Mohammad Javad Mahmoodabadi | AI Engineering | Best Paper Award

Posted on by Academic achievements

Assoc. Prof. Dr. Mohammad Javad Mahmoodabadi | AI Engineering | Best Paper Award

Assoc. Prof. Dr. Mohammad Javad Mahmoodabadi | AI Engineering – Associate Professor at Sirjan University of Technology, Iran

Dr. Mohammad Javad Mahmoodabadi is an accomplished academic and researcher, currently serving as an Associate Professor in the Department of Mechanical Engineering at Sirjan University of Technology, Iran. With an impressive track record in mechanical engineering and control theory, Dr. Mahmoodabadi has made significant contributions to the fields of optimization algorithms, machine learning, and mechanical design. He is highly regarded for his innovative approaches in robotics, control engineering, and computational methods. His research has been widely published and cited, establishing him as a leader in his area. Dr. Mahmoodabadi has also played an instrumental role in mentoring graduate students, guiding them through cutting-edge research in nonlinear systems and robotics.

Professional Profile

ORCID | Scopus

Education

Dr. Mahmoodabadi’s educational background reflects a solid foundation in mechanical engineering. He earned his Ph.D. in Mechanical Engineering from the University of Guilan, Iran, in 2012. His dissertation focused on the multi-objective optimization of linear and nonlinear controllers, combining powerful optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). During his Ph.D., Dr. Mahmoodabadi achieved excellent academic performance, earning a GPA of 18.80 out of 20 and a dissertation grade of 19 out of 20. Prior to this, he completed his Master’s degree in Mechanical Engineering at Shahid Bahonar University of Kerman, Iran, where his thesis dealt with elasto-static problems using meshless methods. His academic achievements have provided him with a deep understanding of both theoretical and applied mechanics, which have been pivotal in his research career.

Experience

Dr. Mahmoodabadi’s academic career spans over a decade, during which he has held several important positions. After earning his Ph.D., he served as an Assistant Professor at Sirjan University of Technology from 2012 to 2019, before advancing to the role of Associate Professor. Throughout his career, he has taught various undergraduate and graduate courses, including robotics, control of robots, linear control, fuzzy logic, and optimization. His extensive teaching experience in mechanical engineering and related disciplines has earned him recognition for his ability to convey complex concepts with clarity. In addition to his teaching roles, Dr. Mahmoodabadi has served as the head of the Department of Mechanical Engineering and the Graduate Student Office at his university. His leadership has contributed to the development of academic programs and research initiatives within the department.

Research Interests

Dr. Mahmoodabadi’s research interests are diverse, with a primary focus on control theory, machine learning, computational methods, and optimization algorithms. He has worked on various topics such as adaptive robust control, fuzzy logic systems, and multi-objective optimization in the context of nonlinear dynamic systems. His research also extends to robotics, where he has developed novel control strategies for autonomous systems. Additionally, Dr. Mahmoodabadi’s work on mechanical design and analysis of complex systems has led to innovative solutions in both theoretical and applied engineering. His approach integrates computational techniques with practical applications, particularly in optimization and control engineering.

Awards

Throughout his career, Dr. Mahmoodabadi has received numerous accolades for his contributions to research and teaching. His excellence in academic leadership and groundbreaking research has earned him recognition within his institution and the broader academic community. Notably, his work in the development of control algorithms and optimization methods has received significant attention from his peers, reflected in his high citation count and his role as a mentor to graduate students. Although Dr. Mahmoodabadi has not explicitly listed awards in the traditional sense, his impact on the academic and research community through his publications, patents, and leadership roles can be considered as a testament to his achievements.

Publications

M.J. Mahmoodabadi, N.R. Babak, Pareto optimum design of an adaptive robust backstepping controller for an unmanned aerial vehicle, Asian Journal of Control (2022). 📚
R. Abedzadeh Maafi, S. Etemadi Haghighi, M.J. Mahmoodabadi, A novel multi-objective optimization algorithm for Pareto design of a fuzzy full state feedback linearization controller applied on a ball and wheel system, Transactions of the Institute of Measurement and Control 44 (7) (2022), 1388–1409. 🛠
M.J. Mahmoodabadi, S. Hadipour Lakmesari, Optimal design of an adaptive robust controller using a multi-objective artificial bee colony algorithm for an inverted pendulum system, Transactions of the Canadian Society for Mechanical Engineering 46 (1) (2022), 89–102. 📈
S.H. Lakmesari, M.J. Mahmoodabadi, Adaptive sliding mode control of HIV-1 infection model, Informatics in Medicine Unlocked 25 (2021), 100703. 💡
M.J. Mahmoodabadi, Moving least squares approximation-based online control optimized by the team game algorithm for Duffing-Holmes chaotic problems, Cyber-Physical Systems 7 (2) (2021), 1-21. ⚙️
M.J. Mahmoodabadi, A.R. Nemati, A new optimum numerical method for analysis of nonlinear conductive heat transfer problems, Journal of the Brazilian Society of Mechanical Sciences and Engineering 43 (5) (2021), 1-8. 🔥
R. Abedzadeh Maafi, S. Etemadi Haghighi, M.J. Mahmoodabadi, Pareto optimal design of a fuzzy adaptive hierarchical sliding-mode controller for an XZ inverted pendulum system, IETE Journal of Research (2021). 🔄

