published in various peer-reviewed journals and conferences. Additionally, she is actively involved in improving the interpretability of NILM models to enhance algorithm performance. Her contributions have been recognized with a Commonwealth Scholarship in 2020.

Summary of Suitability for the Best Scholar Award:

Rachel Stephen Mollel is a highly suitable candidate for the Best Research Scholar Award based on her significant contributions to the fields of machine learning, explainable AI, and energy demand-side management. As a PhD student at the University of Strathclyde, her research aims to address critical energy issues through innovative approaches like Non-Intrusive Load Monitoring (NILM), which helps uncover hidden demand flexibility in residential energy consumption.

Education:

2021 – Present: PhD in Electrical and Electronic Engineering, University of Strathclyde, UK
2010 – 2012: Master of Engineering, Arkansas Tech University, USA (GPA: 3.75/4.0)
2006 – 2010: Bachelor’s degree in Telecommunication Engineering, Visvesvaraya Technological University, India (First Class)

Work Experience:

2011 – 2012: Graduate Assistant, Arkansas Tech University, USA
Assisted in the Digital Logic and Robotics Course & Lab; delivered tutorials, graded lab reports and exams, and supported the development of course materials under faculty supervision.
2014 – 2020: Assistant Lecturer, University of Dar es Salaam, Tanzania
Delivered lectures, prepared and graded exams in Control Systems Engineering and Fundamentals of Electrical Engineering. Supervised undergraduate student projects, practical training, and fieldwork. Managed various administrative duties, such as student registration and coordination of departmental examinations.