Iman Hajird | Irrigation and Drainage | Editorial Board Member

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Dr. Iman Hajird | Irrigation and Drainage | Editorial Board Member

University of Tehran | Iran

Dr. Iman Hajirad is a water engineering researcher whose contributions to irrigation science and sustainable water management are reflected in his growing scholarly impact, with 60 citations, an h-index of 5, and an i10-index of 1. His work focuses on advancing irrigation efficiency, improving crop water productivity, and developing climate-resilient water strategies for arid and semi-arid regions. He has conducted extensive research on pulsed and continuous drip irrigation systems, analyzing their effects on yield, evapotranspiration, soil moisture dynamics, and crop response factors, particularly for silage maize and wheat. Dr. Hajirad employs soil water balance models, crop coefficient estimation methods, and advanced simulation tools such as HYDRUS-2D to deepen understanding of soil–water interactions under variable irrigation regimes. His studies also integrate remote sensing data from platforms like WaPOR, alongside IoT-enabled irrigation systems, to support precision agriculture and smart water management. He has explored nonlinear growth modeling, irrigation scheduling, and practical strategies for optimizing yield under saline and water-scarce conditions, contributing valuable insights for sustainable agricultural planning. In recent work, he has addressed broader themes such as climate-resilient water infrastructure, global water resource adaptation, and innovative irrigation practices for environmental sustainability. Dr. Hajirad also plays an active role in academic communication through editorial leadership in several scientific journals and has received multiple national awards and best paper recognitions for his impactful research. His body of work advances efficient irrigation, data-driven water management, and resilient agricultural systems.

Profile: Google Scholar

Featured Publications

  • Mohammadi, S., Mirlatifi, S. M., Homaee, M., Dehghanisanij, H., & Hajirad, I. (2024). Evaluation of silage maize production under pulsed drip irrigation in a semi-arid region. Irrigation Science, 42(2), 269–283.

  • Hajirad, I., Mohammadi, S., & Dehghanisanij, H. (2023). Determining the critical points of a basin from the point of view of water productivity and water consumption using the WaPOR database. Environmental Sciences Proceedings, 25(1), 86.

  • Pourgholam-Amiji, M., Hajirad, I., Nayebi, J., Alavi, S. R., Nozari, F., & Akbarpour, M. (2024). Improving wheat irrigation productivity in Iran (Part one: From the viewpoint of irrigation system and water management). Water and Soil Management and Modelling, 4(1), 171–193.

  • Hajirad, I., Mirlatifi, S. M., Dehghanisanij, H., & Mohammadi, S. (2021). Determining yield response factor (Ky) of silage maize under different irrigation levels of pulsed and continuous irrigation management. Central Asian Journal of Plant Science Innovation, 1(4), 214–220.

  • Hajirad, I., Mirlatifi, S. M., Dehaghani, S. H., & Mohammadi, S. (2021). Investigating the effect of deficit irrigation on yield and water productivity of silage maize under pulsed and continuous drip irrigation management. Iranian Water Research Journal, 15(342), 15–23.*

Ali Rafe | Food Sciecne and Technology | Editorial Board Member

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Prof. Dr. Ali Rafe | Food Sciecne and Technology | Editorial Board Member

Research Institute of Food Sciecne and Technology | Iran

Prof. Dr. Ali Rafe is a leading researcher in food engineering whose work focuses on sustainable food systems, plant proteins, biopolymer interactions, and the rheological and interfacial properties of complex food matrices. His research advances the understanding of protein–polysaccharide complexation, electrostatic coacervation, and structure–function relationships, supporting the development of clean-label, high-performance, and nutritionally enhanced food formulations. Prof. Dr. Ali Rafe has made major contributions to encapsulation science by designing complex coacervates and nanophytosomes for protecting and delivering sensitive bioactive compounds such as saffron constituents, barberry anthocyanins, and pomegranate extracts, achieving improved stability, controlled release, and bioavailability. His work extensively investigates how pH, ionic environments, and processing conditions affect the physicochemical and rheological behaviors of plant and dairy protein systems—including canola, sesame, and wheat germ proteins—thereby enhancing texture, sensory attributes, and overall functionality in diverse food products. He also explores advanced processing methods such as pulsed electric fields, cold plasma, and enzymatic treatments to improve extraction efficiency, antioxidant retention, and techno-functional performance of food ingredients. Beyond fundamental research, he contributes to food product development in areas such as pickled cucumbers, dairy creams, ketchup systems, and hydrocolloid-stabilized formulations. With 2743 citations, an h-index of 33, and an i10-index of 55, Prof. Dr. Ali Rafe has established significant scientific influence and continues to shape innovative, sustainable, and health-promoting solutions within the field of food engineering.

