Kiyanoosh Razzaghi | Heat Exchanger | Research Excellence Award

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Dr. Kiyanoosh Razzaghi | Heat Exchanger | Research Excellence Award

👤 Dr. Kiyanoosh Razzaghi, University of Sistan and Baluchestan, Iran

Dr. Kiyanoosh Razzaghi is an esteemed academic and researcher in chemical engineering, serving as the Head of the Department of Chemical Engineering at the University of Sistan and Baluchestan, Iran. Born on July 4, 1979, in Bandar Anzali, Iran, he is a dedicated scholar with a profound interest in optimizing chemical processes, particularly in distillation, energy efficiency, and reactor systems. With over two decades of professional experience, Dr. Razzaghi’s career spans roles in academia and the petrochemical industry. His research contributions have been widely recognized at prestigious conferences and through impactful publications. Outside of work, he is a committed family man, married with two children, balancing his professional excellence with personal responsibilities.

Professional Profile

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🌟 Summary of Suitability for the Award

Dr. Kiyanoosh Razzaghi, Assistant Professor and Head of the Department of Chemical Engineering at the University of Sistan and Baluchestan, demonstrates exceptional qualifications for the Research for Research Excellence Award. With a robust academic foundation, including a Ph.D. in Chemical Engineering, Dr. Razzaghi has accumulated extensive professional experience in both academia and industry. His tenure as Head of the Department underscores his leadership capabilities and contributions to advancing chemical engineering education and research.

Dr. Razzaghi’s prolific research output includes numerous publications and conference presentations spanning diverse topics, such as process optimization, heat transfer, distillation column control, and environmental engineering. These works reflect his innovative approaches to addressing pressing challenges in chemical engineering and his commitment to advancing sustainable solutions.

🎓 Education 

Dr. Kiyanoosh Razzaghi’s academic journey is marked by excellence and dedication to chemical engineering. He earned his Ph.D. (2006–2011) and M.S. (2003–2006) degrees from the University of Sistan and Baluchestan, where he delved into advanced chemical engineering concepts, laying the groundwork for his future innovations. His B.S. degree (1997–2002) from the Iran University of Science and Technology provided a robust foundation in chemical processes and industrial applications. Throughout his studies, he displayed exceptional analytical and problem-solving skills, with his research often focusing on process optimization, energy conservation, and innovative reactor designs. His academic achievements have been complemented by active involvement in scholarly conferences and collaborations, positioning him as a thought leader in his field.

💼  Professional Experience

Dr. Kiyanoosh Razzaghi’s professional journey is a testament to his commitment to advancing chemical engineering. Since 2018, he has served as an Assistant Professor at the University of Sistan and Baluchestan, mentoring aspiring engineers while spearheading impactful research. Previously, he held similar academic roles, including Lecturer and Assistant Professor (2011–2018). Before transitioning into academia, Dr. Razzaghi gained valuable industry experience as a Process Engineer at Kharg Petrochemical Complex (2002–2003), specializing in methanol production. Additionally, he worked in Sadi Tile Manufacturing Co., applying chemical engineering principles to industrial processes. His diverse career highlights his ability to bridge theory with practice, bringing real-world insights to the classroom and research lab.

🏅 Awards and Recognition

Dr. Kiyanoosh Razzaghi’s career is decorated with numerous accolades for his contributions to chemical engineering. He has been recognized at national and international conferences, such as the World Congress of Chemical Engineering and the Canadian Chemical Engineering Conference, for his groundbreaking research in distillation column control, heat transfer, and process optimization. His work on energy efficiency in catalytic conversion units and innovative mixer designs has earned him widespread acclaim. Additionally, Dr. Razzaghi’s dedication to teaching and mentoring has been acknowledged by his institution, reflecting his impact on both students and colleagues. These achievements underline his commitment to driving innovation and excellence in chemical engineering.

