Yang Han | Computer Science | Best Researcher Award

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Dr. Yang Han | Computer Science | Best Researcher Award

Associate Researcher at Tianjin University, China

Yang Han is an emerging researcher with a strong academic background in mathematics, having completed both his Master’s and PhD at Nankai University, followed by a research position at Tianjin University. His work bridges mathematical theory and practical applications in engineering, focusing on areas such as topological data analysis, signal processing, and intelligent fault diagnosis. In recent years, he has published extensively in high-impact journals like IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals, and presented at reputable international conferences such as IEEE PESGM and ACPEE. His interdisciplinary research is marked by innovation and relevance, especially in appliance identification, load forecasting, and fault detection using advanced mathematical tools. Though early in his research career, Yang has demonstrated strong potential and a clear trajectory of growth. His dedication, academic rigor, and collaborative approach position him as a promising candidate for the Best Researcher Award.

🔹Professional Profile 

Google Scholar
ORCID Profile 

🏆Strengths for the Award

Yang Han demonstrates a highly impressive academic and research trajectory. With a strong foundation in mathematics from Nankai University, progressing through a Master’s and PhD (2015–2023), and currently holding an associate researcher position at Tianjin University, he shows continuity and growth in academic rigor. His research spans interdisciplinary areas, merging topological data analysis, signal processing, machine learning, and fault diagnosis—fields of significant importance in both academia and industry. Notably, his recent publications in high-impact journals such as IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals reflect both quality and innovation. Additionally, his contributions to top-tier conferences like IEEE PESGM and ACPEE signal strong peer recognition. The combination of applied AI techniques and deep mathematical theory shows versatility, a rare and commendable strength for a young researcher.

Areas for Improvement

While the publication record is strong and growing, most of the impactful work is very recent (primarily in 2024–2025), indicating that Yang Han is in the early stages of building a long-term research profile. Sustained contributions over a longer timeline will better establish him as a leading authority. Another point of improvement would be to take on more lead or sole authorship roles in future publications, as many current works are collaborative with shared credit, which can make it harder to isolate individual impact. Additionally, while his interdisciplinary work is a strength, expanding his network internationally through collaborations beyond China and participating in global research programs could enhance the visibility and influence of his work.

Conclusion

Yang Han is a highly promising and impactful early-career researcher with a unique blend of mathematical depth and applied AI-driven engineering. His recent output demonstrates a clear upward trajectory, both in productivity and innovation. While there is room to further solidify his independent research identity and global presence, his current achievements strongly support his candidacy for the Best Researcher Award. Given his solid grounding, interdisciplinary focus, and growing impact, he is indeed a suitable and deserving nominee for this recognition.

🎓Education

Yang Han began his academic journey at Nankai University, a prestigious institution known for mathematical excellence. From 2015 to 2018, he completed his Master’s degree at the School of Mathematical Sciences and LPMC, focusing on advanced mathematical theories and computational techniques. His strong academic performance and deep interest in topology, algebra, and their applications led him to continue his research as a PhD student in the same department from 2019 to 2023. During his doctoral studies, he expanded his expertise into applied mathematics and began to explore connections with engineering systems and data-driven problem solving. His doctoral research provided the foundation for his transition into interdisciplinary areas such as topological data analysis and graph signal processing. His time at Nankai University was marked by academic growth, critical thinking, and active participation in scholarly research. This rigorous educational background prepared him for a successful research career bridging mathematics and electrical engineering.

💼Experience

Yang Han currently holds the position of Associate Researcher at the School of Electrical and Information Engineering, Tianjin University. Since assuming this role in 2023, he has actively contributed to research in intelligent systems, signal processing, and data analytics. Before this, he spent nearly a decade at Nankai University, where he completed his Master’s and PhD studies, engaging in teaching support and foundational research. His experience spans a variety of projects focused on non-intrusive load monitoring, equipment fault diagnosis, and appliance identification—often leveraging advanced mathematical tools like topological data analysis and fast Fourier transforms. He has contributed to both national and international research collaborations, presented at prestigious conferences, and published in leading journals. His ability to blend abstract mathematical methods with real-world engineering challenges exemplifies his versatile experience. His role also involves mentoring junior researchers and contributing to interdisciplinary innovation at the intersection of mathematics, artificial intelligence, and electrical engineering.

