Busra Buran | Decision Making | Best Researcher Award

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Dr. Busra Buran | Decision Making | Best Researcher Award

Research Scholar| Istanbul Technical University, Turkey

Dr. Büşra Buran, born on August 13, 1988, in Kocaeli, Turkey, is a prominent expert in management engineering with a career deeply rooted in public transportation strategy and optimization. She currently serves as the Head of Strategy Development at IETT, Istanbul’s public transport authority. With over a decade of professional experience, Dr. Buran has successfully led numerous projects involving strategic planning, quality management, service improvement, and international collaboration. Her academic achievements include a Ph.D. in Management Engineering from Istanbul Technical University, an M.Sc. in Industrial Engineering from Galatasaray University, and a B.Eng. in Industrial Engineering from Yıldız Technical University. Fluent in English, she combines technical expertise with leadership capabilities, supported by various certifications in project management, quality control, and innovation. Her work has been recognized internationally, and she actively contributes to research with several publications focusing on fuzzy logic applications, transportation models, and digital transformation strategies in public transit systems.

Profile

🎓 Education

Dr. Büşra Buran holds a robust academic background in industrial and management engineering. She completed her Ph.D. in Business Management Engineering at Istanbul Technical University (2018–2023), graduating with a GPA of 3.5/4. Her doctoral research focused on advanced decision-making methods and innovation in public transportation. Prior to that, she earned her Master of Science in Industrial Engineering from Galatasaray University (2010–2012), achieving a GPA of 3.5/4. She began her academic journey with a Bachelor’s degree in Industrial Engineering from Yıldız Technical University (2007–2010), graduating with distinction (3.4/4). Her academic excellence is complemented by certifications in project management, innovation, quality systems, and team leadership. Dr. Buran’s education is deeply integrated with applied engineering practices, statistical modeling (SPSS, Minitab), simulation tools (Simul8), and machine learning (R), equipping her with a strong foundation in both theory and practice. Her studies enabled her to apply analytical, technical, and managerial skills to large-scale public service systems.

🧪 Experience

Dr. Büşra Buran has extensive professional experience in the public transportation sector, primarily at IETT, Istanbul’s leading transit authority. Since 2017, she has been serving as the Head of Strategy Development, where she manages strategic planning, performance metrics, quality systems, corporate innovation, and international partnerships. From 2014 to 2017, she was the Manager of Service Improvement, leading the development of a service quality model, auditing standards, and benchmarking global practices. Between 2013 and 2014, she worked as the Manager of Operation Planning, handling performance analysis and optimization of bus networks, including collaborative projects with TÜBİTAK. Her career began in 2010 as a Consultant for Istanbul’s Bus Rapid Transit system, optimizing routes and managing training for international BRT implementations. She is experienced in project management, process auditing, KPI reporting, and digital transformation. Her leadership has significantly enhanced the efficiency and global standing of Istanbul’s public transport network.

🏅 Awards and Honors

Dr. Büşra Buran has consistently demonstrated academic and professional excellence throughout her career. She graduated with honors at every level—achieving GPAs of 3.4/4 in her undergraduate studies, 3.5/4 in her master’s, and 3.5/4 during her Ph.D. She has also been recognized for her contributions to transportation planning and quality improvement at IETT, receiving internal commendations for successful project outcomes and strategic innovations. Her scientific research on fuzzy logic applications in public transport and sentiment analysis during the COVID-19 pandemic has been published in prestigious journals and international conferences by Springer and Elsevier. She has also earned certifications in Project Management, Corporate Quality, Risk & Process Management, and Leadership Innovation, strengthening her credentials as a thought leader in urban mobility systems. Her scholarly achievements, combined with her impactful real-world projects, highlight her as a distinguished figure in engineering management and urban transportation.

🔬 Research Focus

Dr. Buran’s research centers on the intersection of engineering management, public transportation systems, and fuzzy decision-making models. Her Ph.D. work and subsequent publications explore advanced methodologies such as Spherical and Intuitionistic Fuzzy AHP, hybrid fuzzy systems, sentiment analysis, and business model frameworks tailored for urban transport. She has published widely on topics such as bus type selection, public transport quality modeling, and global benchmarking of transit business models. Her studies frequently employ machine learning and simulation tools like R and Simul8, along with statistical software for system optimization. A significant part of her work also delves into digital transformation, strategic innovation, and service efficiency in public bus systems, particularly in mega-cities like Istanbul. Her research not only contributes theoretical advancements in engineering management but also provides practical insights for decision-makers in transit agencies, enhancing transportation planning, passenger satisfaction, and sustainable mobility strategies.