Profile: Google Scholar

Featured Publications

  • Moghadam, A., Ghorbani-HasanSaraei, A., Rafe, A., Fazeli, F., & Shahidi, S. A. (2025). Improving the functional properties of canola protein isolate through electrostatic coacervation with soluble fraction of Tragacanth gum. International Journal of Biological Macromolecules, 148, 103.

  • Dara, A., Feizy, J., Naji-Tabasi, S., Fooladi, E., & Rafe, A. (2023). Intensified extraction of anthocyanins from Berberis vulgaris L. by pulsed electric field, vacuum-cold plasma, and enzymatic pretreatments: Modeling and optimization. Chemical and Biological Technologies in Agriculture, 10(1), 93.

  • Jamshidian, H., & Rafe, A. (2024). Complex coacervate of wheat germ protein/high methoxy pectin in encapsulation of d-limonene. Chemical and Biological Technologies in Agriculture, 11(1), 60.

  • Ardestani, F., Haghighi Asl, A., & Rafe, A. (2024). Characterization of caseinate-pectin complex coacervates as a carrier for delivery and controlled-release of saffron extract. Chemical and Biological Technologies in Agriculture, 11(1), 118.

  • Ghorbani, A., Rafe, A., Hesarinejad, M. A., & Lorenzo, J. M. (2025). Impact of pH on the physicochemical, structural, and techno-functional properties of sesame protein isolate. Food Science & Nutrition, 13(1), e4760.

Michal Haindl | Visual Texture Inpainting | Best Researcher Award

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Prof. Dr. Michal Haindl | Visual Texture Inpainting | Best Researcher Award

Institute of Information Theory and Automation of the Czech Acadaemy of Sciences | Czech Republic

Prof. Dr. Michal Haindl is a leading Czech researcher recognized internationally for his extensive contributions to pattern recognition, texture analysis, material appearance modeling, and computational imaging, with a prolific body of work spanning more than two decades and over 130 publications. As a pioneer in advanced texture modeling, he has developed influential methods such as Bidirectional Texture Function (BTF) models, multispectral and 3D causal random field texture representations, anisotropic BRDF modeling, and rotationally invariant textural features that have shaped modern approaches in computer vision. His research addresses fundamental challenges in texture fidelity, similarity criteria, segmentation, scale and illumination invariance, and material recognition, providing robust frameworks widely applied in remote sensing, biomedical imaging, cultural heritage restoration, forestry classification, and disease detection. Prof. Dr. Haindl has also significantly advanced unsupervised learning and benchmarking for image segmentation, contributing datasets, evaluation metrics, and criteria that have become reference standards in the field. His work on medical imaging—including mammogram enhancement, melanoma recognition, and disease survival modeling—reflects his interdisciplinary impact across health analytics and AI-driven diagnostic support. Additionally, he has contributed to computational methods for evaluating physical and rendered materials, transfer learning for texture models, and structural detection in archeology. Through sustained innovation, extensive collaborations, and consistent publication in high-impact journals and conferences, Prof. Dr. Michal Haindl has established himself as a foundational figure in texture-based pattern recognition and material appearance research, continuously driving forward the scientific understanding and practical applications of computational vision.

Profiles: Orcid | Scopus

Featured Publications

  • Haindl, M., & Mikes, S. (2023). Optimal activation function for anisotropic BRDF modeling. Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 1–9.

  • Mikes, S., & Haindl, M. (2022). Texture segmentation benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 1–15.

  • Vacha, P., & Haindl, M. (2023). Texture recognition under scale and illumination variations. Journal of Information and Telecommunication, 7(4), 1–14.

  • Remes, V., & Haindl, M. (2019). Bark recognition using novel rotationally invariant multispectral textural features. Pattern Recognition Letters, 128, 1–8.

  • Haindl, M. (2022). Bidirectional texture function modeling. In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging (pp. 1–15). Springer.

