Sonia Akram | Applied Mathematics | Best Researcher Award

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Ms. Sonia Akram | Applied Mathematics | Best Researcher Award

Researcher at University of Gujrat, Pakistan

Sonia Akram, born on February 4, 1998, in Gujrat, Pakistan, is a dedicated researcher in the field of Applied Mathematics, specializing in Soliton Theory. She completed her M.Phil from the University of Gujrat in August 2023, achieving a perfect CGPA of 4.00. With a remarkable portfolio of 20 published research articles, Sonia’s work is recognized in reputable peer-reviewed journals. Passionate about advancing mathematical physics, she aims to incorporate artificial intelligence into her future research. Sonia actively participates in conferences to share her insights and collaborate with fellow researchers. Her academic journey reflects her commitment to contributing to the advancement of knowledge in her field.

Profile:

Scopus Profile

Strengths for the Award:

  1. Strong Academic Background:
    • Sonia holds an M.Phil in Applied Mathematics with a perfect CGPA, showcasing her academic excellence.
    • Her thesis focuses on a relevant and advanced topic in mathematical physics, indicating a deep understanding of her field.
  2. Significant Research Output:
    • She has published 20 research articles in reputable peer-reviewed journals, highlighting her productivity and commitment to research.
    • Her recent work involves complex analyses such as Lie Symmetry, Bifurcation, Chaos, and Sensitivity Analysis, demonstrating her versatility and depth of knowledge.
  3. Innovative Research Interests:
    • Sonia’s plan to integrate Neural Network modeling into her research indicates a forward-thinking approach, bridging traditional mathematics with modern artificial intelligence.
  4. Recognition and Awards:
    • She has received a gold medal for her performance in her Master’s program and has been recognized with a merit-based laptop from the Prime Minister of Pakistan, showcasing her achievements.
  5. International Engagement:
    • Participation in international conferences indicates her willingness to engage with the global research community and stay updated with current trends.

Areas for Improvement:

  1. Networking and Collaboration:
    • While she has published multiple articles, fostering collaborations with researchers from different fields could further enhance her research profile and introduce her to new methodologies.
  2. Broader Impact:
    • Emphasizing the practical applications of her research in real-world scenarios could strengthen her research narrative and appeal to a wider audience.
  3. Public Outreach:
    • Engaging in community outreach or educational initiatives to promote mathematics and its applications could enhance her visibility and demonstrate her commitment to the field beyond academia.
  4. Diversity of Research Topics:
    • While her current focus is impressive, diversifying her research topics could open new avenues and strengthen her overall impact in the field.

Education:

Sonia Akram holds an M.Phil in Applied Mathematics from the University of Gujrat, where she graduated with a perfect CGPA of 4.00 in August 2023. Her thesis focused on the “Wave Structure of Some Nonlinear Dynamical Models Arise in Mathematical Physics,” demonstrating her expertise in advanced mathematical concepts. Prior to her M.Phil, she earned a Master’s degree in Mathematics from the same institution (2018-2021) and a Bachelor’s degree in Science (2016-2018), laying a solid foundation in mathematics and statistics. Sonia’s academic training encompasses various areas of natural sciences, enhancing her analytical and problem-solving skills. Her educational achievements underscore her commitment to excellence and her potential to contribute significantly to the field of applied mathematics.

Experience:

Sonia Akram has gained valuable experience in research and academia through her extensive work in Applied Mathematics. During her M.Phil studies, she focused on soliton and lump solutions of nonlinear dynamical models, contributing to her field with 20 research publications. Her collaboration with prominent researchers has enabled her to engage in significant studies, including modulation instability analysis and bifurcation analysis. In addition to her research, Sonia has attended several academic conferences, such as the “1st International Alumni’s Mathematics UET Conference” in February 2022, where she presented her findings and networked with fellow researchers. This experience has allowed her to refine her communication skills and enhance her professional network. Sonia’s commitment to advancing mathematical research positions her as an emerging expert in her field.