✅ Conclusion

Dr. Büşra Buran exemplifies the synergy between academic research and public sector innovation. With her strong background in industrial and management engineering, she has driven strategic advancements in Istanbul’s public transportation. Her work is a testament to effective leadership, data-driven decision-making, and continuous quality improvement in urban mobility systems.

Publications

Quanying Lu | Forecasting | Best Researcher Award

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Dr. Quanying Lu | Forecasting | Best Researcher Award

Dr. Quanying Lu is an Associate Professor at Beijing University of Technology, specializing in energy economics, forecasting, and systems engineering. 🎓 She completed her Ph.D. at the University of Chinese Academy of Sciences and has published 30+ papers in top journals, including Nature Communications and Energy Economics. 📚 She has held postdoctoral and research positions in prestigious institutions and actively contributes to policy research. 🌍

Profile

Education 🎓

  • Ph.D. (2017-2020): University of Chinese Academy of Sciences, School of Economics and Management, supervised by Prof. Shouyang Wang.
  • M.Sc. (2014-2017): International Business School, Shaanxi Normal University, supervised by Prof. Jian Chai.
  • B.Sc. (2010-2014): International Business School, Shaanxi Normal University, Department of Economics and Statistics.

Experience 👨‍🏫

  • Associate Professor (06/2022–Present), Beijing University of Technology, supervising Ph.D. students.
  • Postdoctoral Fellow (07/2020–05/2022), Academy of Mathematics and Systems Science, Chinese Academy of Sciences.
  • Research Assistant (08/2018–10/2018), Department of Management Sciences, City University of Hong Kong.

Awards & Recognitions 🏅

  • Outstanding Young Talent, Phoenix Plan, Chaoyang District, Beijing (2024).
  • Young Scholar of Social Computing, CAAI-BDSC (2024).
  • Young Scholar of Forecasting Science, Frontier Forum on Forecasting Science (2024).
  • Young Elite Scientists Sponsorship, BAST (2023).
  • Excellent Mentor, China International “Internet Plus” Innovation Competition (2023).

Research Interests 🔬

Dr. Lu specializes in energy economics, environmental policy analysis, economic forecasting, and systems engineering. 📊 Her research addresses crude oil price dynamics, carbon reduction strategies, and financial market interactions. 💡 She integrates machine learning with forecasting models, contributing to sustainable energy and environmental policies. 🌍