🌍 Research Skills On Heat Exchanger 

Dr. Kiyanoosh Razzaghi possesses a robust skill set in chemical engineering research, specializing in process optimization, control systems, and energy efficiency. He is adept at utilizing computational fluid dynamics, μ-synthesis techniques, and exergy analysis to solve complex engineering challenges. His expertise extends to designing innovative reactor systems, static mixers, and heat exchangers. Dr. Razzaghi is skilled in applying theoretical concepts to practical problems, with a strong focus on sustainable and energy-efficient solutions. His collaborative approach has facilitated interdisciplinary research, resulting in impactful findings presented at renowned conferences and published in esteemed journals.

📖 Publication Top Notes

  • Effect of water flow rate on internal heat and mass transfer and daily productivity of a weir-type cascade solar still
    • Authors: FF Tabrizi, M Dashtban, H Moghaddam, K Razzaghi
    • Citations: 159
    • Year: 2010
  • On the effect of phase fraction on drop size distribution of liquid–liquid dispersions in agitated vessels
    • Authors: K Razzaghi, F Shahraki
    • Citations: 32
    • Year: 2010
  • Design and characterization of a Low‐pressure‐drop static mixer
    • Authors: SM Hosseini, K Razzaghi, F Shahraki
    • Citations: 30
    • Year: 2019
  • Nonsquare multivariable non-minimal state space-proportional integral plus (NMSS-PIP) control for atmospheric crude oil distillation column
    • Authors: MM Khalilipour, J Sadeghi, F Shahraki, K Razzaghi
    • Citations: 27
    • Year: 2016
  • Theoretical model for multiple breakup of fluid particles in turbulent flow field
    • Authors: K Razzaghi, F Shahraki
    • Citations: 26
    • Year: 2016
  • Improving mixing performance by curved‐blade static mixer
    • Authors: H Mahmoodi, K Razzaghi, F Shahraki
    • Citations: 22
    • Year: 2020
  • Hydrogen network retrofit via flexibility analysis: The steady-state flexibility index
    • Authors: MRS Birjandi, F Shahraki, K Razzaghi
    • Citations: 20
    • Year: 2017
  • Enhancement of gasoline selectivity in combined reactor system consisting of steam reforming of methane and Fischer-Tropsch synthesis
    • Authors: A Ghareghashi, F Shahraki, K Razzaghi, S Ghader, MA Torangi
    • Citations: 16
    • Year: 2017
  • Robust control of an ill-conditioned plant using μ-synthesis: A case study for high-purity distillation
    • Authors: K Razzaghi, F Shahraki
    • Citations: 14
    • Year: 2007
  • Experimental characterization of heat transfer enhancement in a circular tube fitted with Koflo Blade™ inline mixer
    • Authors: R Zarei, K Razzaghi, F Shahraki
    • Citations: 12
    • Year: 2021

Prof. Dr. Haiyan Wang | Statistics | Best Researcher Award

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Prof. Dr. Haiyan Wang | Statistics | Best Researcher Award

Prof. Dr. Haiyan Wang, Kansas State University, United States

Professor Haiyan Wang, based at Kansas State University, is an esteemed academic in the Department of Statistics. With a Ph.D. from The Pennsylvania State University (2004), she has developed a robust research profile in statistical methods, specializing in nonparametric techniques for big data, image analysis, and high-dimensional data studies. Her scholarly journey encompasses profound contributions to advancing rank tests, functional data analytics, and data mining. At Kansas State University, Professor Wang has risen through the ranks from Assistant Professor to a full Professorship, illustrating her steadfast commitment to academic excellence and statistical innovation. Her research impacts not only theoretical statistics but also practical applications in data science, fostering collaboration across various disciplines. With a passion for teaching and mentoring, she continues to shape future statisticians while driving forward significant breakthroughs in her field.

