Alia Al-Ghosoun | Engineering and Technology | Best Researcher Award

Dr Alia Al-Ghosoun | Engineering and Technology | Best Researcher Award

Assistant professor, Philadephia University, Jordan

Dr. Alia Radwan Al-Ghosoun is an Assistant Professor in the Mechatronics Engineering Department at Philadelphia University, Jordan. With a deep passion for advanced engineering research, she holds a DPhil in Engineering from Durham University, UK, where her work focused on shallow water dynamics and adaptive control methods for hydrodynamic systems. Dr. Al-Ghosoun’s research spans fluid mechanics, computational modeling, and the application of artificial intelligence in engineering problems. She has worked as a post-doctoral researcher at Durham University and has held multiple academic positions at the University of Jordan, where she contributed to the development of energy-efficient systems and intelligent control techniques. Dr. Al-Ghosoun’s commitment to advancing knowledge in hydrodynamics and environmental modeling has resulted in impactful publications and contributions to numerical simulation and uncertainty quantification. She is passionate about improving the practical application of engineering solutions for environmental challenges.

Profile

Scopus

Strengths for the Award

  1. Advanced Academic Background:
    • Dr. Al-Ghosoun holds a Doctor of Philosophy in Engineering from Durham University, UK, where her research focused on shallow water flow dynamics and adaptive control techniques to improve the accuracy of these systems. This is a highly specialized field with significant implications in environmental modeling, water systems, and engineering, marking her as an expert in computational engineering and fluid dynamics.
    • Her post-doctoral research at Durham University further solidifies her expertise, particularly in understanding and quantifying uncertainty in numerical modeling of hydrodynamics, which is crucial for predicting real-world environmental phenomena.
  2. Impactful and Diverse Research Contributions:
    • Dr. Al-Ghosoun has published several peer-reviewed papers in high-impact journals such as Environmental Modelling and Software, Communications in Computational Physics, and International Journal of Computational Methods. These works cover areas such as uncertainty quantification, morphodynamics, and numerical simulation of shallow water flows and hydrosediment processes.
    • Her conference papers and book chapters demonstrate a commitment to advancing computational methods in hydrodynamics and environmental modeling, particularly addressing the challenges of bed topography deformation, fluid-structure interactions, and stress analysis in hydro-sediment systems.
  3. Interdisciplinary Research:
    • Dr. Al-Ghosoun’s research stands at the intersection of mechatronics, engineering, and environmental sciences, with a focus on adaptive control techniques and artificial intelligence. This interdisciplinary approach is essential in addressing complex real-world problems related to fluid dynamics and energy systems.
    • The integration of AI techniques (such as genetic algorithms) in energy consumption optimization and shallow water flow models highlights her innovative approach to solving large-scale engineering problems.
  4. Global Collaboration and Recognition:
    • With international experience as a Post-Doctoral Researcher at Durham University and several collaborative research efforts with Jordanian and UK-based academic institutions, Dr. Al-Ghosoun has developed a robust international network. Her involvement in global research platforms, such as ResearchGate, attests to her active engagement in the academic community and dissemination of her work.
  5. Teaching and Mentoring Experience:
    • Dr. Al-Ghosoun has demonstrated a strong commitment to education as an Assistant Professor at Philadelphia University, where she contributes to the development of young engineers in Mechatronics Engineering. Her role as a Teaching Assistant and Research Assistant at various institutions indicates her foundational experience in nurturing future engineers and scientists.
  6. Recognition of Research Excellence:
    • Dr. Al-Ghosoun’s papers, particularly her works on uncertainty quantification and modeling techniques for shallow water systems, have gained traction in the academic community. For instance, her work published in Environmental Modelling and Software (2021) has already accumulated 10 citations, signaling its importance in the field.

Areas for Improvement

  1. Broader Citation Impact:
    • While Dr. Al-Ghosoun’s work is highly specialized and impactful, the citation counts for some of her research papers remain low (e.g., her paper on stress analysis has 0 citations). Increasing visibility in wider journals and collaborating with researchers in complementary fields could enhance the reach and impact of her publications.
  2. Increased Public Engagement:
    • Engaging in public outreach or community-based projects that demonstrate the application of her research (e.g., how adaptive control methods improve water management or energy efficiency in real-world scenarios) could enhance the broader social impact of her work.
  3. Further Collaborative Interdisciplinary Projects:
    • Although her work spans several fields, further involvement in cross-disciplinary projects—especially those integrating sustainable engineering and climate resilience—could increase the relevance of her research to pressing global challenges, like climate change adaptation and sustainable resource management.