Nikolaos Varotsis | Technological Change | Best Researcher Award

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Dr. Nikolaos Varotsis | Technological Change | Best Researcher Award

Lonian University | Greece

Dr. Nikolaos Varotsis is a multidisciplinary researcher whose work spans tourism management, behavioral economics, and knowledge management, focusing on how individuals, tourists, and organizations make decisions under complex socio-economic and informational conditions. With 203 citations, an h-index of 8, and an i10-index of 8, his research output demonstrates strong and steadily growing academic influence in both tourism studies and behavioral sciences. His contributions advance understanding of tourist information search behavior, destination brand equity, cultural tourism development, and digital entrepreneurship within the tourism sector. Drawing from behavioral economics and social simulation methodologies, Dr. Nikolaos Varotsis investigates mental accounting, organizational motivation, tax behavior, and the interplay of psychological, economic, and social factors influencing tax planning and tax compliance. His work also provides significant insights into telecommuting performance, work–family conflict, and public sector attitudes during the COVID-19 era, along with fiscal foresight models, shadow economy reduction strategies, and e-payment institutionalization. In tourism research, he has produced influential studies on information service management, wedding tourism decision motives, alternative tourism models for island destinations, and quality standards in hospitality services. His publications appear in respected journals such as Cogent Business & Management, Journal of Convention & Event Tourism, Nonlinear Dynamics Psychology and Life Sciences, Journal of Economic Structures, Digital Policy, Regulation and Governance, and Theoretical Economics Letters. Dr. Nikolaos Varotsis diverse research interests continue to evolve around tourism behavior, social simulations, organizational change, knowledge management, and the integration of behavioral insights into tourism innovation and public administration.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Varotsis, N., & Mylonas, N. (2024). A systematic literature review on information service management and information-seeking behavior in tourism. Cogent Business & Management, 11(1), 2385731.

  • Mylonas, N., Varotsis, N., & Vozinidou, I.-M. (2024). Unveiling the relationship between travel decision motives and destination brand equity in wedding tourism. Journal of Convention & Event Tourism, 25(2), 233–248.

  • Kontogeorgis, G., & Varotsis, N. (2022). Cultural tourism in developed island tourist destinations: The development of an alternative tourism model in Corfu. Journal of Environmental Management and Tourism, 13(2), 490–503.

  • Varotsis, N. (2022). A fiscal policy foresight tax model, shadow economy reduction, and e-payment institutionalization as a result of knowledge management. Theoretical Economics Letters, 12(6), 1710–1728.

  • Varotsis, N. (2022). Exploring the influence of telework on work performance in public services: Experiences during the COVID-19 pandemic. Digital Policy, Regulation and Governance, 24(3), 248–263.

Lijuan Wu | Power Device | Best Researcher Award

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Mrs. Lijuan Wu | Power Device | Best Researcher Award

Changsha University of Science and Technology | China

Mrs. Lijuan Wu is a distinguished researcher specializing in power semiconductor devices and power integrated circuits, with a strong focus on the design, simulation, and optimization of SiC and GaN-based electronic components such as MOSFETs, HEMTs, and IGBTs. Her work emphasizes enhancing energy efficiency, switching controllability, and device reliability for next-generation semiconductor applications. With 69 research publications, 317 citations, and an h-index of 9, she has made impactful contributions through innovative designs, including Double-RESURF SiC MOSFETs and self-clamped P-shield trench MOSFETs. Mrs. Wu has successfully led 10 funded research projects, including one supported by the National Natural Science Foundation of China, and multiple provincial and industry collaborations, bridging theoretical modeling with applied semiconductor technology. She has published 24 first-author papers in high-impact journals such as IEEE Transactions on Electron Devices and Microelectronics Journal, and authored the national monograph Optimization Technology for Charge Field in Heterogeneous Voltage-Resistant Layers of Power Semiconductors. Additionally, she has filed 12 invention patents, with 2 granted, showcasing her commitment to innovation and technology transfer. Her academic influence extends to mentoring graduate research, guiding numerous students in publishing high-quality papers, and contributing to the advancement of semiconductor device education and research. Through her sustained scholarly excellence and leadership in semiconductor innovation, Mrs. Wu continues to make significant strides toward energy-efficient and reliable power electronics.