Professional Profile

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Summary of Suitability for the Research for Best Researcher Award

Professor Haiyan Wang, a distinguished researcher in the Department of Statistics at Kansas State University, brings a wealth of expertise in statistical methods that are highly relevant in the current era of big data. Her research contributions span several critical areas, including nonparametric methods for big data, high-dimensional data analysis, image analysis, and computational statistics. These research domains are crucial in addressing the challenges posed by the explosion of data in science and industry, making her work both timely and impactful.

🎓   Education

Haiyan Wang earned her Ph.D. in Statistics from The Pennsylvania State University in 2004, where she focused on testing in multifactor heteroscedastic ANOVA and repeated measures designs with a large number of levels. Under the guidance of Professor Michael G. Akritas, she honed her expertise in statistical modeling and data analysis. Prior to her doctoral studies, she completed both her Master of Science (1999) and Bachelor of Science (1996) degrees at Beijing University, China. Her education laid a strong foundation for her analytical acumen, blending rigorous theoretical knowledge with applied methodologies. Through her studies, Professor Wang gained proficiency in complex statistical approaches, equipping her to tackle challenges in high-dimensional data, computational statistics, and advanced nonparametric techniques, which have become central to her research and academic endeavors.

💼  Professional Experience

Haiyan Wang began her academic career as a Graduate Teaching Assistant at The Pennsylvania State University from 1999 to 2003, transitioning to a Graduate Research Assistant role until 2004. She joined Kansas State University as an Assistant Professor in 2004, where she demonstrated exceptional research and teaching capabilities. Her commitment earned her tenure as an Associate Professor in 2010. In 2016, she was promoted to Professor, a testament to her continued excellence in statistical research and academic service. Throughout her career, she has actively engaged in teaching, curriculum development, and mentorship, shaping the future of statistics students. Her professional journey is marked by significant scholarly output, leadership roles in research collaborations, and active involvement in academic and research committees, further enhancing the reputation of Kansas State University in the field of statistics.

🏅 Awards and Recognition

Professor Haiyan Wang has received several accolades acknowledging her contributions to statistics and academia. Her research, often groundbreaking in nonparametric and high-dimensional data analysis, has been recognized through grants and publication awards. Her ability to innovate in computational and data-driven statistical methods has drawn acclaim from academic peers and professional organizations. In addition to research honors, she has been celebrated for her excellence in teaching and mentorship, fostering a new generation of statisticians and researchers. As a leading scholar at Kansas State University, she has also been acknowledged for her contributions to cross-disciplinary projects that leverage statistical analysis for broader scientific discoveries, securing her place as a respected voice in the global statistical community.

🌍   Research Skills

Professor Wang’s research skills are diverse and comprehensive. She specializes in nonparametric methods for big data analysis, including techniques for high-dimensional datasets, image analysis, and the development of rank tests. Her expertise extends to longitudinal and functional data analysis, crucial for understanding time-dependent and complex structured data. She is also adept in computational methods, utilizing advanced algorithms to facilitate large-scale data mining. Her skill set includes analyzing clustered and structured data, essential for applications in bioinformatics and social sciences. A visionary researcher, she combines rigorous theoretical development with practical applications, pushing the boundaries of what is possible in statistical analysis, machine learning, and big data interpretation.

📖 Publications Top Notes

  • Title: New two-sample tests for skewed populations and their connection to theoretical power of Bootstrap-t test

 

Dr. Mlungisi Duma | Artificial Intelligence | Best Researcher Award

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Dr. Mlungisi Duma | Artificial Intelligence | Best Researcher Award

Dr. Mlungisi Duma, University of Johannesburg, South Africa

Dr. Mlungisi Duma is a Senior Researcher and Development Manager at the University of Johannesburg. With over 19 years of experience in the IT profession, he has a rich background in software development and management. Dr. Duma holds a PhD in Electronic and Electrical Engineering, specializing in Artificial Intelligence, from the University of Johannesburg. His research focuses on machine learning, evolutionary computation, and optimization algorithms. He has successfully led numerous projects, published in top-tier journals, and is an active reviewer for multiple prestigious journals. His expertise extends to consultancy, where he manages application development for First National Bank, overseeing code maintenance for ATM systems. Dr. Duma’s contributions to academia and industry showcase his dedication to innovation and leadership in AI research.