🏆Awards and Honors

While formal individual awards are not explicitly listed in the available data, Yang Han’s growing list of high-impact publications and conference presentations serves as strong evidence of professional recognition. His work has been published in top-tier journals such as IEEE Transactions on Instrumentation and Measurement, Chaos, Solitons & Fractals, and Engineering Applications of Artificial Intelligence, reflecting a high level of peer recognition. He has also contributed to leading international conferences, including IEEE PESGM and the Asia Conference on Power and Electrical Engineering (ACPEE), where selection itself is a mark of merit. These platforms are known for their rigorous review processes, indicating that his work meets and often exceeds international research standards. Additionally, his involvement in collaborative, interdisciplinary projects and authorship in multiple papers shows that he is a valued team member in academic and industrial circles. As his career progresses, further formal awards and honors are likely to follow.

🔬 Research Focus on Computer Science

Yang Han’s research is centered at the intersection of applied mathematics, artificial intelligence, and electrical engineering. His primary focus lies in topological data analysis, signal processing, and machine learning techniques for complex system monitoring and fault detection. He has contributed significantly to non-intrusive load monitoring (NILM), using graph signal processing to identify energy consumption patterns without intrusive sensors. He also works on fault diagnosis through time-frequency analysis and the application of mathematical topology in real-world engineering systems. His innovative approach often involves transforming abstract mathematical concepts—such as Betti curves and topological invariants—into practical tools for appliance identification and power grid analysis. Furthermore, Yang Han is exploring adaptive methods for equipment behavior modeling and data-driven forecasting. This unique research blend offers both theoretical advancements and immediate practical value, demonstrating his ability to tackle emerging challenges in intelligent energy systems and industrial diagnostics with precision and depth.

📚 Publications Top Notes

  • Title: Energy dissipation analysis of elastic–plastic materials
    Authors: H Yang, SK Sinha, Y Feng, DB McCallen, B Jeremić
    Year: 2018
    Citations: 94

  • Title: Study on the mechanical behavior of sands using 3D discrete element method with realistic particle models
    Authors: WJ Xu, GY Liu, H Yang
    Year: 2020
    Citations: 46

  • Title: Nonlinear finite elements: Modeling and simulation of earthquakes, soils, structures and their interaction
    Authors: B Jeremić, Z Yang, Z Cheng, G Jie, N Tafazzoli, M Preisig, P Tasiopoulou, …
    Year: 2018
    Citations: 37

  • Title: The real-ESSI simulator system
    Authors: B Jeremić, G Jie, Z Cheng, N Tafazzoli, P Tasiopoulou, F Pisanò, JA Abell, …
    Year: 1988
    Citations: 35

  • Title: Study on the meso-structure development in direct shear tests of a granular material
    Authors: H Yang, WJ Xu, QC Sun, Y Feng
    Year: 2017
    Citations: 28

  • Title: Energy dissipation analysis for inelastic reinforced concrete and steel beam-columns
    Authors: H Yang, Y Feng, H Wang, B Jeremić
    Year: 2019
    Citations: 27

  • Title: Time domain intrusive probabilistic seismic risk analysis of nonlinear shear frame structure
    Authors: H Wang, F Wang, H Yang, Y Feng, J Bayless, NA Abrahamson, B Jeremić
    Year: 2020
    Citations: 22

  • Title: Seismic resonant metamaterials for the protection of an elastic-plastic SDOF system against vertically propagating seismic shear waves (SH) in nonlinear soil
    Authors: C Kanellopoulos, N Psycharis, H Yang, B Jeremić, I Anastasopoulos, …
    Year: 2022
    Citations: 21

  • Title: Energy dissipation in solids due to material inelasticity, viscous coupling, and algorithmic damping
    Authors: H Yang, H Wang, Y Feng, F Wang, B Jeremić
    Year: 2019
    Citations: 20

