Farshad Sadeghpour | Data prediction | Best Researcher Award

Dr. Farshad Sadeghpour | Data prediction | Best Researcher Award

Farshad Sadeghpour (b. 1996) šŸ‡®šŸ‡· is a Petroleum Engineer and Data Scientist šŸ’»šŸ›¢ļø with expertise in reservoir engineering, petrophysics, and AI applications in the energy sector. Based in Tehran, Iran šŸ“, he holds a Master’s and Bachelor’s in Petroleum Exploration. With extensive experience in EOR, SCAL/RCAL analysis, and machine learning, Farshad has contributed to both academic and industrial R&D at RIPI, NISOC, and PVP. He has published multiple research articles šŸ“š, won international awards šŸ†, and participated in key petroleum projects. He served in the military šŸŖ– and actively collaborates with academia and industry on AI-driven energy solutions.

Profile

Education šŸŽ“

šŸ§‘ā€šŸŽ“ Master’s in Petroleum Engineering (Petroleum Exploration), Petroleum University of Technology, Abadan šŸ‡®šŸ‡· (2019–2022) | GPA: 18.82/20
šŸŽ“ Bachelor’s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran šŸ‡®šŸ‡· (2015–2019) | GPA: 19.14/20
šŸ“š Courses covered include reservoir engineering, geomechanics, well-logging, and advanced data analytics.
šŸ› ļø Projects include COā‚‚ storage modeling, permeability prediction via AI, and LWD-based mud loss forecasting.
šŸ“Š Developed key industry collaborations with NISOC, RIPI, and OEID through thesis, internships, and military service projects.
šŸ’” Honed computational and simulation skills using MATLAB, Python, COMSOL, Petrel, and ECLIPSE.
šŸ›ļø Academic mentors: Dr. Seyed Reza Shadizadeh, Dr. Bijan Biranvand, Dr. Majid Akbari.

Experience šŸ‘Øā€šŸ«


šŸ”¬ Computer Aided Process Engineering (CAPE) – Petroleum Reservoir Engineer (Nov 2024–Present)
šŸ›¢ļø Petro Vision Pasargad – Reservoir Engineer & Lab Operator (Sep 2023–May 2024)
🧠 Research Institute of Petroleum Industry (RIPI) – Petroleum Engineer, Data Scientist (Mar 2023–Apr 2024)
šŸ­ National Iranian South Oil Company (NISOC) – Petroleum Engineer, Petrophysicist (Mar 2021–Nov 2024)
🧪 Internships: NIOC – Exploration Management, Oil & Energy Industries Development (OEID)
šŸ“Š Key contributions include EOR analysis, SCAL/RCAL lab testing, permeability modeling, machine learning pipelines, and field data analysis.
🧾 Delivered reports, simulations, and AI models supporting production optimization and reservoir characterization.

Awards & Recognitions šŸ…

šŸ„‰ 3rd Prize Winner – EAGE Laurie Dake Challenge 2022 (Madrid, Spain) šŸŒ
šŸŽ–ļø Recognized for thesis excellence in AI-driven mud loss prediction with NISOC collaboration
šŸ“Œ Acknowledged during military service project with RIPI for developing ANN-based well log models
šŸ… Published in high-impact journals such as Energy, Geoenergy Science and Engineering, and JRMGE
āœļø Co-author of multiple peer-reviewed papers and under-review articles across petroleum engineering disciplines
šŸ”¬ Worked alongside top researchers including Dr. Ostadhassan, Dr. Gao, and Dr. Hemmati-Sarapardeh
šŸ› ļø Actively participated in multidisciplinary teams combining AI, geomechanics, and petrophysics
šŸ“¢ Regular presenter and contributor at petroleum conferences and AI-in-energy seminars.