Professional Profile

Scopus

Summary of Suitability for the Research for Best Researcher Award

Dr. Mlungisi Duma’s diverse contributions to the fields of artificial intelligence and evolutionary computation, along with his leadership in both academia and industry, make him highly suitable for the ‘Research for Best Researcher Award’. His ability to bridge the gap between theoretical research and real-world applications, coupled with his strong publication record and professional recognition, demonstrate his excellence as a researcher. He is well-positioned to receive this award, as his work contributes significantly to advancing the field of artificial intelligence.

🎓  Education 

Dr. Mlungisi Duma holds a Master’s degree in Computer Science and a PhD in Electronic and Electrical Engineering, both from the University of Johannesburg. His PhD specialization in Artificial Intelligence equipped him with expertise in machine learning, evolutionary computation, and optimization algorithms. Throughout his academic journey, Dr. Duma has actively engaged in cutting-edge research projects, collaborating with renowned academics and professionals. His commitment to education has been reflected in his role as a judge and reviewer at various academic institutions and scientific journals. As a Senior Member of IEEE and ACM, he continues to contribute to the advancement of AI technologies. Dr. Duma’s academic background is a testament to his passion for innovation, making significant contributions to the field of Artificial Intelligence.

 💼  Experience

With 19 years of experience in the IT industry, Dr. Mlungisi Duma has held various technical and leadership roles. For nine years, he worked as a software developer, honing his skills in coding, system architecture, and application design. Following this, he spent a decade as a Development Manager and Software Architect at First National Bank, where he led teams responsible for developing and maintaining ATM systems. Currently, he is a Senior Researcher and Development Manager at the University of Johannesburg, where he leads AI research initiatives. His extensive experience spans both academic and industry settings, bridging the gap between theoretical research and practical applications. Dr. Duma’s ability to manage multidisciplinary teams and deliver innovative solutions reflects his leadership and technical acumen.

🏅Awards and Honors 

Dr. Mlungisi Duma has received several prestigious awards and honors for his contributions to Artificial Intelligence and software development. He is a Senior Member of IEEE and ACM, recognizing his influence in the academic and professional communities. Dr. Duma’s work has also been recognized through his membership in the Golden Key International Honour Society, a distinction for top-performing academics globally. He has served as a judge at the University of Johannesburg’s Academy of Computer Science project day and has been a frequent reviewer for renowned journals, including IEEE Access, Applied Soft Computing, and the Journal of Mathematical Problems in Engineering. These honors highlight Dr. Duma’s commitment to academic excellence and his significant contributions to advancing AI technologies.

 🌍 Research Focus

Dr. Mlungisi Duma’s research focuses on Artificial Intelligence, particularly in the fields of machine learning, evolutionary computation, and optimization algorithms. He has contributed extensively to developing novel AI models for predictive modeling, control parameter optimization, and feature selection. His recent work includes optimizing ant colony algorithms and using artificial immune systems for collaborative filtering in recommender systems. Dr. Duma’s research is not only theoretical but also has practical applications, as demonstrated by his consultancy projects for First National Bank, where AI-driven solutions are implemented in ATM systems. He has published widely in top-tier journals, reflecting his thought leadership in AI and its applications in diverse sectors. His current projects aim to enhance AI’s role in automation and decision-making across industries.

 📖 Publication Top Notes

  • Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms
    • Citations: 22
  • Optimising latent features using artificial immune system in collaborative filtering for recommender systems
    • Citations: 20
  • Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm
    • Citations: 30
  • Classification with missing data using multi-layered artificial immune systems
    • Citations: 4
  • Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection
    • Citations: 2