Sandeep Belidhe | Engineering | Best Innovation Award

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Mr. Sandeep Belidhe | Engineering | Best Innovation Award

DevSecOps Engineer at Sparksoft Corp, United States

Sandeep Belidhe is a highly experienced IT professional with over 10.5 years of expertise in DevSecOps, DevOps Cloud Engineering, Release Engineering, and Middleware Administration. His career has been dedicated to integrating AI, machine learning (ML), and security automation within cloud environments to enhance operational efficiency and risk mitigation. Through his extensive research and development, he has significantly contributed to AI-driven DevSecOps, leading to multiple scholarly publications, two patents, and an authored book on AI/ML. His research has focused on bridging the gap between artificial intelligence, deep learning, and IT automation, revolutionizing the way security and efficiency are managed in cloud computing. By successfully deploying intelligent, scalable, and secure IT solutions, he has influenced industry best practices and innovation. Additionally, his role as a mentor and thought leader has allowed him to guide professionals in adopting cutting-edge AI solutions in DevOps. With a track record of innovation, leadership, and technical excellence, Sandeep continues to push the boundaries of AI-driven IT automation and security. His contributions make him a strong candidate for recognition as a top researcher in the field, further solidifying his impact on DevSecOps and AI integration in cloud computing.

Professional Profile

Education

Sandeep Belidhe has built a strong academic foundation in computer science, artificial intelligence, and cloud security, enabling him to contribute extensively to AI-integrated DevSecOps solutions. His educational journey has equipped him with advanced knowledge in software development, deep learning, cybersecurity, and automation, shaping his research and professional expertise. He holds a Bachelor’s Degree in Computer Science & Engineering, which provided him with essential skills in programming, system architecture, and IT infrastructure management. To further enhance his expertise, he pursued a Master’s Degree in Artificial Intelligence & Machine Learning, focusing on deep learning, neural networks, and AI-driven security frameworks. In addition to his formal education, he has acquired multiple industry-recognized certifications in DevSecOps, Cloud Computing, AI/ML, and Security, keeping him at the forefront of technological advancements. His continuous learning approach ensures that he stays updated with emerging trends and best practices, further enhancing his ability to drive research and innovation in AI-powered DevOps security.

Professional Experience

Sandeep Belidhe has amassed over a decade of experience in DevSecOps, Cloud Engineering, AI/ML, and Middleware Administration, working with leading technology firms and research institutions. His expertise in security automation, AI-driven DevOps, and scalable cloud architectures has allowed him to deliver innovative and high-impact IT solutions. Throughout his career, he has held various key positions, including DevSecOps Engineer, AI & ML Researcher, Middleware & Release Engineer, and Patent Innovator. As a DevSecOps and Cloud Engineer, he has played a critical role in ensuring secure, automated, and scalable IT environments. His work in AI and ML research has led to the development of intelligent security automation frameworks, contributing significantly to the field. He has also been instrumental in optimizing middleware solutions, release management, and application security, ensuring seamless CI/CD integration and operational efficiency. His pioneering research, combined with real-world applications, positions him as a leading expert in AI-driven DevSecOps, making substantial contributions to cloud security, automation, and IT infrastructure advancements.

Research Interest

Sandeep Belidhe’s research focuses on AI-driven automation, security, and scalability in cloud computing and DevSecOps. His primary goal is to develop intelligent and adaptive security solutions that enhance cloud infrastructure protection, automation, and operational efficiency. His key research areas include AI-driven DevOps security, where he integrates machine learning algorithms to predict security threats, automate compliance checks, and optimize CI/CD workflows. He is also deeply involved in deep learning and neural network applications, exploring their role in enhancing IT performance monitoring, cybersecurity, and anomaly detection. Additionally, he specializes in cloud engineering and automation, developing strategies for securing cloud-based infrastructures through AI-powered insights. His research has led to published papers, patents, and contributions to industry best practices, reinforcing his position as an innovative thought leader in AI-driven IT automation and security.