Awards and Honors:

Sonia Akram has received several prestigious awards recognizing her academic excellence and research contributions. She was honored as a gold medalist during her Master’s program in Mathematics at the University of Gujrat, highlighting her exceptional academic performance. Additionally, she was awarded a merit-based laptop from the Prime Minister of Pakistan, which underscores her dedication and hard work in the field of mathematics. These accolades not only reflect Sonia’s academic achievements but also serve as motivation for her continued pursuit of knowledge and research excellence. Her accomplishments position her as a strong candidate for further awards and recognition in her academic and research endeavors, inspiring future generations of mathematicians.

Research Focus:

Sonia Akram’s research focuses on soliton theory and nonlinear dynamical models in both classical and fractional forms. She employs linear stability theory to analyze modulation instability and has published 20 articles in reputable peer-reviewed journals. Her work explores advanced topics such as Lie Symmetry Analysis, Bifurcation Analysis, and Chaos Theory, showcasing her versatility in applied mathematics. Recently, she has extended her research to include sensitivity analysis of various nonlinear models, further enhancing the depth of her studies. Sonia is also planning to integrate neural network modeling in artificial intelligence into her future research, bridging the gap between traditional mathematics and modern computational methods. Her commitment to advancing knowledge in applied mathematics reflects her passion for solving complex problems and contributing to the scientific community.

Publications Top Notes:

  1. Soliton solutions for some higher order nonlinear problems of mathematical engineering, Nonlinear Engineering. Modeling and Application (2023).
  2. Soliton solutions and sensitive analysis to nonlinear wave model arising in optics, Physica Scripta, 2024.
  3. Propagation of solitary wave solutions to (4+1)-dimensional Davey–Stewartson–Kadomtsev–Petviashvili equation arise in mathematical physics and stability analysis, 2024.
  4. Stability analysis and solitonic behaviour of Schrödinger’s nonlinear (2+1) complex conformable time fractional model, Optical and Quantum Electronics, 2024.
  5. Dispersive optical soliton solutions to the truncated time M-fractional paraxial wave equation with its stability analysis, 2024.
  6. Dynamical behaviors of analytical and localized solutions to the generalized Bogoyavlvensky–Konopelchenko equation arising in mathematical physics, 2024.
  7. Stochastic wave solutions of fractional Radhakrishnan–Kundu–Lakshmanan equation arising in optical fibers with their sensitivity analysis, 2024.
  8. Analysis of bifurcation, chaotic structures, lump and M − W-shape soliton solutions to (2 + 1) complex modified Korteweg-de-Vries system, 2024.
  9. Retrieval of diverse soliton, lump solutions to a dynamical system of the nonlinear Biswas–Milovic equation and stability analysis, 2024.
  10. Stability analysis and soliton solutions of truncated M-fractional Heisenberg ferromagnetic spin chain model via two analytical methods, 2024.
  11. Analysis of new soliton type solutions to generalized extended (2 + 1)-dimensional Kadomtsev-Petviashvili equation via two techniques, 2024.

Conclusion:

Sonia Akram is a promising researcher with a solid foundation in applied mathematics and a notable publication record. Her innovative approach to integrating AI into her research, coupled with her academic achievements, positions her as a strong candidate for the Best Researcher Award. By addressing some areas for improvement, particularly in networking and broader impact, she can further elevate her research profile and contribution to the field. Recognizing her efforts with this award would not only honor her achievements but also encourage her continued growth and innovation in mathematical research.

Majed Almubarak | Geomechanics | Best Researcher Award

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Mr Majed Almubarak | Geomechanics | Best Researcher Award

PhD Student, Massachusetts Institute of Technology, United States

Majed AlMubarak is a dedicated PhD candidate in Petroleum Engineering at Texas A&M University, where he maintains a perfect GPA of 4.0. With a rich academic background that includes a Master’s degree from MIT and a Bachelor’s degree from Texas A&M, Majed has consistently demonstrated excellence in his studies. He has significant experience in both industry and research, having worked as a reservoir engineer at Saudi Aramco and contributed to various high-impact research projects. Majed is passionate about advancing energy technologies and sustainable practices within the petroleum industry.