  • Title: 3-d non-linear modeling and its effects in earthquake soil-structure interaction
    Authors: SK Sinha, Y Feng, H Yang, H Wang, B Jeremic
    Year: 2017
    Citations: 19

  • Title: Plastic-energy dissipation in pressure-dependent materials
    Authors: H Yang, H Wang, Y Feng, B Jeremić
    Year: 2020
    Citations: 18

  • Title: Relationship between multifunctionality and rural sustainable development: Insights from 129 counties of the Sichuan Province, China
    Authors: X Li, J Liu, J Jia, H Yang
    Year: 2022
    Citations: 17

  • Title: Modeling and simulation of earthquake soil structure interaction excited by inclined seismic waves
    Authors: H Wang, H Yang, Y Feng, B Jeremić
    Year: 2021
    Citations: 17

  • Title: An energy-based analysis framework for soil structure interaction systems
    Authors: H Yang, H Wang, B Jeremić
    Year: 2022
    Citations: 14

  • Title: A robust and efficient federated learning algorithm against adaptive model poisoning attacks
    Authors: H Yang, D Gu, J He
    Year: 2024
    Citations: 11

Milind Cherukuri | Computer Science | Young Researcher Award

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Mr. Milind Cherukuri | Computer Science | Young Researcher Award

Salesforce Business Analyst & Administrator at University of North Texas, United States

Milind Cherukuri is a dynamic early-career researcher and technologist with a strong foundation in artificial intelligence, machine learning, and software engineering. With a Master’s in Computer Science from the University of North Texas, he has applied his expertise across leading organizations such as Caris Life Sciences, Amazon, and Infor. His research spans sentiment analysis, AI safety, LLM prompt engineering, and image segmentation, resulting in five peer-reviewed publications and presentations at major conferences like IEEE AI Summit and EEET 2024. Milind has a proven ability to translate research into real-world impact, particularly in healthcare, where he optimized clinical systems through AI-driven automation and data integration. Recognized as a Senior Member of IEEE in 2025, he actively contributes to the research community through peer review and technical leadership. His innovative mindset, technical depth, and cross-domain contributions position him as a strong candidate for the Young Researcher Award.

Professional Profile

Google Scholar

Education

Milind Cherukuri holds a Master’s degree in Computer Science from the University of North Texas, where he deepened his expertise in artificial intelligence, data science, and advanced software systems. Prior to that, he earned his Bachelor’s degree in Computer Science from SRM University, Chennai, India. His academic journey reflects a consistent focus on technical excellence, with coursework and projects covering machine learning, sentiment analysis, and cloud computing. During his graduate studies, Milind engaged in applied research initiatives and honed his skills in experimental design, statistical analysis, and academic writing. He leveraged these experiences to produce scholarly work and effectively bridge theory with practice. His education provided a strong foundation for multidisciplinary research, particularly in AI-driven applications across healthcare and enterprise environments. The blend of technical depth and research exposure during his formative academic years has directly influenced his ability to contribute meaningfully to both industrial innovation and scientific advancement.

Professional Experience

Milind Cherukuri’s professional journey spans prominent roles at Caris Life Sciences, Amazon, and Infor, reflecting a robust blend of research, software development, and systems integration experience. At Caris Life Sciences, he currently serves as a Salesforce Business Analyst and Administrator, where he leads automation, healthcare data integration, and clinical research optimizations. His work has directly impacted clinical decision-making by aligning technology with operational and regulatory needs. At Amazon, he developed scalable microservices, optimized APIs, and applied AI insights to enhance customer experience and personalization. Prior to that, at Infor in India, Milind supported legacy modernization and contributed to internal research on sentiment analysis and recommendation systems. Across these roles, he demonstrated an ability to innovate at scale while contributing to internal research pipelines and tool development. His hands-on experience across cloud platforms, AI tools, and enterprise software showcases a rare ability to move seamlessly between engineering execution and applied research.