Research Skills

Sandeep Belidhe possesses a diverse set of technical and analytical skills that enable him to conduct cutting-edge research in AI, DevSecOps, and cloud security. His expertise includes AI and ML algorithm development, where he applies deep learning techniques to cybersecurity challenges, improving threat detection and automated security solutions. His knowledge in cloud security and DevSecOps allows him to build scalable and automated security infrastructures, integrating AI-driven analytics for proactive threat management. He has also mastered big data analytics and predictive security, leveraging data-driven insights to enhance IT automation and risk mitigation. Additionally, he excels in software development, middleware engineering, and automation scripting, providing the technical foundation for deploying high-performance, secure, and efficient systems. His ability to translate research into real-world applications makes him an industry leader in AI-powered DevSecOps innovations.

Awards and Honors

Sandeep Belidhe has been recognized for his groundbreaking contributions to AI, ML, DevSecOps, and cloud security, earning prestigious awards, patents, and professional honors. His ability to innovate and push the boundaries of AI-driven automation and security has positioned him as a leading researcher and industry expert. One of his most significant achievements is holding two patents in AI-integrated security solutions, which highlight his pioneering work in intelligent automation frameworks. Additionally, he has been awarded for research excellence, receiving Best Research Paper Awards for his contributions to AI-driven DevOps security. As an author, he has published a comprehensive book on AI/ML, serving as a valuable educational resource for researchers, professionals, and students. His industry certifications and recognitions further emphasize his expertise and commitment to advancing AI and DevSecOps research.

Conclusion

Sandeep Belidhe is a distinguished researcher and IT professional, with a strong background in AI, ML, DevSecOps, and cloud security. His 10.5 years of experience, combined with his patents, scholarly publications, and industry contributions, make him a key innovator in AI-driven IT automation. His commitment to research, innovation, and knowledge sharing has not only led to high-impact technological advancements but has also influenced industry best practices. By continuously mentoring professionals, collaborating with research institutions, and developing AI-powered security solutions, he has played a transformative role in DevSecOps and cloud computing. Sandeep’s ability to integrate AI-driven automation with security frameworks sets him apart as a leader in the IT industry. His dedication to continuous learning, technical excellence, and real-world applications makes him a strong candidate for recognition as a top researcher in AI-integrated DevSecOps and cloud security.

Publications Top Notes

  1. Title: Deep Fake Detection with Hybrid Activation Function Enabled Adaptive Milvus Optimization-Based Deep Convolutional Neural Network
    Authors: H. Mashetty, N. Erukulla, S. Belidhe, N. Jella, V. Reddy Pishati, B.K. Enesheti
    Year: 2025

  2. Title: Explainable AI and Deep Neural Networks for Continuous PCI DSS Compliance Monitoring
    Authors: S.K.D. Sandeep Belidhe, Phani Monogya Katikireddi
    Year: 2024

  3. Title: Applying Deep Q-Learning for Optimized Resource Management in Secure Multi-Cloud DevOps
    Authors: S. Belidhe
    Year: 2022

  4. Title: AI-Driven Governance for DevOps Compliance
    Authors: S. Belidhe
    Year: 2022

  5. Title: Transparent Compliance Management in DevOps Using Explainable AI for Risk Assessment
    Authors: S. Belidhe
    Year: 2022

  6. Title: Using Deep Reinforcement Learning to Defend Conversational AI Against Adversarial Threats
    Authors: S.K.D. Phani Monogya Katikireddi, Sandeep Belidhe
    Year: 2021

  7. Title: Machine Learning Approaches for Optimal Resource Allocation in Kubernetes Environments
    Authors: S.B. Sandeep Kumar Dasa, Phani Monogya Katikireddi
    Year: 2021

  8. Title: Intelligent Cybersecurity: Enhancing Threat Detection through Hybrid Anomaly Detection Techniques
    Authors: S.B. Phani Monogya Katikireddi, Sandeep Kumar Dasa
    Year: 2021

  9. Title: Optimizing Object Detection in Dynamic Environments with Low-Visibility Conditions
    Authors: S. Belidhe, S.K. Dasa, S. Jaini

Yuxin Ma | Engineering | Best Researcher Award

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Mr. Yuxin Ma | Engineering | Best Researcher Award