Profile

Google Scholar

Strengths for the Award

  1. Academic Excellence:
    • Majed has demonstrated exceptional academic performance throughout his educational journey, achieving a 4.0 GPA in his PhD program and a 4.9 GPA in his Master’s degree at MIT. His summa cum laude distinction during his undergraduate studies further showcases his commitment to excellence.
  2. Diverse Research Experience:
    • His extensive research background spans multiple prestigious institutions, including Texas A&M University and MIT. He has participated in various impactful projects related to petroleum engineering, rock mechanics, and geothermal systems, indicating a breadth of knowledge and adaptability in different research environments.
  3. Innovative Contributions:
    • Majed’s research on CO2 injection challenges, electro-hydraulic fracturing, and the development of novel fracturing fluids demonstrates his capacity for innovation in addressing complex industry challenges. His work on smart underground space exploration also reflects a forward-thinking approach to integrating technology and research.
  4. Publication Record:
    • With multiple published works and citations in prominent journals and conferences, Majed has established himself as a thought leader in his field. His involvement in various projects that contribute to the understanding of fluid dynamics and rock interactions enhances his visibility and credibility as a researcher.
  5. Industry Experience:
    • His practical experience as a reservoir engineer at Saudi Aramco equips him with a strong understanding of real-world applications, enhancing his research’s relevance and applicability to the petroleum industry.

Areas for Improvement

  1. Networking and Collaboration:
    • While Majed has a strong foundation, increasing his engagement in interdisciplinary collaborations could enrich his research perspectives and lead to novel findings. Actively participating in more workshops and conferences can enhance his professional network.
  2. Broader Impact of Research:
    • Focusing on how his research can be translated into broader societal benefits, such as environmental sustainability and energy efficiency, could enhance the impact of his work and appeal to a wider audience.
  3. Leadership Roles:
    • Taking on leadership roles in research projects or student organizations could further develop his management and mentorship skills, positioning him as a leader in the academic community.

Education

Majed AlMubarak is currently pursuing a PhD in Petroleum Engineering at Texas A&M University, expected to graduate in 2026 with a 4.0 GPA. He holds a Master of Science in Civil and Environmental Engineering from MIT, where he achieved an impressive GPA of 4.9. Majed also earned his Bachelor of Science in Petroleum Engineering from Texas A&M University in 2019, graduating summa cum laude with a GPA of 3.91. His educational journey reflects a strong foundation in engineering principles, enhanced by rigorous coursework and research experiences that have shaped his expertise in geomechanics and reservoir engineering.

Experience

Majed AlMubarak has gained valuable industry experience as a Reservoir Engineer at Saudi Aramco’s EXPEC Advanced Research Center. During his tenure from 2019 to 2020, he led projects focusing on CO2 enhanced oil recovery and experimental work addressing CO2 injection challenges. His hands-on approach involved utilizing advanced monitoring techniques and conducting laboratory experiments to improve recovery efficiency. In addition to his industry experience, Majed has served as a Graduate Research Assistant at Texas A&M University and MIT, where he engaged in significant research projects, including acid fracturing geomechanics and electro-hydraulic fracturing for geothermal systems. His diverse experience positions him as a well-rounded professional in the field of petroleum engineering.

Awards and Honors

Majed AlMubarak has received numerous accolades throughout his academic career, underscoring his dedication and excellence in engineering. He was a finalist in the Best Young Professional SPE Endogenous Contest in 2020 and received the Distinguished Student Award from the Dwight Look College of Engineering in 2019. His commitment to research was recognized when he secured first place in the SPE Petroleum Engineering Student Paper Contest in 2018. Furthermore, Majed has consistently achieved academic excellence, earning a place on the President’s List and the Dean’s List from 2015 to 2019. His undergraduate studies were fully sponsored by Saudi Aramco Oil Company, reflecting his potential and the value he brings to the engineering community.

Research Focus

Majed AlMubarak’s research focuses on advancing the understanding of geomechanics and reservoir engineering within the petroleum sector. His current projects at Texas A&M University involve evaluating acid fracturing geomechanics in carbonate rocks and assessing fracture conductivity in the Austin Chalk Formation. He explores innovative solutions for CO2 enhanced oil recovery and examines the efficiency of matrix acid stimulation techniques. His previous work at MIT concentrated on electro-hydraulic fracturing and the effects of various parameters on rock testing, further enriching his expertise. Majed is dedicated to addressing challenges in the energy industry, particularly in improving recovery techniques and promoting sustainable practices through advanced engineering solutions.