Education

Dr. Alia Radwan Al-Ghosoun earned her Doctor of Philosophy (DPhil) in Engineering from Durham University, UK in January 2021. Her doctoral research focused on understanding the effects of bathymetric movement on shallow water flows and their interaction with the seabed, leading to the development of adaptive control methods for improved accuracy in hydrodynamic simulations. Prior to this, she completed a Post-Doctorate at Durham University in 2022, where she explored the application of uncertainty quantification in complex engineering models. Dr. Al-Ghosoun holds a Master’s Degree in Mechanical Engineering from the University of Jordan, where she developed AI-based predictive models for fuel consumption in Jordan and optimized energy efficiency through genetic algorithms. She also earned her Bachelor’s degree in Mechatronics Engineering from the University of Jordan. Dr. Al-Ghosoun’s academic background equips her with interdisciplinary expertise in engineering and environmental science.

Experience

Dr. Alia Radwan Al-Ghosoun is currently an Assistant Professor at Philadelphia University in the Mechatronics Engineering Department since October 2022, where she teaches and conducts research in engineering systems and adaptive control techniques. Prior to this, she was a Post-Doctoral Researcher at Durham University, UK (2021-2022), focusing on uncertainty quantification in shallow water systems. Dr. Al-Ghosoun completed her DPhil at Durham University (2016-2021), where her research involved modeling shallow water flows and the interaction of bed topography. She has also held roles as a Research Assistant at the University of Jordan’s Water, Energy, and Environment Center (2012-2016) and the King Abdullah Design and Development Bureau (KADDB) (2012). Earlier in her career, she worked as a Teaching Assistant in both Mechatronics and Mechanical Engineering departments at the University of Jordan. Dr. Al-Ghosoun’s interdisciplinary experience blends academia with applied engineering solutions.

Awards and Honors

Dr. Alia Radwan Al-Ghosoun has been recognized for her research excellence and commitment to advancing knowledge in hydrodynamics and adaptive control systems. Her academic achievements are highlighted by her work at Durham University, where she earned a prestigious Doctoral Fellowship for her research on shallow water dynamics and bed interaction. She has also received recognition for her post-doctoral research contributions in uncertainty quantification and numerical simulations. Dr. Al-Ghosoun’s work has been presented at major academic conferences, and she has contributed to a variety of high-impact journal publications. In addition to her research accomplishments, she has been awarded teaching grants to support her role as an educator at Philadelphia University, where she mentors the next generation of Mechatronics engineers. Her consistent efforts to bridge the gap between theoretical research and practical engineering applications have earned her widespread recognition within her academic and professional communities.

Research Focus

Dr. Alia Radwan Al-Ghosoun specializes in hydrodynamic modeling, shallow water flows, and the application of adaptive control systems to improve the accuracy of complex environmental simulations. Her research interests focus on uncertainty quantification and the development of computational models for the numerical simulation of fluid dynamics, particularly in the context of stochastic bed topography and morphodynamics. She has worked extensively on shallow water waves, bathymetric effects, and water-bed interaction. One of her core research goals is to enhance the predictive accuracy of models used for environmental management and engineering systems by incorporating artificial intelligence techniques, such as genetic algorithms and surrogate models. Dr. Al-Ghosoun is passionate about integrating AI-based solutions into environmental and energy systems to address challenges like resource optimization, pollution reduction, and sustainable energy. Her work in hydro-sediment-morphodynamics provides valuable insights into climate change adaptation and water resource management.

Publication Top Notes

  1. Uncertainty quantification for stochastic morphodynamics 🌊🧑‍🔬, AIP Conference Proceedings, 2024.
  2. A Novel Computational Approach for Wind-Driven Flows over Deformable Topography 💨🌍, Lecture Notes in Computer Science, 2024.
  3. A Nonintrusive Reduced-Order Model for Uncertainty Quantification in Numerical Solution of One-Dimensional Free-Surface Water Flows Over Stochastic Beds 📊💧, International Journal of Computational Methods, 2022.
  4. Efficient Computational Algorithm for Stress Analysis in Hydro-Sediment-Morphodynamic Models 💻⚙️, Lecture Notes in Computer Science, 2022.
  5. A surrogate model for efficient quantification of uncertainties in multilayer shallow water flows 🌊🔬, Environmental Modelling and Software, 2021.
  6. A computational model for simulation of shallow water waves by elastic deformations in the topography 🌊⚡, Communications in Computational Physics, 2021.
  7. Uncertainty Quantification of Bathymetric Effects in a Two-Layer Shallow Water Model: Case of the Gibraltar Strait 🏝️🌊, Springer Water, 2020.
  8. A hybrid finite volume/finite element method for shallow water waves by static deformation on seabeds 🌊🧮, Engineering Computations, 2020.
  9. A new numerical treatment of moving wet/dry fronts in dam-break flows 💧🚨, Journal of Applied Mathematics and Computing, 2019.