Publications 

[1] Liang, Q., Lin, Q., Guo, M., Lu, Q., Zhang, D. Forecasting crude oil prices: A
Gated Recurrent Unit-based nonlinear Granger Causality model. International
Review of Financial Analysis, 2025, 104124.
[2] Wang, S., Li, J., Lu, Q. (2024) Optimization of carbon peaking achieving paths in
Chinas transportation sector under digital feature clustering. Energy, 313,133887
[3] Yang, B., Lu, Q.*, Sun, Y., Wang, S., & Lai, K. K. Quantitative evaluation of oil
price fluctuation events based on interval counterfactual model (in Chinese).
Systems Engineering-Theory & Practice, 2023, 43(1):191-205.
[4] Lu, Q.*, Shi, H., & Wang, S. Estimating the shock effect of “Black Swan” and
“Gray Rhino” events on the crude oil market: the GSI-BN research framework (in
Chinese). China Journal of Econometrics, 2022, 1(2): 194-208.
[5] Lu, Q., Duan, H.*, Shi, H., Peng, B., Liu, Y., Wu, T., Du, H., & Wang, S*. (2022).
Decarbonization scenarios and carbon reduction potential for China’s road
transportation by 2060. npj Urban Sustainability, 2: 34. DOI:
https://www.nature.com/articles/s42949-022-000.
[6] Lu, Q., Sun, Y.*, Hong, Y., Wang, S. (2022). Forecasting interval-valued crude
oil prices via threshold autoregressive interval models. Quantitative Finance,
DOI: 10.1080/14697688.2022.2112065
Page 3 / 6
[7] Guo, Y., Lu, Q.*, Wang, S., Wang, Q. (2022). Analysis of air quality spatial
spillover effect caused by transportation infrastructure. Transportation Research
Part D: Transport & Environment, 108, 103325.
[8] Wei, Z., Chai, J., Dong, J., Lu, Q. (2022). Understanding the linkage-dependence
structure between oil and gas markets: A new perspective. Energy, 257, 124755.
[9] Chai, J., Zhang, X.*, Lu, Q., Zhang, X., & Wang, Y. (2021). Research on
imbalance between supply and demand in China’s natural gas market under the
double -track price system. Energy Policy, 155, 112380.
[10]Lu, Q., Sun, S., Duan, H.*, & Wang, S. (2021). Analysis and forecasting of crude
oil price based on the variable selection-LSTM integrated model. Energy
Informatics, 4 (Suppl 2):47.
[11]Shi, H., Chai, J.*, Lu, Q., Zheng, J., & Wang, S. (2021). The impact of China’s
low-carbon transition on economy, society and energy in 2030 based on CO2
emissions drivers. Energy, 239(1):122336, DOI: 10.1016/j.energy.2021.122336.
[12]Jiang, S., Li, Y., Lu, Q., Hong, Y., Guan, D.*, Xiong, Y., & Wang, S.* (2021).
Policy assessments for the carbon emission flows and sustainability of Bitcoin
blockchain operation in China. Nature Communications, 12(1), 1-10.
[13]Jiang, S., Li Y., Lu, Q., Wang, S., & Wei, Y*. (2021). Volatility communicator or
receiver? Investigating volatility spillover mechanisms among Bitcoin and other
financial markets. Research in International Business and Finance,
59(4):101543.
[14]Lu, Q., Li, Y., Chai, J., & Wang, S.* (2020). Crude oil price analysis and
forecasting ：A perspective of “new triangle”. Energy Economics, 87, 104721.
DOI: 10.1016/j.eneco.2020.104721.
[15]Chai, J., Shi, H.*, Lu, Q., & Hu, Y. (2020). Quantifying and predicting the
Water-Energy-Food-Economy-Society-Environment Nexus based on Bayesian
networks – a case study of China. Journal of Cleaner Production, 256, 120266.
DOI: 10.1016/j.jclepro.2020.120266.
[16]Lu, Q., Chai, J., Wang, S.*, Zhang, Z. G., & Sun, X. C. (2020). Potential energy
conservation and CO2 emission reduction related to China’s road transportation.
Journal of Cleaner Production, 245, 118892. DOI:
10.1016/j.jclepro.2019.118892.
[17]Chai, J., Lu, Q.*, Hu, Y., Wang, S., Lai, K. K., & Liu, H. (2018). Analysis and
Bayes statistical probability inference of crude oil price change point.
Technological Forecasting & Social Change, 126, 271-283.
[18]Chai, J., Lu, Q.*, Wang, S., & Lai, K. K. (2016). Analysis of road transportation
consumption demand in China. Transportation Research Part D: Transport &
Environment, 2016, 48:112-124.

 

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

Gokhan Yildirim | Marketing analytics | Best Researcher Award

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Dr. Gokhan Yildirim | Marketing analytics | Best Researcher Award

Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School, specializing in marketing analytics and return on investment. His expertise spans digital marketing, long-term marketing effectiveness, and customer mindset metrics. With a strong foundation in applied time series econometrics and machine learning, he has made significant contributions to the field of marketing science. Yildirim has held academic positions at Lancaster University and has been a visiting researcher at Tilburg University. His research has been widely published in top-tier journals, influencing both academia and industry.

Profile

Education 🎓

Gokhan Yildirim earned his PhD in Business Administration and Quantitative Methods from Universidad Carlos III de Madrid (UC3M) in 2012, with a dissertation on marketing dynamics. His academic journey began with a BA in Business Administration (1999–2003) and an MSc in Quantitative Methods (2003–2006) from Marmara University, Istanbul. He also conducted research as a visiting scholar at Tilburg University, Netherlands, further strengthening his expertise in marketing analytics and econometrics.

Experience 👨‍🏫

Yildirim has been an Associate Professor of Marketing at Imperial College Business School since 2019, following his tenure as an Assistant Professor from 2016 to 2019. Before that, he was an Assistant Professor of Marketing Analytics at Lancaster University (2012–2016). His industry collaborations focus on marketing resource allocation, customer analytics, and data-driven decision-making. His research integrates econometric modeling and machine learning to optimize marketing strategies and enhance business performance.

Research Interests 🔬

Yildirim’s research centers on return on marketing investment, digital marketing effectiveness, and customer mindset metrics. He applies advanced econometric and machine learning techniques to analyze marketing resource allocation and long-term advertising impacts. His work explores how marketing strategies influence consumer behavior and business growth, contributing to both academic literature and real-world marketing practices

Awards & Recognitions 🏅

Yildirim has received several prestigious awards, including the 2017–2018 Gary Lilien ISMS-MSI-EMAC Practice Prize for his work on multichannel marketing at L’Occitane. He has also secured multiple research grants, such as the Wharton Customer Analytics Initiative (2015–2016) and the Spanish Ministry of Science and Innovation grants (2012–2018). His contributions have been recognized through funding from AiMark and other leading research bodies, further cementing his influence in marketing analytics.

Publications 📚