Profile: Scopus

Featured Publications

  • Wu, L., et al. (2025). Simulation study of a 1200V 4H–SiC lateral MOSFETs with Double-RESURFs technology for reducing saturation current. Micro and Nanostructures.

  • Wu, L., et al. (2025). The ESD robustness of Schottky-gate p-GaN HEMT under different states. IEEE Transactions on Electron Devices.

  • Wu, L., et al. (2024). Self-clamped P-shield 4H-SiC trench MOSFET for low turn-off loss and suppress switching oscillation. Microelectronics Journal, 142, 106901.

  • Wu, L., et al. (2024). A novel 4H–SiC IGBT with double gate PMOS for improving the switch controllability and FBSOA. Microelectronics Journal, 141, 106820.

  • Wu, L., et al. (2024). Study on the hydrogen effect and interface/border traps of a depletion-mode AlGaN/GaN high-electron-mobility transistor with a SiNx gate dielectric at different temperatures. Micromachines, 15(2), 301.

Sukumar Letchmunan | Computer Science | Best Researcher Award

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Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.

Ivica Kopriva | Pattern Recognition | Best Researcher Award

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Dr. Ivica Kopriva | Pattern Recognition | Best Researcher Award

Rudjer Boskovich Institute | Croatia

Dr. Ivica Kopriva is a distinguished Senior Scientist at the Ruđer Bošković Institute, Croatia, internationally recognized for his pioneering contributions to signal processing, machine learning, and blind source separation. Dr. Ivica Kopriva’s interdisciplinary research integrates statistical learning, low-rank sparse modeling, and nonlinear decomposition methods to address complex challenges in biomedical imaging, remote sensing, and hyperspectral data analysis. His highly influential publications, including “Multi-view Low-Rank Sparse Subspace Clustering” (Pattern Recognition, 2018) and “l₀-Motivated Low-Rank Sparse Subspace Clustering” (IEEE Transactions on Cybernetics, 2018), have significantly advanced subspace clustering and unsupervised learning, with the former ranked among the top 1% of highly cited papers in Engineering. Dr. Ivica Kopriva’s research further encompasses image co-segmentation, tumor detection, and signal demixing, contributing to innovations in medical imaging and AI-based diagnostics. With over 2,546 citations, an h-index of 21, and an i10-index of 50, Dr. Ivica Kopriva’s scholarly impact extends across multiple disciplines of computational and biomedical sciences. His exceptional achievements have been recognized through numerous awards, including the State Award of the Republic of Croatia for Scientific Achievement (2009), multiple Director’s Awards for Scientific Excellence (2010–2021), and MICCAI Outstanding Reviewer Awards. As a Senior Member of IEEE and OSA and an affiliated faculty member at Virginia Commonwealth University, Dr. Ivica Kopriva continues to contribute extensively to international scientific communities, shaping the global research landscape in computational imaging, data-driven signal analysis, and intelligent biomedical systems.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Brbić, M., & Kopriva, I. (2018). Multi-view low-rank sparse subspace clustering. Pattern Recognition, 73, 247–258.

  • Huang, T. M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: Supervised, semi-supervised, and unsupervised learning. Springer Berlin Heidelberg.

  • Ju, W., Xiang, D., Zhang, B., Wang, L., Kopriva, I., & Chen, X. (2015). Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Transactions on Image Processing, 24(12), 5854–5867.

  • Brbić, M., & Kopriva, I. (2020). l₀-Motivated low-rank sparse subspace clustering. IEEE Transactions on Cybernetics, 50(4), 1711–1725.

  • Tolić, D., Antulov-Fantulin, N., & Kopriva, I. (2018). A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering. Pattern Recognition, 82, 40–55.

Di Fan | AI for Education | Best Researcher Award

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Dr. Di Fan | AI for Education | Best Researcher Award

Northeastern University | China

Dr. Di Fan, a prominent researcher at Northeastern University, Shenyang, specializes in big data mining and recommendation systems, with a strong focus on artificial intelligence applications in education. Her research advances data-driven behavior modeling, interpretability analysis, and the construction of personalized learning environments through AI and large language models (LLMs). As a core member of several National Natural Science Foundation of China key projects, she has contributed to studies on learning portrait technology and interactive educational systems. Dr. Fan leads the 2025 Youth Artificial Intelligence Education Project, developing dynamic assessment methods for digital literacy using generative AI. She holds multiple invention patents in knowledge tracking and AI-driven recommendation methods and has played a significant role in formulating local standards for industrial software and cloud computing skills training. Her publications span reputed venues such as JCST, IEEE ISPA, APWeb, and Bench2024, reflecting expertise in explainable AI and multitask learning. Recognized through numerous national awards, including the CCF Outstanding Communicator and National Scholarship honors, Dr. Fan also serves on several executive committees of the China Computer Federation and international editorial boards, contributing actively to advancing AI education, data science, and public technology outreach.

