Dr. Inam Illahi | Software Engineering | Best Researcher Award

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Dr. Inam Illahi | Software Engineering | Best Researcher Award

Dr. Inam Illahi, Emerson University Mutlan, Pakistan

Inam Illahi is an accomplished Assistant Professor at Emerson University Multan, Pakistan. With a rich academic background and over a decade of teaching experience, he has made significant contributions to the field of computer science. Inam holds a PhD in Computer Science and Technology from the Beijing Institute of Technology, where he focused on Assistant Technologies for Crowdsourcing Software Development. His research encompasses machine learning, deep learning, and software development, yielding several publications in prestigious journals. In addition to his academic pursuits, Inam has worked in various educational institutions, enhancing the quality of education and fostering student engagement. His dedication to research and teaching reflects a passion for advancing knowledge and technology, making him a respected figure in his field. Inam’s commitment to improving educational practices and research outcomes highlights his role as a leader in academia.

Professioanl Profile

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Summary of Suitability for the Research for Best Researcher Award

Inam Illahi is a highly qualified candidate for the Research for Best Researcher Award, showcasing a solid academic and professional background in computer science, particularly in the field of software development and machine learning. His extensive teaching experience at various reputable universities, including his current role as an Assistant Professor at Emerson University Multan, highlights his commitment to academia and his ability to contribute significantly to the educational sector.

πŸŽ“  Education

Inam Illahi’s educational journey is marked by notable achievements and a commitment to excellence. He earned his PhD in Computer Science and Technology from the Beijing Institute of Technology, China, between 2016 and 2022. His research during this time focused on Assistant Technologies for Crowdsourcing Software Development, resulting in impactful publications. Prior to his PhD, Inam completed his Master’s in Software Engineering and Management from Chalmers University of Technology, Sweden, in 2010, where he gained insights into software development practices. He also holds a Master of Computer Science from the University of Sargodha, Pakistan, which he completed in 2007. His educational foundation is complemented by a Bachelor of Arts in Computer Science and Economics from the same institution. Inam’s diverse academic experiences, along with his international exposure in Sweden and Denmark, have equipped him with a global perspective and a strong skill set in technology and education.

πŸ’Ό   Experience 

Inam Illahi possesses extensive experience in academia, contributing to various educational institutions over the past decade. Since March 2024, he has been serving as an Assistant Professor at Emerson University Multan, where he is involved in teaching and research activities. Before that, he held a Tenure Track Assistant Professor position at the University of Education, Lahore, Multan Campus, from August 2023 to March 2024. His earlier roles include Assistant Professor at the Institute of Southern Punjab and Lecturer positions at National Textile University, Faisalabad, and Riphah International University. Inam has also served as an Academic Coordinator at COMSATS Institute of Technology, where he played a crucial role in teaching and administration. His experience as a Deputy Director at the Quality Enhancement Cell at The University of Faisalabad further underscores his leadership abilities. Inam’s diverse roles highlight his commitment to enhancing the educational landscape through effective teaching and administrative practices.

πŸ…  Awards and Honors 

Inam Illahi’s commitment to excellence in research and education has earned him several accolades throughout his career. Notably, his innovative work in crowdsourcing software development and machine learning has resulted in multiple publications in reputable journals, receiving recognition from his peers. His research on the β€œDr. Wheat” web-based expert system for diagnosing diseases in Pakistani wheat was presented at the International Conference of Information Security and Internet Engineering in London in 2008, showcasing his contributions to agricultural technology. In addition to research-related recognition, Inam has been actively involved in various academic committees and organizations, where his leadership skills have been acknowledged. His role as Deputy Director at The Quality Enhancement Cell highlighted his commitment to improving educational quality, further solidifying his reputation in academia. Inam’s dedication to research and education continues to inspire students and colleagues alike, contributing to his growing list of honors and achievements.

🌍  Research Focus

Inam Illahi’s research focuses primarily on the intersection of software development and artificial intelligence, with a particular emphasis on crowdsourcing and machine learning. His PhD thesis explored Assistant Technologies for Crowdsourcing Software Development, where he analyzed motivating and inhibiting factors for developers and success prediction in competitive crowdsourcing projects. His innovative contributions include the application of machine learning techniques for resolution prediction, enhancing the success rates of software development initiatives. Inam has published several influential papers in leading journals, examining various aspects of software project management and quality assurance. Notable works include studies on bug report prioritization using convolutional neural networks and severity prediction models. Through his research, Inam aims to bridge the gap between theory and practice in software development, providing valuable insights and tools for industry practitioners. His commitment to advancing knowledge in this rapidly evolving field makes him a key player in the research community.

