Rania Hamdani | Computer Science and Artificial Intelligence | Best Researcher Award

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Ms. Rania Hamdani | Computer Science and Artificial Intelligence | Best Researcher Award

AE3S at University of luxembourg, Luxembourg 

Rania Hamdani is a dynamic early-career research scientist specializing in software engineering, data management, and cloud architecture for Industry 5.0 applications. Currently based at the University of Luxembourg, she is engaged in advanced research on integrating heterogeneous data sources and optimizing decision-making in cloud-based systems. With a strong foundation in software development and operational research, Rania has already co-authored three research papers in Cloud-Edge AI and ontology-driven knowledge management. Her diverse technical skills span Python, Java, Docker, Kubernetes, and Azure DevOps, and she has gained international experience through roles in Luxembourg, Canada, France, and Tunisia. Passionate about both academic and applied innovation, she has contributed to multiple interdisciplinary projects in AI, human-computer interaction, and intelligent systems. Rania is also active in professional communities such as IEEE and youth science associations, reflecting her commitment to collaborative growth and scientific outreach.

Professional Profile 

🎓 Education Background

Rania Hamdani has a strong academic foundation rooted in engineering and scientific rigor. She earned her Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (ENSIT) between 2021 and 2024, where she specialized in Advanced Design, Service-Oriented Architecture, Object-Oriented Programming, Database Management, and Operational Research. Prior to that, she completed a Preparatory Cycle for Engineering Studies at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), focusing intensively on mathematics, physics, and core technology subjects—a rigorous program designed to prepare students for elite engineering schools. Rania also holds a Baccalaureate in Mathematics from Pioneer High School Bourguiba Tunis, where she graduated with distinction (Very Good) in 2019. This academic journey has laid a solid foundation for her multidisciplinary research and professional growth in software and data sciences.

💼 Professional Experience 

Rania Hamdani has developed a rich and diverse professional portfolio across academia and industry, with hands-on experience in software engineering, research, and cloud-based technologies. She is currently a Research Scientist at the University of Luxembourg (since November 2024), where she focuses on optimizing decision-making processes in cloud environments through advanced data integration techniques. Prior to this, she served as a Research Intern at the same institution (May to October 2024), contributing to projects in Ontology-Driven Knowledge Management and Cloud-Edge AI, resulting in three published papers. Alongside her academic work, Rania worked as a Part-Time Software Engineer at CareerBoosts in Canada (2021–2025), where she honed her skills in DevOps, data analysis, test automation, and backend development using tools like Python, Docker, and Kubernetes. Her earlier internships include roles at Qodexia (France), Sagemcom (Tunisia), and Tunisie Telecom, where she worked on smart recruitment platforms, employee management systems, and server monitoring tools using full-stack technologies such as SpringBoot, Angular, and PostgreSQL. This blend of research and industry experience positions Rania as a versatile and forward-thinking technology professional.

🔬 Research Interests of Rania Hamdani

Rania Hamdani’s research interests lie at the intersection of software engineering, operational research, data integration, and cloud-edge intelligence, with a strong orientation toward Industry 5.0 applications. She is particularly passionate about developing intelligent systems that enhance decision-making in cloud-based and distributed environments, leveraging AI, machine learning, and ontology-driven knowledge frameworks. Her work focuses on enabling seamless management of heterogeneous data sources, scalable architectures, and adaptive human-computer interaction (HCI) systems. Rania is also deeply engaged in exploring Cloud-Edge AI ecosystems, recommender systems, and automation pipelines using modern tools like Docker, Kubernetes, TensorFlow, and Neo4j. Her multidisciplinary approach reflects a vision for integrating research-driven insights with real-world industrial challenges, making her contributions both academically valuable and practically impactful.

🏅 Awards and Honors of Rania Hamdani

While still in the early stages of her research career, Rania Hamdani has demonstrated exceptional academic and technical promise. She graduated with a “Very Good” distinction in her Baccalaureate in Mathematics from the prestigious Pioneer High School Bourguiba in Tunis, reflecting her consistent academic excellence. Rania has also earned multiple professional certifications from Microsoft, including Azure Fundamentals, Azure Data Fundamentals, Azure AI Fundamentals, and Azure Security, Compliance, and Identity Fundamentals, showcasing her dedication to staying at the forefront of cloud and AI technologies. Though formal research awards or honors are not yet listed, her early publications, research contributions, and international internships highlight a trajectory poised for future recognition in both academic and industry spheres.

Publications Top Noted

Title: Adaptive human‑computer interaction for Industry 5.0: A novel concept, with comprehensive review and empirical validation
Authors: Rania Hamdani, Inès Chihi
Year: 2025
Journal: Computers in Industry (Volume 168)
DOI: 10.1016/j.compind.2025.104268

🧾 Conclusion

Rania Hamdani is highly suitable for the Best Emerging Researcher or Young Researcher Award category. She has excellent technical skills, promising early-stage research output, international exposure, and a forward-looking vision in areas like Industry 5.0, cloud-edge intelligence, and AI-based decision systems. While still building her publication track record and academic leadership, her current trajectory shows strong promise for future impactful contributions to scientific and industrial domains.