Research Interests šŸ”¬

šŸ“Œ AI & ML applications in petroleum engineering šŸ§ šŸ›¢ļø – including ANN, genetic algorithms, and deep learning
šŸ“Š Mud loss zone prediction, formation permeability modeling, COā‚‚ storage feasibility using ML
🧪 Experimental rock mechanics: nanoindentation, geomechanical upscaling, SCAL/RCAL testing
šŸ“ˆ Petrophysical property estimation in carbonate and unconventional reservoirs
šŸŒ Reservoir simulation, LWD analysis, and smart data integration using Python, Petrel, COMSOL
šŸ“– Notable studies include: elastic modulus upscaling, kerogen behavior under pyrolysis, RQI/FZI modeling
šŸ”¬ Interdisciplinary projects bridging data science with geoscience and reservoir engineering
šŸ¤ Collaboration with academic and industry leaders to develop practical, AI-driven solutions for energy challenges.

PublicationsĀ 
  • Elastic Properties of Anisotropic Rocks Using an Stepwise Loading Framework in a True Triaxial Testing Apparatus

    Geoenergy Science and Engineering
    2025-04 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Hem Bahadur Motra;Ā Chinmay Sethi;Ā Sandra Wind;Ā Bodhisatwa Hazra;Ā Ghasem Aghli;Ā Mehdi Ostadhassan
  • Storage Efficiency Prediction for Feasibility Assessment of Underground CO2 Storage: Novel Machine Learning Approaches

    Energy
    2025-04 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour
  • A new petrophysical-mathematical approach to estimate RQI and FZI parameters in carbonate reservoirs

    Journal of Petroleum Exploration and Production Technology
    2025-03 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Kamran Jahangiri;Ā Javad Honarmand
  • Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt

    Journal of Rock Mechanics and Geotechnical Engineering
    2024-11 |Ā Journal article
    CONTRIBUTORS:Ā Ghasem Aghli;Ā Babak Aminshahidy;Ā Hem Bahadur Motra;Ā Ardavan Darkhal;Ā Farshad Sadeghpour;Ā Mehdi Ostadhassan
  • Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media

    Geoenergy Science and Engineering
    2024-08 |Ā Journal article
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Ardavan Darkhal;Ā Yifei Gao;Ā Hem B. Motra;Ā Ghasem Aghli;Ā Mehdi Ostadhassan

Quanying Lu | Forecasting | Best Researcher Award

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. šŸŽ“ 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. šŸ“š She has held postdoctoral and research positions in prestigious institutions and actively contributes to policy research. šŸŒ

Profile

Education šŸŽ“

  • 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 šŸ‘Øā€šŸ«

  • 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 šŸ…

  • 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 šŸ”¬

Dr. Lu specializes in energy economics, environmental policy analysis, economic forecasting, and systems engineering. šŸ“Š Her research addresses crude oil price dynamics, carbon reduction strategies, and financial market interactions. šŸ’” She integrates machine learning with forecasting models, contributing to sustainable energy and environmental policies. šŸŒ

PublicationsĀ 

[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 ā€œBlack Swanā€ and
ā€œGray Rhinoā€ 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 ā€œnew triangleā€. 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

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. šŸ“ŠšŸ§ šŸ”

Profile

Education šŸŽ“

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. šŸ“ššŸ§‘ā€šŸŽ“šŸ“ˆ

Experience šŸ‘Øā€šŸ«

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. šŸ«šŸ¤–šŸ“”

Research Interests šŸ”¬

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. šŸ§ šŸ“ŠšŸ–„ļø

Awards & Recognitions šŸ…

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. šŸŽ–ļøšŸ“œšŸ”¬

PublicationsĀ 

 

Chunyu Liu | Cognitive Computing | Best Researcher Award

Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. šŸ“š She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. šŸŽ“ She completed postdoctoral training at Peking University. šŸ”¬ Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. šŸ“ Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. šŸŒ Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. šŸš€

Profile

Education šŸŽ“

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. āž• She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. šŸ”¢ Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. šŸ–„ļø Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. šŸ”¬ Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. šŸ“Š Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience šŸ‘Øā€šŸ«