Research Interest

Milind Cherukuri’s research interests lie at the intersection of artificial intelligence, machine learning, sentiment analysis, and safe AI deployment. He is passionate about building explainable, reliable, and application-driven AI systems that serve real-world domains such as healthcare, e-commerce, and cloud ecosystems. His work focuses on areas like multi-dimensional emotion representation, AI safety frameworks for large language models, and optimization techniques for prompt engineering. Milind is particularly interested in how AI can be made more context-aware, ethically responsible, and efficient when integrated into critical infrastructure. His research explores both the theoretical underpinnings of AI algorithms and their translation into user-centric applications. He uses tools such as TensorFlow, scikit-learn, Databricks, and Keras for prototyping and experimentation. Milind’s commitment to conducting reproducible and impactful research is evident through his multiple peer-reviewed publications and active participation in academic peer review and conference presentations.

Award and Honor

Milind Cherukuri has received several accolades that underscore his excellence in both research and professional performance. In 2025, he was elevated to the grade of Senior Member of IEEE, recognizing his significant contributions to engineering and AI research at a relatively early stage in his career. He has authored five peer-reviewed publications across reputable venues and conferences, including IEEE AI Summit and EEET 2024. His work has been cited in discussions on AI safety and ethics, especially regarding GPT-5 development strategies. Within industry roles, Milind earned recognition for developing fault-tolerant systems at Amazon and for improving automation workflows at Caris Life Sciences, boosting operational efficiency by over 30%. He has also contributed as a peer reviewer for research journals, enhancing his engagement with the broader scientific community. These honors reflect a balanced profile of innovation, leadership, and commitment to advancing technology responsibly and effectively.

Conclusion

Milind Cherukuri embodies the qualities of a forward-thinking, multidisciplinary researcher who bridges the worlds of academia and industry with exceptional skill. His educational foundation, professional achievements, and focused research trajectory demonstrate a rare combination of depth and adaptability. From developing scalable software at Amazon to integrating AI solutions in clinical workflows at Caris Life Sciences, he has consistently shown the ability to convert research insights into real-world impact. Milind’s publications, IEEE recognition, and conference engagements highlight his dedication to advancing AI in safe, ethical, and application-driven ways. His involvement in peer review and technical documentation further signals his readiness to contribute to and shape the global research landscape. With a passion for innovation, a track record of scholarly contributions, and strong industry credibility, Milind stands out as a compelling candidate for honors such as the Young Researcher Award, and is poised for continued impact in the field of computer science and artificial intelligence.

Publications Top Notes

  • Title: Comparing Image Segmentation Algorithms
    Author: M. Cherukuri
    Year: 2024
    Citations: 3

  • Title: Cost, Complexity, and Efficacy of Prompt Engineering Techniques for Large Language Models
    Author: M. Cherukuri
    Year: 2025
    Citations: 1

  • Title: WebChecker: A Versatile EVL Plugin for Validating HTML Pages with Bootstrap Frameworks
    Author: M. Cherukuri
    Year: 2025
    Citations: 1

  • Title: Advancing AI Safely: Frameworks and Strategies for the Development of GPT-5 and Beyond
    Author: M. Cherukuri
    Year: 2025
    Citations: 1

  • Title: Exploring Multi-Dimensional Sentiment Analysis: A Study on Emotion Representation Structures and Prediction Models
    Author: M. Cherukuri
    Year: 2024

Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

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Mr. Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

Senior Manager, Data Engineering at Procore Technologies, United States

Shishir Tewari is a seasoned technology leader with over 19 years of experience driving innovation in data engineering, data warehousing, and analytics across top-tier organizations such as Google, Amazon, Morgan Stanley, and Microsoft. He currently leads strategic data initiatives at Procore Technologies, where he has spearheaded the development of AI/ML-driven platforms, cloud migrations, and real-time analytics systems. Known for his expertise in building scalable, high-performance data solutions, Shishir has successfully led global engineering teams and transformed complex data ecosystems on AWS, GCP, and Databricks. His technical vision, operational excellence, and commitment to data quality and governance have consistently delivered measurable business value. Shishir’s continuous pursuit of innovation and deep cross-functional leadership make him a standout contributor in the technology landscape. With a strong foundation in data science, cloud architecture, and team mentorship, he exemplifies the qualities of a forward-thinking, impact-driven technology leader worthy of recognition.