Master Degree Candidate at Shanghai Dianji University, China

Ma Yuxin is an emerging researcher in Electrical Engineering, currently pursuing a Master’s degree at Shanghai Dianji University. With a strong academic background and research focus on Permanent Magnet Synchronous Motor (PMSM) control, Ma has already contributed three research papers to international conferences and journals. Recognized for academic excellence, innovation, and technical proficiency, Ma has received multiple scholarships and awards, including the Shanghai “Science and Technology Star of Tomorrow” Creative Award. Alongside research, Ma has practical experience through an internship at Shanghai Electric Fuji Electric Power Technology Co., Ltd., where they are engaged in PMSM sensorless full-speed control projects. Proficient in MATLAB, AD, PSIM, and Keil, Ma has also earned a Siemens NX CAD Engineer Intermediate Qualification. These achievements reflect a commitment to advancing electrical engineering technologies through both theoretical and practical applications.

Professional Profile

Education

Ma Yuxin completed a Bachelor’s degree in Electrical Engineering from Shanghai Dianji University (2018-2022) with outstanding academic performance, earning multiple university scholarships. Currently, Ma is pursuing a Master’s degree in Electrical Engineering at the same institution (2023-present). During undergraduate studies, Ma actively participated in innovation and entrepreneurship projects, winning recognition for contributions to scientific research. The master’s research focuses on PMSM speed control, leading to three published papers in reputable journals and conferences. Academic achievements also include certification as a Siemens NX CAD Engineer and recognition in the Challenge Cup Shanghai University Science and Technology Competition. These educational experiences have provided a strong foundation in theoretical knowledge, research methodologies, and practical applications, preparing Ma for further advancements in electrical engineering research and development.

Professional Experience

Ma Yuxin is currently working as a Technical Research and Development Engineer at Shanghai Electric Fuji Electric Power Technology Co., Ltd. (2024-2025). This role involves conducting research and development on PMSM sensorless full-speed control projects and software testing experiments. During this position, Ma has gained hands-on experience in electrical system simulation, motor control optimization, and embedded system programming. Additionally, Ma’s university years included participation in competitive engineering projects and industry-relevant training programs, reinforcing both practical and theoretical expertise. This experience, combined with academic research, enables Ma to bridge the gap between academia and industry by applying research insights to real-world engineering challenges. The combination of research and industry exposure highlights Ma’s capability to innovate within electrical engineering and contribute to advancements in motor control technologies.

Research Interests

Ma Yuxin’s primary research interests lie in Permanent Magnet Synchronous Motor (PMSM) speed control, with a focus on sensorless full-speed control optimization. Other areas of interest include power electronics, motor drive systems, embedded control systems, and intelligent motor control using AI-based algorithms. Ma is also keen on exploring advanced control strategies for electric vehicles (EVs), renewable energy applications, and industrial automation. The integration of machine learning with motor control to enhance efficiency, reliability, and fault diagnosis is another potential research direction. By combining theoretical knowledge with experimental validation, Ma aims to contribute to the development of more efficient, robust, and cost-effective electrical motor control systems. These interests align with emerging trends in smart grid technologies, automation, and energy-efficient electrical systems, positioning Ma as a promising researcher in modern electrical engineering applications.

Research Skills

Ma Yuxin possesses strong research skills in electrical system modeling, simulation, and motor control algorithm development. Proficient in using MATLAB, PSIM, AD, and Keil for electrical simulations and control system design, Ma also has experience with embedded programming and software testing. Expertise extends to hardware implementation and real-time testing of PMSM control systems, ensuring research findings are practically applicable. Additionally, Ma is skilled in scientific writing and publishing, having successfully authored and published three research papers in reputable journals and conferences. Knowledge of data analysis, experimental design, and optimization techniques further strengthens Ma’s ability to conduct impactful research. These research skills, coupled with technical proficiency, provide a solid foundation for continued contributions to the field of electrical engineering and motor control technology.

Awards and Honors

Ma Yuxin has received numerous awards and honors for academic excellence, innovation, and research contributions. During undergraduate studies, Ma was recognized as an Outstanding Graduate of Shanghai and awarded multiple university scholarships for both academic performance and practical achievements. Additionally, Ma won the Creative Award in the 18th Shanghai “Science and Technology Star of Tomorrow” selection activity, highlighting innovation in scientific research. Another significant achievement includes securing second prize in the 17th “Challenge Cup” Shanghai University Science and Technology Competition, showcasing strong problem-solving and research capabilities. Further honors include the Siemens NX CAD Engineer Intermediate Qualification Certificate, demonstrating technical expertise. These achievements reflect Ma’s commitment to excellence in research, technical skill development, and innovative problem-solving, reinforcing their suitability for prestigious research awards.