Profile: Orcid

Featured Publications

  • Fan, D., Zhang, T., Li, F., Wu, W., Yu, Y., & Yu, G. (2025). Research on multidimensional evaluation technology of teachers’ digital literacy for LLM as a judge. In Proceedings of Bench2024 (Book chapter). Springer. https://doi.org/10.1007/978-981-96-6310-1_7

  • Fan, D., & Li, F. (2024). A survey of static and temporal explainable methods and their applications in knowledge tracing. Journal of Computer Science and Technology (JCST), CCF-B category.

  • Fan, D., Zhang, T., & Yu, G. (2024). An LLM augmentation and multitask learning-based recommendation model for MOOCs. In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), CCF-C category. IEEE.

  • Fan, D., Wu, W., Yu, Y., & Zhang, T. (2024). ProteinMM: Adaptive multi-view and task-grouped evolutionary learning for prediction of protein structural features. In Proceedings of the Asia-Pacific Web Conference (APWeb), CCF-C category.

Gustavo Arroyo Figueroa | Big Data Architecture | Best Researcher Award

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Dr. Gustavo Arroyo Figueroa | Big Data Architecture | Best Researcher Award

National Institute of Electricity and Clean Energy | Mexico

Dr. Gustavo Arroyo-Figueroa is a distinguished Mexican computer scientist and applied artificial intelligence researcher whose work bridges the domains of intelligent systems, data analytics, and smart grid technologies. He earned his Ph.D. in Computer Science from the Monterrey Institute of Technology and currently serves as Head of Information Technologies Research at the Instituto Nacional de Electricidad y Energías Limpias (INEEL) in Cuernavaca, Mexico. Over his career, he has contributed significantly to the application of machine learning, data science, and big data analytics in power systems, focusing on automation, intelligent control, diagnostics, prediction, and forecasting within energy infrastructures. His research explores Bayesian networks, temporal reasoning, and artificial intelligence methods for fault detection and predictive maintenance in complex industrial systems. Dr. Arroyo-Figueroa has authored influential publications such as A Temporal Bayesian Network for Diagnosis and Prediction, Virtual Reality Training System for Maintenance and Operation of High-Voltage Overhead Power Lines, and Advanced Control Algorithms for Steam Temperature Regulation of Thermal Power Plants, which demonstrate his interdisciplinary expertise combining AI, virtual reality, and control engineering. His recent work also investigates renewable energy integration and the role of data-driven analytics in smart grid optimization. Recognized as a National Researcher by Mexico’s National System of Researchers (SNI), he is a member of the Mexican Society of Artificial Intelligence (SMIA), Academia Mexicana de Computación (AMEXCOMP), and the international CIGRE Study Committee D2, where he actively contributes to research on information systems and telecommunications in the power sector. In 2022, he was honored with the CIGRE Technical Council Award for his outstanding contributions to artificial intelligence applications in the energy industry, underscoring his leadership and commitment to advancing intelligent technologies for sustainable and resilient power systems. His research impact is reflected in over 1,712 citations, an h-index of 24, and an i10-index of 38, highlighting his sustained influence in the fields of artificial intelligence and energy informatics.

Profile: Google Scholar | Orcid | Scopus

Featured Publications

  • García, A. A., Bobadilla, I. G., Figueroa, G. A., Ramírez, M. P., & Román, J. M. (2016). Virtual reality training system for maintenance and operation of high-voltage overhead power lines. Virtual Reality, 20(1), 27–40.

  • Arroyo-Figueroa, G., & Sucar, L. E. (2013). A temporal Bayesian network for diagnosis and prediction. arXiv preprint arXiv:1301.6675.