πŸ“– Publications Top Notes

  • Title: Deep neural network-based severity prediction of bug reports
    Cited by: 94
  • Title: CNN-based automatic prioritization of bug reports
    Cited by: 85
  • Title: Dr. Wheat: a Web-based expert system for diagnosis of diseases and pests in Pakistani wheat
    Cited by: 79
  • Title: Serum tumor necrosis factor-alpha as a competent biomarker for evaluation of disease activity in early rheumatoid arthritis
    Cited by: 19
  • Title: An empirical study on competitive crowdsource software development: motivating and inhibiting factors
    Cited by: 13

Dr. Soheila Kookalani | Construction | Best Researcher Award

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Dr. Soheila Kookalani | Construction | Best Researcher Award

Dr. Soheila Kookalani, Cambridge University, United Kingdom

Soheila Kookalani is a distinguished Research Associate at the University of Cambridge, specializing in Civil and Structural Engineering. She has a profound expertise in steel reuse, the circular economy, life-cycle assessment, and the integration of digital twin technologies in construction. Her research focuses on leveraging artificial intelligence and machine learning to optimize structural designs, promoting sustainability in construction. Soheila holds a Ph.D. in Civil and Structural Engineering from Shanghai Jiao Tong University, where she pioneered research on GFRP elastic gridshells. With a commitment to environmental responsibility, she continuously explores innovative ways to integrate sustainable practices into building designs, aiming to revolutionize construction methodologies. She has numerous publications in leading journals, demonstrating her contribution to both academia and the industry. Her work emphasizes sustainable engineering practices that align with modern technological advancements, particularly in the realm of structural optimization and reuse strategies.

Professional Profile

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Summary of Suitability for the Research for Best Researcher Award

Soheila Kookalani’s innovative approach to structural engineering, coupled with her strong commitment to sustainability and integration of advanced technologies, positions her as a highly suitable candidate for the Research for Best Researcher Award. Her work has significant implications for the future of sustainable construction, making her a valuable asset to both academia and industry.

πŸŽ“ Education 

Soheila Kookalani pursued her academic journey with a solid foundation in architectural and civil engineering. She earned her Bachelor of Science in Architectural Engineering from Azad University in Iran, where her thesis explored hybrid architecture in cinematic arts. She then advanced her studies with a Master of Science in Civil and Structural Engineering from Hohai University, China, focusing on the seismic performance of hybrid structures in high-rise buildings. Her academic journey culminated with a Ph.D. from Shanghai Jiao Tong University, China, where she focused on the structural optimization of GFRP elastic gridshells using machine learning techniques. Her doctoral research contributed to advancing knowledge in structural design and building sustainability. Throughout her studies, Soheila has consistently integrated innovative technologies, such as artificial intelligence and machine learning, into her research, making her an authority in the field of structural design and optimization for sustainable construction.

πŸ’Ό Experience 

Soheila Kookalani has accumulated extensive experience in civil and structural engineering, with a primary focus on sustainable design practices. She currently serves as a Research Associate at the University of Cambridge, where she is engaged in a groundbreaking project on the reuse of structural steel in construction. Her experience spans across diverse areas, including the circular economy, life-cycle assessment, building information modeling (BIM), and digital twin technology. From 2018 to 2022, she conducted research on GFRP elastic gridshells as part of her Ph.D. at Shanghai Jiao Tong University. Prior to this, Soheila gained practical experience working on seismic performance evaluation of hybrid structures during her Master’s at Hohai University. Her expertise also extends to the application of artificial intelligence in optimizing structural designs, demonstrating her capacity to bridge the gap between theoretical research and practical application in sustainable construction.

πŸ…Awards and Honors 

Throughout her academic and professional career, Soheila Kookalani has been recognized for her outstanding contributions to civil and structural engineering. She has received several prestigious awards and honors, including recognition from leading engineering conferences and academic institutions. Her work on the integration of AI and machine learning in structural design has garnered international attention, earning her accolades for innovation in sustainable construction. Soheila has also been honored for her research on GFRP elastic gridshells, receiving commendations for excellence in structural optimization and sustainable design. Her role as a published author in top-tier engineering journals has further solidified her reputation as a leading researcher in the field. Additionally, Soheila’s contributions to the reuse of structural steel and her involvement in cutting-edge projects at the University of Cambridge have earned her numerous industry awards, highlighting her commitment to environmental responsibility and sustainable engineering practices.

🌍 Research Focus 

Soheila Kookalani’s research is centered on the intersection of civil engineering, sustainability, and advanced technology. Her primary focus is on steel reuse, promoting a circular economy in construction through the reuse of structural components. She is also deeply involved in life-cycle assessments, aiming to reduce the environmental impact of construction projects. Her research integrates building information modeling (BIM) and digital twin technology to enhance the design, monitoring, and optimization of construction projects. Soheila is particularly interested in applying artificial intelligence (AI) and machine learning to optimize structural designs, especially in the context of GFRP elastic gridshells. Her work on generative AI in structural engineering seeks to streamline design processes and improve sustainability. By combining these advanced technologies, her research contributes to developing more efficient, eco-friendly building practices that align with global sustainability goals.

πŸ“– Publication Top Notes

  • BIM-based augmented reality for facility maintenance management
    • Cited by: 24
  • Structural analysis of GFRP elastic gridshell structures by particle swarm optimization and least square support vector machine algorithms
    • Cited by: 18
  • Shape optimization of GFRP elastic gridshells by the weighted Lagrange Ξ΅-twin support vector machine and multi-objective particle swarm optimization algorithm considering …
    • Cited by: 14
  • An analytic approach to predict the shape and internal forces of barrel vault elastic gridshells during lifting construction
    • Cited by: 14
  • Effect of Fluid Viscous Damper parameters on the seismic performance
    • Cited by: 14