Mr.Farshad Sadeghpour | Engineering | Best Researcher Award |

Researcher, at Petroleum University of Technology (PUT) ,Iran

Mr. Farshad Sadeghpour is a highly motivated and multidisciplinary Petroleum Engineer and Data Scientist with a strong academic foundation and hands-on industry experience. He specializes in applying artificial intelligence, machine learning, and geomechanical modeling to tackle real-world challenges in petroleum exploration and reservoir engineering. With an impressive track record of publications, research collaborations, and award-winning projects, Mr. Sadeghpour stands out as an innovative young professional pushing the frontiers of modern petroleum engineering.

Professional Profile

Orcid

Education 🎓

Mr. Sadeghpour holds a Master of Science in Petroleum Engineering (Exploration) from the Petroleum University of Technology, Abadan, Iran (2019–2022), where he graduated with a stellar GPA of 18.82/20. He earned his Bachelor of Science in Petroleum Engineering (Exploration) from the Islamic Azad University, Science and Research Branch, Tehran, Iran (2015–2019), achieving an exceptional GPA of 19.14/20. His academic journey reflects deep technical knowledge, diligence, and consistent excellence.

Experience 👩‍

Mr. Sadeghpour has worked across leading organizations in Iran’s energy sector. His roles include Petroleum Engineer, Petrophysicist, and Data Scientist at institutions such as the Research Institute of Petroleum Industry (RIPI), Petro Vision Pasargad (PVP), Computer Aided Process Engineering (CAPE), and the National Iranian South Oil Company (NISOC). His work involved RCAL, SCAL, EOR laboratory operations, geomechanical modeling, machine learning implementation, and reservoir data analysis—reflecting a strong blend of fieldwork, laboratory experience, and data-driven insights.

Research Interests 🔬

Farshad’s research is at the intersection of petroleum engineering, data science, and geomechanics. He focuses on using machine learning, deep learning, and AI-based models to solve complex reservoir problems such as mud loss prediction, permeability estimation, and CO₂ storage assessment. His work emphasizes both theoretical modeling and practical industry applications, often conducted in collaboration with organizations such as NISOC, RIPI, and the National Iranian Oil Company. His master’s thesis and several projects revolve around neural networks, genetic algorithms, and petrophysical characterization, showing his innovative edge.

Awards 🏆

Farshad’s dedication to excellence was recognized internationally when he secured Third Prize in the EAGE Laurie Dake Challenge 2022 held in Madrid, Spain—a highly competitive event for petroleum engineering students worldwide. He has also been involved in significant national-level research projects with reputed institutions, showcasing his contributions to both academic and industrial progress.

Top Noted Publications 📚

Farshad has co-authored and led multiple high-impact publications in Q1 journals, including:

Energy (2025): Machine learning for CO₂ storage feasibility.

Journal of Petroleum Exploration and Production Technology (2025): New petrophysical-mathematical approach for RQI and FZI.

Geoenergy Science and Engineering (2024): Upscaling methods for elastic modulus prediction.

Journal of Rock Mechanics and Geotechnical Engineering (2024): Stress effects on fracture development in the Asmari reservoir.

Multiple papers under review in Marine and Petroleum Geology, International Journal of Coal Geology, and others.

Conclusion

In conclusion, Farshad Sadeghpour exemplifies the profile of a next-generation energy researcher—technically brilliant, research-oriented, and industry-relevant. His interdisciplinary expertise, publication record, award-winning work, and innovative mindset make him an outstanding candidate for prestigious recognitions such as the Best Researcher Award. His contributions are not only academically significant but also strategically aligned with the global shift toward smart and sustainable energy solutions.

Dr. Han Gao | Artificial Intellegence | Best Researcher Award

postdoctoral fellow, at Harvard University, United States.

Dr. Han Gao is a dedicated researcher specializing in scientific deep learning, computational mechanics, and generative models for spatiotemporal physics. With a strong background in machine learning-driven physics simulations, he has contributed significantly to advancing numerical modeling and data-driven solutions for complex physical systems. His work bridges the gap between deep learning and traditional computational fluid dynamics, with applications in turbulence modeling, inverse problems, and reduced-order modeling.

Professional Profile

Scopus

Google Scholar

Education 🎓

Dr. Gao earned his Ph.D. in Aerospace and Mechanical Engineering from the University of Notre Dame (2018–2023), where he focused on scientific deep learning for forward and inverse modeling of spatiotemporal physics. He also holds a Master’s degree in Mechanical Engineering & Materials Science from Washington University in St. Louis (2016–2018), with research on numerical simulations of jet impingement and rotor blade effects. His academic journey began with a Bachelor’s degree in Mechanical Engineering from Shanghai University of Electric Power (2012–2016).

Professional Experience 💼

Dr. Gao is currently a Postdoctoral & Teaching Fellow at Harvard University (2023–present), where he continues his research in deep learning-driven physics simulations while mentoring students. Previously, he served as a Research & Teaching Assistant at the University of Notre Dame (2018–2023) and Washington University in St. Louis (2016–2018). Additionally, he gained industry experience as a Research Intern at Google Research (2022), where he worked on advanced AI-driven physics simulations.

Research Interests 🌍

Dr. Gao’s research revolves around the integration of deep learning techniques with physics-based modeling, particularly in solving partial differential equations (PDEs), turbulence modeling, generative models, and reduced-order modeling. He has developed novel physics-informed neural networks (PINNs), Bayesian generative models, and machine-learning frameworks for high-dimensional complex systems. His work is widely applicable in computational fluid dynamics (CFD), climate modeling, aerodynamics, and engineering simulations.

Awards & Honors 🏆

Dr. Gao has been recognized for his outstanding contributions to computational mechanics and machine learning applications in physics. His publications in top-tier journals, including Nature Communications, and prestigious machine learning conferences such as NeurIPS, ICML, and ICLR, reflect his impact in the field. He has also received competitive research opportunities, including a Google Research internship, showcasing his industry relevance.

Top Noted Publications 📚

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
L. Sun, H. Gao, S. Pan, J.-X. Wang – 916 citations – Computer Methods in Applied Mechanics and Engineering, 2020

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
H. Gao, L. Sun, J.-X. Wang – 567 citations – Journal of Computational Physics, 2021

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
H. Gao, M. J. Zahr, J.-X. Wang – 240 citations – Computer Methods in Applied Mechanics and Engineering, 2022

Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
H. Gao, L. Sun, J.-X. Wang – 214 citations – Physics of Fluids, 2021

Predicting physics in mesh-reduced space with temporal attention
X. Han, H. Gao, T. Pfaff, J.-X. Wang, L.-P. Liu – 106 citations – ICLR, 2022

Conclusion

Dr. Han Gao is a highly promising researcher with a strong publication record, interdisciplinary expertise, and experience at prestigious institutions. His contributions to scientific deep learning and computational mechanics make him a strong contender for the Best Researcher Award. To further solidify his case, he could focus on gaining more individual recognitions, expanding his leadership roles, and demonstrating the real-world impact of his research.