Conclusion

Dr. Alia Radwan Al-Ghosoun is an exceptional candidate for the Best Researcher Award. Her contributions to the fields of hydrodynamics, uncertainty quantification, and adaptive control systems are not only advancing the understanding of complex environmental processes but are also pioneering new computational techniques that can improve the accuracy and efficiency of engineering systems. Her ability to merge artificial intelligence with environmental modeling positions her as a leader in the field. Her ongoing efforts in teaching, mentoring, and global academic collaborations further highlight her potential to shape the future of engineering and environmental sciences. With a few strategic steps to broaden her citation impact and public visibility, Dr. Al-Ghosoun could solidify her place as a thought leader in her field.

Sanyogita Manu | Engineering and Technology | Best Researcher Award

Ms. Sanyogita Manu | Engineering and Technology | Best Researcher Award

PhD Candidate, The University of British Columbia, Canada

Publication Profile

Google scholar

Strengths for the Award

  1. Innovative Research Focus: Sanyogita’s work addresses a significant issue—indoor environmental quality during a time when many transitioned to remote work due to the pandemic. Her systematic study has the potential to inform guidelines and policies related to home office setups, highlighting its relevance in current public health discussions.
  2. Methodological Rigor: The research employs a robust methodology, utilizing continuous monitoring of various IEQ parameters alongside subjective assessments from participants. This comprehensive approach enhances the reliability of her findings.
  3. Professional Affiliations and Contributions: Sanyogita is actively engaged in professional organizations related to her field, serving on committees and reviewing journals. Her involvement in international conferences signifies her commitment to advancing research in IEQ and energy-efficient design.
  4. Publication Record: With multiple peer-reviewed publications and conference proceedings, Sanyogita demonstrates a solid track record in disseminating her research findings, contributing to the academic community’s understanding of indoor environments.
  5. Awards and Recognition: Her prior achievements and recognitions, including scholarships and awards, underscore her dedication and excellence in research.

Areas for Improvement

  1. Broader Impact Assessment: While her research is focused on WFH settings, there may be an opportunity to expand her study to include diverse populations and different geographical locations to enhance the generalizability of her findings.
  2. Interdisciplinary Collaboration: Collaborating with professionals from related fields such as psychology, sociology, or occupational health could enrich her research and offer a more holistic understanding of the WFH experience.
  3. Public Engagement: Engaging in public outreach or workshops to share her findings with broader audiences, including policymakers and the general public, could enhance the impact of her work and foster practical applications of her research.

Education

Sanyogita holds a Master’s degree in Interior Architecture and Design, specializing in Energy and Sustainability from CEPT University, India, where her dissertation focused on optimizing window performance in commercial buildings. She also earned her Bachelor’s degree in Interior Design from the same institution, with a dissertation exploring the thermal effects of furniture in interior environments. 🎓

Experience

With extensive experience in academia and research, Sanyogita has contributed to various projects assessing indoor environmental conditions and energy efficiency in buildings. She has served on several scientific committees and has been actively involved in peer review for reputable journals, reflecting her expertise in the field. 🏢

Research Focus

Her research primarily focuses on indoor environmental quality (IEQ) and its impact on occupant well-being and productivity, particularly in work-from-home settings. Sanyogita employs a systematic approach to evaluate both perceived and observed IEQ, utilizing a variety of environmental monitoring tools. 🔍

Awards and Honours

Sanyogita is a member of multiple prestigious organizations, including the International Society of Indoor Air Quality and Climate (ISIAQ) and the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). She has been recognized for her contributions to building performance simulation and energy conservation, reflecting her commitment to sustainable practices. 🏆

Publication Top Notes

Manu, S., & Rysanek, A. (under review). A novel dataset of indoor environmental conditions in work-from-home settings. Building and Environment.