  • Sánchez-López, A., Arroyo-Figueroa, G., & Villavicencio-Ramírez, A. (2004). Advanced control algorithms for steam temperature regulation of thermal power plants. International Journal of Electrical Power & Energy Systems, 26(10), 779–785.

  • Pérez-Ramírez, M., Arroyo-Figueroa, G., & Ayala, A. (2021). The use of a virtual reality training system to improve technical skill in the maintenance of live-line power distribution networks. Interactive Learning Environments, 29(4), 527–544.

  • Arroyo-Figueroa, G., Ruiz-Aguilar, G. M. L., Cuevas-Rodríguez, G., & others. (2011). Cotton fabric dyeing with cochineal extract: Influence of mordant concentration. Coloration Technology, 127(1), 39–46.

Teodoro Cassola | Spatial Databases | Best Researcher Award

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Dr. Teodoro Cassola | Spatial Databases | Best Researcher Award

Schlumberger | Netherlands

Dr. Teodoro Cassola is a distinguished geoscientist and petroleum systems expert currently affiliated with Schlumberger, specializing in structural geology, basin modeling, and petroleum geology. With 68 citations, an h-index of 5, and an i10-index of 2, his research contributions demonstrate growing influence in the fields of basin evolution, geothermal exploration, and subsurface energy systems. Trained at ETH Zurich, Dr. Cassola has built a strong academic and professional foundation that bridges advanced geological modeling with practical energy applications. His research focuses on understanding the interplay between tectonics, sedimentation, and thermal processes to predict the distribution and behavior of hydrocarbons and geothermal resources. Among his most cited works are “A Basin Thermal Modelling Approach to Mitigate Geothermal Energy Exploration Risks: The St. Gallen Case Study” (Geothermics, 2020) and “Structural Restoration and Basin Modelling of the Central Apennine Orogen/Foredeep/Foreland System” (Marine and Petroleum Geology, 2021), both of which significantly advanced the understanding of subsurface processes affecting exploration risks. His basin-scale modeling across Central Italy and the Adriatic Sea has provided critical insights into petroleum system development, hydrocarbon migration, and structural restoration. Recent works, including studies on CO₂ storage in the Baltic Sea (Applied Sciences, 2025) and geothermal drilling risk mitigation in hydrocarbon-bearing zones (International Journal of Coal Geology, 2025), illustrate his commitment to leveraging petroleum geology expertise for energy transition and carbon management. Earlier investigations into forearc basin mechanics and Neogene vertical tectonics in Anatolia and the Mediterranean have further enriched the geoscientific understanding of crustal deformation and basin dynamics. Overall, Dr. Cassola’s interdisciplinary research integrates structural, thermal, and geochemical modeling to enhance exploration efficiency and sustainability, reflecting a career devoted to advancing geothermal and petroleum system science for a low-carbon energy future.

Profile: Google Scholar

Featured Publications

  1. Omodeo-Salé, S., Eruteya, O. E., Cassola, T., Baniasad, A., & Moscariello, A. (2020). A basin thermal modelling approach to mitigate geothermal energy exploration risks: The St. Gallen case study (eastern Switzerland). Geothermics, 87, 101876.

  2. D’Ambrosio, A., Lipparini, L., Bigi, S., Cassola, T., Bambridge, V. R., Derks, J. F., & Moscariello, A. (2021). Structural restoration and basin modelling of the Central Apennine orogen/foredeep/foreland system: New insights on the regional petroleum system. Marine and Petroleum Geology, 127, 104948.

  3. Ruggieri, R., Trippetta, F., Cassola, T., & Petracchini, L. (2022). Basin modeling constrains source rock position and dimension in the Burano-Bolognano petroleum system (Central Italy). Journal of Asian Earth Sciences, 240, 105436.

  4. Lipparini, L., D’Ambrosio, A., Trippetta, F., Bigi, S., Derks, J. F., Bambridge, V. R., & Cassola, T. (2021). A new regional petroleum systems model for Central Italy and the central Adriatic Sea supported by basin modelling and an analysis of hydrocarbon occurrences. Journal of Petroleum Geology, 44(4), 461–485.

  5. Fernández-Blanco, D., Mannu, U., Cassola, T., Bertotti, G., & Willett, S. D. (2021). Sedimentation and viscosity controls on forearc high growth. Basin Research, 33(2), 1384–1406.