Farshad Sadeghpour | Data prediction | Best Researcher Award

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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 Masterā€™s and Bachelorā€™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‡· (2019ā€“2022) | GPA: 18.82/20
šŸŽ“ Bachelorā€™s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran šŸ‡®šŸ‡· (2015ā€“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) ā€“ Petroleum Reservoir Engineer (Nov 2024ā€“Present)
šŸ›¢ļø Petro Vision Pasargad ā€“ Reservoir Engineer & Lab Operator (Sep 2023ā€“May 2024)
šŸ§  Research Institute of Petroleum Industry (RIPI) ā€“ Petroleum Engineer, Data Scientist (Mar 2023ā€“Apr 2024)
šŸ­ National Iranian South Oil Company (NISOC) ā€“ Petroleum Engineer, Petrophysicist (Mar 2021ā€“Nov 2024)
šŸ§Ŗ 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.
šŸ§¾ Delivered reports, simulations, and AI models supporting production optimization and reservoir characterization.

Awards & Recognitions šŸ…

šŸ„‰ 3rd Prize Winner ā€“ 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 ā€“ including ANN, genetic algorithms, and deep learning
šŸ“Š Mud loss zone prediction, formation permeability modeling, COā‚‚ storage feasibility using ML
šŸ§Ŗ 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
    DOI:Ā 10.1016/j.geoen.2025.213883
    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
    DOI:Ā 10.1016/j.energy.2025.136040
    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
    DOI:Ā 10.1007/s13202-025-01952-6
    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
    DOI:Ā 10.1016/j.jrmge.2023.12.027
    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
    DOI:Ā 10.1016/j.geoen.2024.212915
    CONTRIBUTORS:Ā Farshad Sadeghpour;Ā Ardavan Darkhal;Ā Yifei Gao;Ā Hem B. Motra;Ā Ghasem Aghli;Ā Mehdi Ostadhassan

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

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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 (2018ā€“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 2016ā€“2017. Her academic background also includes a Masterā€™s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004ā€“2007) and a Masterā€™s degree in Mathematics from the University of Marmara in Istanbul (1995ā€“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 ā€“ SPSSā€ (GEP 4th year, hybrid learning) and supervised masterā€™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 CPSā€™s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019ā€“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:

ā€¢ Membership of the American Risk and Insurance Association (ARIA)
ā€¢ Membership of the academic association AFSE.
ā€¢ Member of the RED Flag Project of the University of OrlĆ©ans in cooperation with CRJPothier.
ā€¢ Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
ā€¢ Virtual Incubator Tailored to All Entrepreneurs (VITAE).
ā€¢ 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)
    Banulescuā€Radu, D., & Yankolā€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.

Vikas Palekar | Machine Leaning | Best Researcher Award

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Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

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Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

He understands the growing need for Machine Learning and has a keen interest in the field, which he considers a blessing. Recognizing the importance of managing large and complex computations to control various aspects of the human environment has led him into this vast world. He is particularly fascinated by machine learning, especially reinforcement learning, supervised learning, semi-supervised learning, outliers, and basic data challenges. Furthermore, optimization, an area of artificial intelligence that requires fundamental studies and a change in approach, is another of his key research interests.

Profile

Education

He holds a Masterā€™s degree in Artificial Intelligence Engineering from the University of Shafagh, completed in 2020. His thesis was titled “Trees Social Relations Optimization Algorithm: A New Swarm-Based Metaheuristic Technique to Solve Continuous and Discrete Optimization Problems.” He also earned a Bachelorā€™s degree in Software Engineering from Azad Lahijan University, which he attended from 2007 to 2011.

Research Interests

Theory: Reinforcement Learning (high-dimensional problems, regularized algorithms, model
learning,
representation learning and deep RL, learning from demonstration, inverse optimal control, deep
Reinforcement Learning); Machine Learning (statistical learning theory, nonparametric
algorithms, time series. processes, manifold learning, online learning); Large-scale Optimization;
Evolutionary Computation, Metaheuristic Algorithm, Deep Learning, Healthcare Machine
learning, Big Data, Data Problems (Imbalanced), Signal Analysis
Applications: Automated control, space affairs, robotic control, medicine and health, asymmetric
data, data science, scheduling, proposing systems, self-enhancing systems

Work Experience

He is a freelance programmer with expertise in various operating systems, including Microsoft Windows and Linux (Arch, Ubuntu, Fedora). He is proficient in software tools such as Microsoft Office, Anaconda, Jupyter, PyCharm, Visual Studio, Tableau, RapidMiner, MATLAB, and Visual Studio. His programming skills include Matlab, Python, C++, Scala, Java, and Julia, with a focus on data mining, data science, computer vision, and machine learning. He is experienced with Python libraries like Pandas, Numpy, Matplotlib, Seaborn, PyCV, TensorFlow, Time Series Analysis, Spark, Hadoop, and Cassandra. Additionally, he is skilled in using Github, Docker, and MySQL. His expertise spans machine learning, deep learning, imbalanced data, missing data, semi-supervised learning, healthcare machine learning, algorithm design, and metaheuristic algorithms. He is fluent in English and Persian.

Publications