Conclusion

Ma Yuxin is a promising researcher in electrical engineering, demonstrating strong academic performance, research productivity, and technical expertise. With three research papers published, awards in innovation competitions, and hands-on experience in PMSM control projects, Ma has a solid foundation for continued contributions to the field. However, further research in high-impact journals, international collaborations, and patent applications would strengthen the case for prestigious research awards. Participation in conferences, industrial projects, and interdisciplinary research could also enhance visibility in the academic community. Given Ma’s current trajectory, continued growth in these areas will position them as a leading researcher in electrical motor control and automation technologies.

Publications Top Notes

  1. Publication: Speed Control of PMSM Based on Series Lead Correction Doubly Fed Differential LADRC

    • Authors: Yuxin Ma
    • Year: 2025

  2. Publication: Research on PMSM Speed Control Based on Improved Super-Twisting Sliding Mode Active Disturbance Rejection Control

    • Authors: Yuxin Ma, Ziqi Lei, Pingping Gu, Xinpeng Feng, Wei Zhang, Chaohui Zhao
    • Year: 2024

 

Zhongwei Wu | Engineering | Best Researcher Award

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Dr. Zhongwei Wu | Engineering | Best Researcher Award

Lecturer at Yangtze University, China

Dr. Zhongwei Wu is a Lecturer in the College of Petroleum Engineering at Yangtze University. He specializes in geo-energy development with a focus on shale and tight oil reservoirs, CO₂ flooding and storage, and big data applications in energy systems. With over four years of professional experience, he has made significant contributions to hydraulic fracturing and proppant transport models, providing theoretical support for efficient oil and gas extraction. Dr. Wu has managed 18 research projects worth $810,000, authored 26 SCI-indexed journal papers, and holds 20 patents. His research outputs have been cited over 110 times in the last three years. His work is recognized for its practical applications and academic rigor, making him a promising figure in petroleum engineering.

Professional Profile

Education

Dr. Zhongwei Wu holds a Bachelor of Petroleum Engineering from Yangtze University (2009–2013) and a Master’s in Oil and Natural Gas Engineering from China University of Geosciences, Beijing (2013–2016). He completed his Ph.D. in Oil and Gas Field Development Engineering at China University of Petroleum (East China) in 2020, during which he was a visiting doctoral researcher at the University of Alberta (2018–2019). His academic journey reflects a commitment to mastering advanced concepts in petroleum engineering and geo-energy systems.

Professional Experience

Dr. Wu’s career spans academia and research, beginning as a Post-doctoral Fellow at China University of Petroleum (East China) from 2020 to 2022. In November 2022, he joined Yangtze University as a Lecturer in the College of Petroleum Engineering. Over his career, he has led groundbreaking studies on hydraulic fracturing and effective utilization methods in shale/tight oil reservoirs. His consultancy work includes 17 industry-sponsored projects, reflecting his ability to integrate research with real-world applications. Dr. Wu has also served as an editor, reviewer, and conference committee member, contributing to advancing the petroleum engineering field.

Research Interests

Dr. Wu’s research focuses on geo-energy development, particularly shale/tight oil reservoirs and carbon capture, utilization, and storage (CCUS). His interests include optimizing hydraulic fracturing techniques, CO₂ flooding for enhanced oil recovery, and leveraging big data technologies for energy systems. His innovative models on fracture-proppant dynamics and effective utilization range have practical implications for improving oil and gas production efficiency. His work bridges theoretical advancements and industrial applications, driving sustainable energy development.

Research Skills

Dr. Wu demonstrates expertise in advanced modeling and simulation techniques for hydraulic fracturing and CO₂ flooding. He is skilled in designing and conducting laboratory experiments to validate theoretical frameworks. His proficiency in data analysis and big data applications enhances his ability to optimize energy systems. Additionally, his experience managing multi-million-dollar research projects highlights his project management and collaborative skills, ensuring impactful outcomes in petroleum engineering research.