Manu, S., & Rysanek, A. (2024). A Co-Location Study of 87 Low-Cost Environmental Monitors: Assessing Outliers, Variability, and Uncertainty. Buildings, 14(9), Article 9. Link

Manu, S., et al. (2024). A state-of-the-art, systematic review of indoor environmental quality studies in work-from-home settings. Building and Environment, 111652. Link

Doctor-Pingel, M., et al. (2019). A study of indoor thermal parameters for naturally ventilated occupied buildings in the warm-humid climate of southern India. Building and Environment, 151, 1-14. Link

Manu, S., et al. (2019). Performance evaluation of climate responsive buildings in India – Case studies from cooling dominated climate zones. Building and Environment, 148, 136-156. Link

Gupta, R., et al. (2019). Customized performance evaluation approach for Indian green buildings. Building Research & Information, 47(1), 56–74. Link

Conclusion

Sanyogita Manu’s research on indoor environmental quality in work-from-home settings is both timely and significant. Her methodological rigor, publication record, and active participation in professional communities demonstrate her dedication to advancing knowledge in her field. While there are areas for improvement, her strengths strongly position her as a worthy candidate for the Best Researcher Award. Her work has the potential to influence policy and improve well-being in residential work environments, making her contributions invaluable in today’s context.

Qi Liang | Pattern Recognition | Excellence in Research

Mr Qi Liang | Pattern Recognition | Excellence in Research

Master in Tongji University at China

Qi Liang is a dedicated researcher and master’s student at Tongji University, PR China, specializing in mechanical engineering. With a strong foundation in industrial engineering from Jiangsu University of Science and Technology, Qi has a keen interest in advancing technology through innovative research. Recognized for introducing self-supervised learning methods in semiconductor applications, Qi’s work aims to solve complex challenges in pattern recognition. Their publication in Engineering Applications of Artificial Intelligence reflects a commitment to high-impact research. With multiple ongoing projects and a focus on practical applications, Qi is paving the way for efficient solutions in the semiconductor industry.

Profile

Google Scholar

Strengths for the Award

  1. Innovative Research: Qi Liang has introduced a self-supervised learning method for few-shot learning in semiconductor applications, demonstrating originality and a significant contribution to the field.
  2. Publication Record: The recent publication in Engineering Applications of Artificial Intelligence showcases a commitment to high-quality research, adding to the credibility of the work.
  3. Diverse Research Interests: With a focus on computer vision, multi-modal learning, and fault diagnosis, Qi’s work spans multiple cutting-edge areas, which increases the potential impact of the research.
  4. Practical Applications: The research addresses real-world challenges in the semiconductor industry, offering low-cost, efficient methods that have immediate applicability.
  5. Academic Engagement: Qi’s active involvement in ongoing projects and industry collaborations indicates a robust engagement with both academic and practical aspects of research.

Areas for Improvement

  1. Broader Collaboration: Expanding collaborations with international researchers could enhance the research’s visibility and applicability on a global scale.
  2. Increased Publication Volume: While the current publication is commendable, a more extensive publication record could further establish Qi’s expertise and leadership in the field.
  3. Outreach and Communication: Engaging in more outreach activities, such as conferences and seminars, could help disseminate findings and foster connections within the research community.

Education 

Qi Liang graduated with a Bachelor’s degree in Industrial Engineering from Jiangsu University of Science and Technology, where foundational principles of engineering and technology were mastered. Currently, Qi is pursuing a Master’s degree in Mechanical Engineering at Tongji University, one of China’s prestigious institutions, now in their third year of the program. This advanced education has allowed Qi to engage deeply with cutting-edge topics, particularly in computer vision and machine learning. Through rigorous coursework and research, Qi has developed expertise in areas such as pattern recognition, self-supervised learning, and fault diagnosis, equipping them with the skills necessary to tackle complex engineering problems and contribute significantly to both academic and industrial advancements.

Experience

Qi Liang has gained substantial experience through multiple research projects, totaling five completed or ongoing initiatives that emphasize practical applications of machine learning in semiconductor manufacturing. In addition to academic research, Qi has participated in three consultancy and industry-sponsored projects, bridging the gap between theoretical knowledge and real-world applications. Their collaborative efforts in research have led to valuable partnerships and a broader understanding of the industry’s challenges and needs. As the first to implement self-supervised learning techniques in few-shot learning tasks related to wafer map pattern recognition, Qi has showcased exceptional innovation. This unique approach has opened new avenues for cost-effective and efficient solutions within the semiconductor sector, positioning Qi as an emerging leader in their field.