Professional Profile 

Google Scholar

Education

Shishir Tewari holds a Bachelor of Technology in Information Technology from U.P.T.U., India, graduating in 2006. Demonstrating a commitment to lifelong learning and innovation, he further enhanced his credentials with a specialization in Data Science and Analytics from Rutgers University, New Jersey, in 2018–2019. This advanced academic training equipped him with modern analytical techniques, machine learning algorithms, and statistical modeling—skills that have been instrumental in his professional success. His educational background lays a strong foundation for his technical leadership, blending theoretical knowledge with real-world application. The combination of engineering fundamentals and data science expertise positions Shishir as a well-rounded technology leader who can bridge the gap between innovation and implementation in enterprise environments.

Professional Experience

Shishir Tewari brings over 19 years of robust experience across global technology firms, including Google, Amazon, Morgan Stanley, Microsoft, and currently, Procore Technologies. His career spans technical leadership, large-scale data architecture, and cloud-native platform innovation. At Google, he led a global team optimizing financial data pipelines and infrastructure. At Amazon, he designed high-performance advertising data systems, enabling substantial revenue impact. At Procore, he has driven major initiatives including AI/ML-powered data platforms and cloud migrations. His ability to manage large engineering teams, align data strategy with business goals, and optimize performance at scale reflects his leadership maturity. Shishir’s diverse experience across industries—finance, tech, construction, and advertising—gives him a unique, cross-sector perspective on data-driven transformation.

Research Interest

Shishir Tewari’s research interests lie at the intersection of big data engineering, AI/ML-driven analytics, and cloud computing. He is particularly passionate about optimizing large-scale data systems for performance, governance, and real-time decision-making. With practical expertise in cloud platforms like AWS, GCP, and Databricks, his focus is on leveraging modern data stacks and open-source technologies to power next-generation analytics and automation. He is also interested in the application of machine learning for master data management, anomaly detection, and predictive modeling within business intelligence ecosystems. While not rooted in academic publishing, his work consistently applies research principles to solve real-world business problems, delivering measurable impact. Future interests include exploring the integration of generative AI with enterprise data platforms and advancing data democratization through self-service analytics tools.

Award and Honor

While specific awards and honors are not listed in his profile, Shishir Tewari’s consistent elevation to senior technical and leadership roles in globally respected organizations serves as a testament to his excellence and recognition within the industry. Being entrusted with mission-critical projects at Google, Amazon, and Morgan Stanley speaks to his reliability, vision, and execution skills. His role in leading high-visibility initiatives such as financial data certification, AI/ML-driven analytics platforms, and major cloud migrations reflects the high degree of trust and credibility he commands. He has likely received internal accolades for his contributions to performance optimization, cost reduction, and innovation. A nomination for a Technology and Innovation Leadership Award would further formalize and honor his significant contributions to data-driven transformation and technological advancement in enterprise settings.

Conclusion

Shishir Tewari exemplifies the qualities of a forward-thinking technology leader, with deep expertise in data engineering, cloud architecture, and strategic innovation. His two-decade-long career reflects a commitment to excellence, from hands-on development to executive-level leadership. With advanced training in data science, he brings both theoretical rigor and practical vision to his work. His impactful roles at top-tier organizations demonstrate his ability to lead cross-functional teams, optimize large-scale systems, and implement transformative technologies. Passionate about leveraging AI/ML and cloud platforms to drive business value, Shishir’s professional journey is marked by continuous learning and measurable outcomes. He stands out as a prime candidate for recognition through a Technology and Innovation Leadership Award, not only for his technical contributions but also for his ability to inspire, mentor, and lead organizations into the future of data-driven innovation.