Awards and Honors

Dr. Wu has received recognition for his outstanding contributions to petroleum engineering. He holds one institutional award and has established a functional MoU with a collaborating university, emphasizing his commitment to collaborative research. With over 110 citations in three years and a growing H-index of 12, his work is gaining increasing recognition in academia and industry. His innovations, backed by 20 patents and numerous publications, reflect his leadership in advancing geo-energy development technologies.

Conclusion

Zhongwei Wu stands out as a promising researcher in the field of geo-energy development and CCUS. His expertise in shale/tight oil reservoirs, coupled with significant contributions through patents, publications, and industry projects, solidifies his position as a strong contender for the Best Researcher Award. By addressing areas of improvement, particularly in international collaborations and visibility at scientific forums, he can further strengthen his candidature and global impact.

Publication Top Notes

  1. Influence of reservoir heterogeneity on immiscible water-alternating-CO2 flooding: A case study”
    • Authors: Jia, P.; Cui, C.; Wu, Z.; Yan, D.
    • Year: 2024
    • Journal: Energy Geoscience
    • Volume/Issue: 5(3), Article 100272
    • Citations: 1
  2. “A novel method to determine the optimal threshold of SEM images”
    • Authors: Zhang, Z.; Cui, C.; Wu, Z.
    • Year: 2024
    • Journal: Marine and Petroleum Geology
    • Volume: 163, Article 106804
    • Citations: 1
  3. “Screening and field application of microbial-flooding activator systems”
    • Authors: Yao, X.; Gai, L.; Feng, Y.; Ma, J.; Wu, Z.
    • Year: 2024
    • Journal: Energy Geoscience
    • Volume/Issue: 5(2), Article 100240
  4. “Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM”
    • Authors: Cui, C.; Qian, Y.; Wu, Z.; Lu, S.; He, J.
    • Year: 2024
    • Journal: Energy Exploration and Exploitation
    • Volume/Issue: 42(2), pp. 444–461
    • Citations: 3
  5. “Simulation of the Microscopic Seepage Process of CO2 Storage in Saline Aquifers at the Pore Scale”
    • Authors: Cui, C.; Li, J.; Wu, Z.
    • Year: 2024
    • Journal: Energy and Fuels
    • Volume/Issue: 38(3), pp. 2084–2099
    • Citations: 2
  6. “Pressure Analysis of Vertical-Wells with the Hydraulic Fracturing Assisted Water Injection in Low-Permeability Hydrogen and Carbon Reservoirs”
    • Authors: Yu, Z.; Liu, S.; Tang, J.; Du, J.; Wu, Z.
    • Year: 2024
    • Journal: ACS Omega
  7. “The Imbibition Mechanism and the Calculation Method of Maximum Imbibition Length during the Hydraulic Fracturing”
    • Authors: Wu, Z.; Li, X.; Cui, C.; Wang, Y.; Trivedi, J.J.
    • Year: 2024
    • Journal: International Journal of Energy Research
    • Article: 8371615
    • Citations: 1
  8. “Shale Pore-Scale Numerical Simulation of Oil-Water Two-Phase Flow”
    • Authors: Qian, Y.; Cui, C.-Z.; Wu, Z.-W.; Sui, Y.-F.; Lu, S.-Q.-S.
    • Year: 2024
    • Book: Springer Series in Geomechanics and Geoengineering
    • Pages: 905–914
  9. “Optimization of cushion gas types and injection production parameters for underground hydrogen storage in aquifers”
    • Authors: Hao, Y.; Ren, K.; Cui, C.; Wu, Z.
    • Year: 2023
    • Journal: Energy Storage Science and Technology
    • Volume/Issue: 12(9), pp. 2881–2887
    • Citations: 1
  10. “An improved Eulerian scheme for calculating proppant transport in a field-scale fracture for slickwater treatment”
    • Authors: Sun, L.; Cui, C.; Wu, Z.; Trivedi, J.J.; Guevara, J.
    • Year: 2023
    • Journal: Geoenergy Science and Engineering
    • Volume: 227, Article 211866
    • Citations: 3