Research Focus 

Qi Liang’s research focuses on the intersection of computer vision and machine learning, with a strong emphasis on pattern recognition, keypoint detection, and image retrieval. Specializing in self-supervised and multi-modal learning, Qi aims to develop innovative methodologies that minimize the reliance on labeled data while maximizing efficiency and applicability in industrial contexts. Current research projects explore dynamic adaptation mechanisms for few-shot learning, specifically tailored for wafer map pattern recognition in the semiconductor industry. Qi is also interested in signal processing and fault diagnosis, seeking to improve reliability and performance in manufacturing processes. This research direction not only contributes to the academic community but also addresses pressing industry challenges, promoting advancements in automation and smart manufacturing.

Publication Top Notes

  • Masked Autoencoder with Dynamic Multi-Loss Adaptation Mechanism for Few Shot Wafer Map Pattern Recognition 📄

Conclusion

Qi Liang’s innovative contributions to the field of mechanical engineering and computer vision make a strong case for the Excellence in Research award. The unique approach to self-supervised learning in few-shot learning for wafer map pattern recognition signifies both a breakthrough in methodology and practical application in the semiconductor industry. With a few strategic improvements, Qi has the potential to further amplify the impact of their research and cement their status as a leading researcher in their field.

Azadeh Dogani | Technology | Best Researcher Award

Dr. Azadeh Dogani | Technology | Best Researcher Award

 

Dr Azadeh Dogani, Ferdowsi university of Mashhad, Iran

Azadeh Dogani is a passionate researcher specializing in Water Resources and Resilience 🌊. With a Ph.D. in Agricultural Economics from Ferdowsi University of Mashhad 🎓, she focuses on modeling systems dynamics and multi-objective programming. As Vice Chairman at Radzist Toos Waste Management Co. and Vice-president at Futuristic Modern City Technical and Engineering Consulting Company, she excels in marketing research and feasibility studies 📊. Azadeh’s research contributions, published in prestigious journals like Processes and Water Resources Management, demonstrate her expertise in systems modeling and optimization algorithms 📈. Skilled in Vensim, SPSS, GAMS, Stata, and R, she is committed to advancing sustainable practices in water management 💧.

Publication Profile

Orcid

Education

Azadeh Dogani holds a Ph.D. in Agricultural Economics 🌾 from Ferdowsi University of Mashhad, Iran, graduating in 2023. Her doctoral thesis focused on modeling the resilience of Mashhad Plain to the reduction of underground water resources using System Dynamic and Multi-Objective Programming 📊. She completed her M.Sc. in Agricultural Economics at Azad University of Marvdasht in 2011, investigating factors influencing investment in agriculture 🌱. Azadeh began her academic journey with a B.Sc. in Agricultural Economics from Azad University of Mashhad in 2008, laying the foundation for her expertise in agricultural economics and sustainability practices 🎓.

Experience

Since 2019, Azadeh Dogani has served as Vice Chairman of the Board at Radzist Toos Waste Management Co. 🌍 Her role involves conducting marketing research across diverse fields, preparing justification plans, and assessing feasibility for projects focused on used catalyst and carbon management. Concurrently, from 2021 to 2023, Azadeh led a research project at Khorasan Razavi Regional Water Company in Mashhad, Iran 🚰. She currently holds the position of Vice-president at Futuristic Modern City Technical and Engineering Consulting Company, beginning in 2022. Azadeh’s leadership spans environmental sustainability and technical consulting, emphasizing her commitment to innovative solutions in waste management and urban development 🏙️.

Research Focus

Azadeh Dogani’s research focuses on advancing fault warning systems in industrial settings 🛠️. Her work integrates cutting-edge algorithms like Coati Optimization and Bidirectional Long Short-Term Memory Networks (Bi-LSTM) to enhance predictive capabilities and mitigate operational risks. In collaboration with international researchers, she has contributed significantly to the development of innovative algorithms such as LightGBM for industrial fault detection, emphasizing efficiency and accuracy in complex systems 📈. Azadeh’s publications in esteemed journals underscore her expertise in optimizing algorithms for industrial applications, aiming to improve system reliability and performance amidst technological advancements and operational challenges 🌐.

Publication Top Notes