Publication Top Notes

  • Investigation of acid-induced emulsion and asphaltene precipitation in low permeability carbonate reservoirs.
  • A collective clay stabilizers review.
  • Insights on potential formation damage mechanisms associated with hydraulic fracturing.
  • Recent advances in waterless fracturing fluids: A review.
  • Chelating agent for uniform filter cake removal in horizontal and multilateral wells: laboratory analysis and formation damage diagnosis.
  • Influence of zirconium crosslinker chemical structure and polymer choice on the performance of crosslinked fracturing fluids.
  • Zirconium crosslinkers: Understanding performance variations in crosslinked fracturing fluids.
  • Enhancing foam stability through a combination of surfactant and nanoparticles.
  • A study on the adsorption behavior of different surfactants in carbonate using different techniques.
  • Turning the most abundant form of trash worldwide into effective corrosion inhibitors for applications in the oil and gas industry.

Conclusion

Majed AlMubarak is a highly qualified candidate for the Best Researcher Award, showcasing exceptional academic achievements, a diverse research portfolio, and significant contributions to the field of petroleum engineering. His strengths in innovation, publication, and industry experience solidify his position as a leading researcher. By focusing on enhancing his networking, broadening the societal impact of his research, and developing leadership skills, Majed can further elevate his profile and influence in the academic and professional communities. His potential for continued excellence makes him a deserving candidate for this prestigious recognition.

Qi Liang | Pattern Recognition | Excellence in Research

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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.

Niloufar Salehi | Circular Supply Chains | Best Researcher Award

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Ms Niloufar Salehi | Circular Supply Chains | Best Researcher Award

MS Niloufar Salehi , KTH Royal Institute of Technology , Sweden

Niloufar Salehi is a dedicated PhD candidate at KTH Royal Institute of Technology in Stockholm, specializing in Circular Manufacturing Systems. With over five years of experience, Niloufar excels in developing data-driven decision-support tools that facilitate the transition to sustainable manufacturing practices. Her strategic and analytical mindset empowers her to address complex challenges in the field, aiming for innovative solutions that contribute to a circular economy. Niloufar’s collaborative spirit has fostered partnerships with international universities and research institutes, enriching academic discourse and resulting in numerous publications. She is deeply committed to sustainability, believing that urgent action is needed to ensure a better future.

Publication Profile

Google Scholar

Strengths for the Award

  1. Expertise in Circular Manufacturing: Niloufar has a deep understanding of Circular Manufacturing Systems, demonstrated through her extensive research and development of decision-support tools. Her work directly contributes to advancing sustainable practices in manufacturing.
  2. Strong Analytical and Problem-Solving Skills: Her ability to tackle complex challenges through multi-method simulation modeling shows a high level of analytical competence, making her a valuable asset in the field.
  3. Successful Project Management: Niloufar has effectively led and contributed to multiple EU-funded projects, showcasing her leadership in data gathering, project coordination, and stakeholder engagement.
  4. Collaborative Research Contributions: She has established fruitful collaborations with over four international universities, resulting in several impactful publications, reflecting her ability to work well in diverse research environments.
  5. Teaching and Mentoring: Her experience in teaching and supervising students demonstrates her commitment to knowledge dissemination and nurturing the next generation of researchers.

Areas for Improvement

  1. Broader Impact Communication: While her research is impactful, enhancing her communication skills to reach broader audiences could amplify the visibility of her contributions.
  2. Networking Expansion: Increasing her involvement in industry conferences and workshops may provide additional platforms for sharing her research and establishing more collaborations.
  3. Interdisciplinary Approaches: Exploring intersections with other disciplines, such as social sciences or policy-making, could enrich her research and broaden its applicability.

Education 

Niloufar holds a PhD in Production Engineering from KTH Royal Institute of Technology (2018–2024), where her thesis focuses on creating multi-method simulation model-based decision-support tools for Circular Supply Chain implementation. Prior to her doctoral studies, she earned an MSc in Energy Systems Engineering from Sharif University of Technology in Iran (2014–2017), achieving a GPA of 17.78/20 and ranking in the top 10% of her class. Her master’s thesis concentrated on optimizing recovery technologies for municipal solid waste through a multi-criteria decision-making model, highlighting her strong foundation in sustainability. Niloufar’s academic journey began with a BSc in Chemical Engineering, also from Sharif University (2009–2013), where she laid the groundwork for her future research in sustainable engineering practices and environmental management.