Publications Top Notes

  1. Title: AI Powered Data Governance – Ensuring Data Quality and Compliance in the Era of Big Data
    Authors: S. Tewari
    Year: 2025

  2. Title: Operationalizing Explainable AI in Business Intelligence: A Blueprint for Transparent Enterprise Analytics
    Authors: A. Chitnis, S. Tewari
    Year: 2024

  3. Title: AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty
    Authors: S. Tewari, A. Chitnis
    Year: 2024

  4. Title: Scalable Metadata Management in Data Lakes Using Machine Learning
    Authors: S. Tewari
    Year: 2023
    Citation: (Update needed)

  5. Title: AI-Powered Financial Forecasting: Enhancing Accuracy with Machine Learning in Enterprise System
    Authors: S. Tewari
    Year: 2023)

  6. Title: Detecting Data Drift and Ensuring Observability with Machine Learning Automation
    Authors: A. Chitnis, S. Tewari
    Year: 2022

  7. Title: Anomaly Detection in Large Scale Data Platforms with Machine Learning
    Authors: S. Tewari
    Year: 2022

  8. Title: Leveraging Graph Based Machine Learning to Analyze Complex Enterprise Data Relationships
    Authors: S. Tewari, A. Chitnis
    Year: 2021

Eduardo Coronel | Computer Science | Best Researcher Award

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Dr. Eduardo Coronel | Computer Science | Best Researcher Award

M.Sc. Eng. at Facultad Politécnica,  Paraguay

Eduardo Damián Coronel Torales, born on March 5, 1991, in Asunción, Paraguay, is a distinguished researcher and engineer specializing in electrical engineering, automation, and artificial intelligence applications. He has actively contributed to academia, industry, and international conferences, earning recognition for his innovative work in energy distribution and automation systems. His professional journey has taken him from academic research to practical implementations in one of the world’s largest hydroelectric plants, Itaipu Binacional. With a strong foundation in engineering and computational intelligence, Coronel Torales has made significant contributions to optimizing power distribution and developing automation solutions. His research extends beyond Paraguay, reaching international platforms and collaborations. He continues to push the boundaries of technology by integrating advanced optimization techniques, machine learning, and smart grid systems, positioning himself as a leader in his field.

Professional Profile

Education

Coronel Torales holds a Master’s degree in Electrical Engineering with an emphasis on Energy Systems Planning from the Facultad Politécnica of the Universidad Nacional del Este, obtained in 2021. His postgraduate research focused on optimizing power distribution using computational intelligence. He completed his undergraduate degree in Electronics Engineering with a specialization in Mechatronics at the Universidad Nacional de Asunción in 2017. During his academic career, he demonstrated exceptional analytical and problem-solving skills, engaging in multiple research projects related to automation, robotics, and energy systems. His academic journey reflects a strong commitment to technological advancements and interdisciplinary research. The combination of these degrees has provided him with a robust foundation in both theoretical and practical aspects of energy optimization, artificial intelligence, and industrial automation, equipping him with the expertise to tackle complex engineering challenges at both research and industrial levels.

Professional Experience

With extensive experience in academia and industry, Coronel Torales has worked as a research engineer at Itaipu Binacional, contributing to the modernization of automation systems. His expertise in failure analysis using PI tools and machine learning models has been instrumental in enhancing the reliability of large-scale energy infrastructure. He has also served as a postgraduate lecturer at the Universidad Nacional del Este, teaching heuristic optimization methods. Additionally, he has worked as an instructor at the Paraguay-Korea Advanced Technology Center (SNPP-KOICA), where he trained professionals in digital electronics and industrial automation. His work experience blends research, teaching, and industry applications, allowing him to bridge the gap between theory and practice. Through his diverse roles, he has been actively involved in developing intelligent systems, optimizing automation processes, and mentoring students and professionals in engineering disciplines.

Research Interests

Coronel Torales’ research interests lie at the intersection of power systems optimization, automation, and artificial intelligence. He has extensively explored the use of metaheuristic and multi-objective optimization techniques for enhancing the efficiency of electrical power distribution systems. His research also focuses on computer vision, machine learning, and control systems, particularly for applications in autonomous vehicles, industrial automation, and smart grids. Additionally, he is interested in the integration of AI-driven fault detection and predictive maintenance in large-scale energy infrastructures. His work contributes to improving the reliability and efficiency of energy management systems through data-driven solutions. By combining engineering principles with computational intelligence, he aims to develop sustainable and intelligent solutions for modern energy challenges. His forward-thinking research aligns with global trends in smart energy systems, IoT-enabled automation, and digital transformation in power distribution networks.