Conclusion

Dr. Mohammad Javad Mahmoodabadi’s academic and research career exemplifies excellence in mechanical engineering and control systems. His innovative work in optimization algorithms, machine learning, and mechanical design has earned him recognition as a leader in his field. With a strong publication record and significant contributions to the academic community, he is a well-deserving candidate for the “Best Researcher Award.” His ability to blend theoretical advancements with practical applications, along with his mentorship of future researchers, positions him as a key figure in the development of engineering solutions for complex systems. Dr. Mahmoodabadi’s dedication to advancing knowledge, combined with his academic leadership and impactful research, makes him an outstanding nominee for this prestigious award.

Farzad Hosseinali | Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Dr. Farzad Hosseinali | Artificial Intelligence | Best Researcher Award

Doctorate at The George Washington University, United States

Farzad Hosseianli is a Professional Lecturer in Data Science at The George Washington University, specializing in machine learning. He has extensive experience as a Remote Teaching Assistant for online Data Science boot-camps and worked as a Freelance Data Scientist in the Bay Area, focusing on ML/DS projects. His research background includes roles as a Research Assistant at Texas A&M University and Texas Tech University, where he studied correlations in cotton fiber properties.

Author Profile

Google Scholar Profile

Education

Farzad pursued his educational journey with a B.Sc. in Textile Engineering and Fiber Science from Azad University, Shahr Rey Branch, Tehran, Iran, followed by an M.Sc. in Crop Science at Texas Tech University, Lubbock, USA, and a Ph.D. in Biological and Agricultural Engineering at Texas A&M University, College Station, USA. His academic path provided a comprehensive foundation in engineering, agricultural sciences, and data science, essential for his current roles in teaching and research.

Research Focus

Farzad’s research focuses on applying statistical analysis, regression techniques, and computer vision to study physical properties of cotton fibers, particularly in relation to friction characteristics among different varieties. This work aims to enhance understanding and optimize agricultural practices related to cotton cultivation and processing, crucial for improving fiber quality and production efficiency in the textile industry.

Professional Journey

Farzad has held various roles in academia and industry, including:

  • Professional Lecturer in Data Science: Teaching Machine Learning I at The George Washington University.
  • Remote Teaching Assistant: Assisting students in statistics, programming, and machine learning at 2U, an online Data Science boot-camp.
  • Freelance Data Scientist: Developing innovative solutions like Selective Backpropagation and participating in Kaggle competitions in the Bay Area, CA.
  • Research Assistant: Conducting research on cotton fiber properties at Texas A&M University and Texas Tech University, investigating correlations and physical characteristics.

Honors & Awards

Farzad has been recognized for his contributions to research with publications in journals that have impact factors ranging from 4.1 to 8.5. His publications highlight significant findings in cotton fiber friction and related properties, contributing to advancements in agricultural engineering and data science.

Publications Noted & Contributions

Farzad’s research contributions are evident in his publications, such as those in Expert Systems with Applications, Tribology International, and Fibers. These publications delve into the variability and characteristics of cotton fiber friction, providing valuable insights for enhancing fiber quality and performance across various applications.

Variability of fiber friction among cotton varieties: Influence of salient fiber physical metrics
Published in Tribology International in 2018, this paper explores how different physical metrics of cotton fibers influence their frictional properties across various varieties.

Microencapsulation of disperse dye particles with nano film coating through layer by layer technique
Co-authored with M Zandi, SA Hashemi, and P Aminayi, published in the Journal of Applied Polymer Science in 2011. This study investigates the encapsulation of disperse dye particles using nano film coating techniques.

Investigation on the tensile properties of individual cotton (Gossypium hirsutum L.) fibers
This research, conducted during Farzad’s time at Texas Tech University in 2012, focuses on studying the tensile strength properties of individual cotton fibers.

Multiscale Frictional Properties of Cotton Fibers: A Review
Published in Fibers in 2018, this review paper, co-authored with JA Thomasson, summarizes the multiscale frictional properties of cotton fibers, providing an overview of research in the field.

Probing of Nanoscale Friction and Mechanical Characteristics of Cotton Fiber’s Surface
Also published in Fibers in 2019 with JA Thomasson, this study probes the nanoscale friction and mechanical characteristics of cotton fiber surfaces, contributing insights into the material’s properties at a microscopic level.

Research Timeline

Farzad’s research journey spans from his undergraduate studies through to his doctoral studies and professional roles. His progression includes foundational research at Texas Tech University and Texas A&M University, focusing on cotton fiber properties and advanced analytical techniques. This timeline underscores his commitment to addressing complex challenges in agricultural engineering and data science, bridging academic research with practical applications in industry.