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. šŸŽ“ She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. šŸ” Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). šŸ“” She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. šŸ”¬ Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. šŸš€

Research Interests šŸ”¬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. šŸ“” Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. šŸŽÆ She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. šŸ¤– By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. šŸ† Her research also inspires the development of new AI theories and algorithms based on principles of brain function. šŸ“Š She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. šŸš€ Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. šŸ…

 

Awards & Recognitions šŸ…

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. šŸ… She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. šŸŽ–ļø Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. šŸ“œ Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. šŸ“ She has also been honored with research grants and funding for AI-driven cognitive studies. šŸ”¬ Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. šŸŒ Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. šŸš€

Publications šŸ“š

Gokhan Yildirim | Marketing analytics | Best Researcher Award

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 šŸŽ“

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 šŸ‘Øā€šŸ«

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 šŸ”¬

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 šŸ…

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 šŸ“š

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018–2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016–2017. Her academic background also includes a Master’s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004–2007) and a Master’s degree in Mathematics from the University of Marmara in Istanbul (1995–1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as ā€œData Analysis for Business Managementā€ (BBA3), ā€œData Processingā€ (MSc, e-learning), ā€œDigital and Salesā€ (GEP 5th year), and ā€œIntroduction to Statisticsā€ (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught ā€œQuantitative Data Analytics – SPSSā€ (GEP 4th year, hybrid learning) and supervised master’s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the ā€œCaisse de PrĆ©voyance Socialeā€ (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPS’s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019–2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTISĀ® Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

• Membership of the American Risk and Insurance Association (ARIA)
• Membership of the academic association AFSE.
• Member of the RED Flag Project of the University of OrlĆ©ans in cooperation with CRJPothier.
• Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
• Virtual Incubator Tailored to All Entrepreneurs (VITAE).
• Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescu‐Radu, D., & Yankol‐Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.

Jianbang Liu | AI-driven emotion | Best Researcher Award

Dr. Jianbang Liu | AI-driven emotion | Best Researcher Award

JianBang Liu is a faculty member at the Xinyu University, China, where he actively contributes to both research and education. His research interests lie at the intersection of Artificial Intelligence (AI), Human-Computer Interaction (HCI), and Artificial Sentiment Analysis, with a specific focus on developing AI-driven emotion and cognition analysis. He has published extensively in international journals, significantly advancing the fields of HCI and AI. He continues to explore innovative applications of these technologies, aiming to bridge theoretical research with practical implementations.

Profile

Education

JianBang Liu obtained his Master’s degree from Qilu University of Technology (Shandong Academy of Sciences), China, in 2018. He then completed his Ph.D. at the Institute of Visual Informatics, UniversitiKebangsaan Malaysia (National University of Malaysia), specializing in Human-Computer Interaction (HCI) and Artificial Intelligence (AI).

Research Interests

Artificial Intelligence (AI), Human-Computer Interaction (HCI), AI-driven emotion and cognition analysisRe

Research Innovation

Completed/Ongoing Research Projects: State the number of research projects you have completed or are currently working on.

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Books Published (ISBN): Specify the number of books you have published with ISBN numbers.

Patents Published/Under Process: Mention the number of patents you have published or are currently in the process of publishing.

JournalsPublished: State the number of articles you have published in indexed journals.

Editorial Appointments: If applicable, list any editorial positions you hold in journals or conferences.

Collaborations: Describe any significant collaborations you have been part of in your research career.

Professional Memberships: List memberships in professional organizations or societies relevant to your field.

Areas of Research: Specify the main areas or topics you focus on in your research work.

Books /Chapters in Books:

Local optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimization Algorithm (Intelligent Engineering Optimisation with the Bees Algorithm (978-3-031-64935-6/ 978-3-031-64936-3 (eBook)))

Publication

  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Personalized Emotion Analysis Based on Fuzzy Multi-Modal Transformer Model (SCI Q2)
  • Immersive VR Learning experiences from the perspective of telepresence, emotion, and cognition(SSCI Q1)