Awards and Honors

Coronel Torales has received international recognition for his research contributions, including multiple conference presentations at IEEE and other prestigious platforms. His work on remote-controlled switch optimization in power distribution systems has been published in IEEE Latin America Transactions and presented at international computing and engineering conferences such as CLEI, ICDIM, and INTERCON. He has been acknowledged for his contributions to automation failure analysis at Itaipu Binacional, influencing modernization decisions in one of the world’s largest hydroelectric plants. Additionally, his early research in autonomous vehicle navigation and fuzzy logic control earned him invitations to research symposiums in Argentina, Peru, South Korea, and the United States. His ability to translate research into practical applications has cemented his reputation as an emerging leader in electrical engineering and computational intelligence. His continued contributions are setting a benchmark for innovation in energy systems and industrial automation.

Conclusion

Eduardo Damián Coronel Torales has a strong research background with impactful contributions in energy systems optimization, automation, and AI applications. His publications, international recognition, and industry collaboration make him a strong candidate for the Best Researcher Award. However, to further strengthen his candidacy, he should aim for higher-impact journal publications, more independent research leadership, and broader contributions in emerging fields.

Publications Top Noted

  • Coronel, E., Barán, B., & Gardel, P. (2025). A Survey on Data Mining for Data-Driven Industrial Assets Maintenance Technologies. Journal article. DOI: 10.3390/technologies13020067.
  • Coronel Torales, E. D. (2024). Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making. CIGRE Paris Session 2024, Conference poster.
  • Coronel, E., Barán, B., & Gardel, P. (2022). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Meta-heuristic Approach. IEEE Latin America Transactions. DOI: 10.1109/TLA.2022.9675464.
  • Coronel Torales, E. D. (2021). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Multi-Objective Approach. 2021 XLVII Latin American Computing Conference (CLEI). DOI: 10.1109/clei53233.2021.9639970.
  • Coronel Torales, E. D. (2020). Optimización en la Ubicación de Seccionadores Tele-comandados en Sistemas de Distribución de Energía Eléctrica con enfoque meta-heurístico y soporte de decisión multi-criterio. Edited book. DOI: 10.13140/RG.2.2.32305.92002.
  • Coronel Torales, E. D. (2017). Estimación de disponibilidad de energía eléctrica de la Central Hidroeléctrica Itaipú y del crecimiento de la energía cedida al Paraguay hasta el 2023. Facultad Politécnica – Universidad Nacional del Este. DOI: 10.13140/RG.2.2.11838.79685.
  • Coronel Torales, E. D. (2015). Reliable navigation-path extraction system for an autonomous mobile vehicle. 2015 Tenth International Conference on Digital Information Management (ICDIM). DOI: 10.1109/icdim.2015.7381882.
  • Coronel Torales, E. D. (2015). PROTOTIPO DE VEHÍCULO AUTÓNOMO CON RNA Y VISIÓN POR COMPUTADORA. Simposio Argentino de Sistema Embebidos (SASE), Conference poster.
  • Coronel Torales, E. D. (2015). SISTEMA DE ALGORITMOS DE VISIÓN POR COMPUTADOR, APRENDIZAJE DE MÁQUINA, LOCALIZACIÓN Y NAVEGACIÓN DESARROLLADOS EN MATLAB, CON IMPLEMENTACIÓN EN VEHÍCULOS TERRESTRES PARA AUTO-CONDUCCIÓN. XXII Congreso Internacional de Ingeniería Eléctrica, Electrónica, Computación y Afines INTERCON 2015, Conference paper.
  • Coronel Torales, E. D. (2014). STABILITY COMMAND OF A TILT-ROTOR VEHICLE WITH A FUZZY LOGIC CONTROLLER. 3rd Conference of Computational Interdisciplinary Sciences – CCIS 2014, Conference poster. ISBN: 978-85-68888-00-1.
  • Coronel Torales, E. D. (2014). BALANCEADOR AERODINÁMICO CON LÓGICA DIFUSA. XXI Congreso Internacional de Ingeniería Electrónica, Eléctrica y Computación INTERCON 2014, Conference poster.