Experience 

Niloufar’s professional journey spans significant roles, including her current position as a PhD candidate at KTH Royal Institute of Technology, where she leads data gathering and project management efforts for multiple EU-funded projects. Notably, she has contributed to Horizon Europe’s DiCiM project, focusing on digitalized value management for Circular Manufacturing Systems. In her role within H2020 ReCiPSS, she has been instrumental in transitioning linear supply chains to circular ones, developing multi-method simulation models that assess economic, environmental, and technical performance. Additionally, Niloufar has experience as a researcher at Sharif Energy Research Institute, where she conducted techno-economic analyses related to anaerobic digestion. Her extensive teaching experience includes courses on Circular Manufacturing Systems at KTH and guest lectures at various institutions, demonstrating her commitment to sharing knowledge and fostering future generations in the field.

Research Focus 

Niloufar’s research focuses on Circular Manufacturing Systems, specifically the development of decision-support tools that facilitate the implementation of circular supply chains. She employs multi-method simulation models to analyze the complexities of manufacturing systems and support sustainable practices. Her work emphasizes data-driven solutions for transitioning traditional linear supply chains into circular frameworks, addressing the urgent need for sustainability in manufacturing. Key areas of interest include resource efficiency, waste reduction, and the integration of digital technologies in manufacturing processes. Niloufar’s research contributions also explore stakeholder dynamics in circular supply chains, ensuring that economic, environmental, and technical aspects are considered for effective decision-making. Through collaboration with international partners and participation in various EU-funded projects, she aims to enhance academic knowledge and provide practical solutions that drive the adoption of circular economy principles in manufacturing.

Publications Top Notes

  • Amir, S., Salehi, N., Roci, M., Sweet, S., & Rashid, A. (2022). Towards circular economy: A guiding framework for circular supply chain implementation. 📚
  • Roci, M., Salehi, N., Amir, S., Shoaib-ul-Hasan, S., Asif, F. M. A., Mihelič, A., & Rashid, A. (2022). Towards circular manufacturing systems implementation: A complex adaptive systems perspective using modelling and simulation as a quantitative analysis tool. 🌍
  • Roci, M., Salehi, N., Amir, S., Asif, F. M. A., Shoaib-ul-Hasan, S., & Rashid, A. (2022). Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making. 🛠️
  • Salehi, N., Amir, S., Roci, M., Shoaib-ul-Hasan, S., Asif, F. M. A., Mihelič, A., Sweet, S., & Rashid, A. (2024). Towards circular manufacturing systems implementation: An integrated analysis framework for circular supply chains. 🔄
  • Asif, F. M., Salehi, N., & Lieder, M. (2022). Consumer Perceptions of the Circular Business Model: A Case of Leasing Strollers. 👶
  • Kokare, S., Asif, F. M. A., Mårtensson, G., Shoaib-ul-Hasan, S., Rashid, A., Roci, M., & Salehi, N. (2021). A comparative life cycle assessment of stretchable and rigid electronics: a case study of cardiac monitoring devices. ❤️
  • Shoaib-ul-Hasan, S., Roci, M., Asif, F. M. A., Salehi, N., & Rashid, A. (2021). Analyzing temporal variability in inventory data for life cycle assessment: Implications in the context of circular economy. 📊
  • Villamil Velasquez, C., Salehi, N., & Hallstedt, S. I. (2020). How Can Information and Communications Technology Support the Link Between Circular Economy and Product Life Cycle Management? – a Review. 💻
  • Salehi, N., Mahmoudi, M., Bazargan, A., & McKay, G. (2019). Exergy and Life Cycle-Based Analysis. In C. M. Hussain (Ed.), Handbook of Environmental Materials Management (pp. 1057–1078). 📖

Conclusion

Niloufar Salehi exemplifies the qualities of a leading researcher in the field of Circular Manufacturing Systems. Her strong foundation in data-driven decision support, coupled with her project management and collaborative skills, positions her as a frontrunner for the Best Researcher Award. With targeted improvements in communication and networking, she has the potential to significantly elevate her impact in both academic and industrial contexts.