 

 

Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

Research Scholar at Indian Institute of Technology Kharagpur, India

Raviteja Sista is a dynamic and accomplished researcher specializing in Artificial Intelligence, Deep Learning, and Medical Image Analysis. Currently pursuing his Ph.D. at the Indian Institute of Technology Kharagpur with an outstanding GPA of 9.4, he is a recipient of the prestigious Prime Minister’s Research Fellowship. Raviteja holds an MSc in Signal Processing and Communications from the University of Edinburgh and a Bachelor’s in Electronics and Communication Engineering from Osmania University. His research focuses on developing AI-driven frameworks for surgical planning and outcome prediction, with notable contributions to multimodal graph-based learning and surgical video analysis. He has published extensively in top-tier journals such as Medical Image Analysis and Computers in Biology and Medicine, and has actively contributed to international AI challenges and symposia. His technical expertise, academic excellence, and dedication to solving real-world healthcare problems through AI make him a standout figure in the research community.

Professional Profile 

🎓 Education of Raviteja Sista

Raviteja Sista has pursued a stellar academic path marked by excellence and innovation. He is currently enrolled in a Ph.D. program at the Indian Institute of Technology Kharagpur, specializing in Artificial Intelligence at the Centre of Excellence, where he maintains an impressive GPA of 9.4/10. Prior to this, he earned his Master of Science in Signal Processing and Communications with Distinction from the University of Edinburgh (2019–2020). His foundational engineering training was completed with a Bachelor of Engineering in Electronics and Communication from M.V.S.R. Engineering College, affiliated with Osmania University, where he secured a remarkable 85.34%. Raviteja also boasts an outstanding academic record from his early years, achieving 94.6% in Intermediate studies at Narayana Junior College and a CGPA of 9.8/10 in Class X from Lotus National School, Hyderabad.

💼 Professional Experience of Raviteja Sista

Raviteja Sista has a well-rounded professional background that bridges academia, research, and industry. He is currently a Teaching Assistant at IIT Kharagpur, where he supports academic instruction in AI and deep learning. Over the years, he has held teaching roles at several institutions including SRKR Engineering College, CSI Wesley Institute of Technology, Assam Down Town University, and JNTU Kakinada, demonstrating his commitment to education and knowledge dissemination. Complementing his academic roles, Raviteja also gained valuable industry experience as an Associate Software Developer Intern at Accenture Solutions Pvt. Ltd. and through multiple internships at Defence Research and Development Laboratory (DRDL), Hyderabad. His professional journey reflects a strong blend of research, software development, and teaching expertise, all anchored in the field of artificial intelligence and signal processing.

🔬 Research Interests of Raviteja Sista

Raviteja Sista’s research interests lie at the intersection of artificial intelligence and healthcare, with a strong focus on applying deep learning techniques to complex real-world problems. His core areas of interest include Deep Learning, Medical Image Analysis, Digital Signal Processing, Image Processing, Artificial Intelligence, and Design of Algorithms. He is particularly passionate about developing AI-powered systems for surgical planning and automation, leveraging multimodal data, graph neural networks, and computer vision. His work aims to enhance patient safety, improve clinical outcomes, and drive innovation in intelligent medical systems. Raviteja’s commitment to impactful, interdisciplinary research is evident in his projects and publications, which bridge technical depth with healthcare relevance.

🏅 Awards and Honors of Raviteja Sista

Raviteja Sista has been recognized with several prestigious awards and honors that highlight his academic brilliance and research potential. Most notably, he was awarded the Prime Minister’s Research Fellowship (PMRF) in 2022, one of India’s most esteemed research fellowships supporting exceptional doctoral scholars. He also earned a Certificate of Merit for completing the “Advanced Certification in Artificial Intelligence and Machine Learning” from IIIT Hyderabad in 2019. Additionally, Raviteja demonstrated national-level academic excellence by ranking in the Top 3% among over 1 lakh candidates in GATE 2019, a highly competitive examination for engineering graduates in India. These accolades reflect his consistent pursuit of excellence and his growing reputation as a promising researcher in the field of artificial intelligence.

🧾 Conclusion 

Sista Raviteja stands out as a highly qualified, technically accomplished, and visionary researcher in AI for healthcare. With strong academic credentials, impactful projects, respected publications, and active involvement in the scientific community, he demonstrates clear potential for leadership in scientific research.Despite minor areas of potential growth in independent authorship and translational work, his contributions already meet and, in some cases, exceed the typical benchmarks for the Best Researcher Award.

📚 Publications Top Noted

  1. Title: Deep neural hashing for content-based medical image retrieval: A survey
    Authors: A. Manna, R. Sista, D. Sheet
    Journal: Computers in Biology and Medicine, Volume 196, Article 110547
    Year: 2025
    Citations: –
  2. Title: Artificial Intelligence (AI)–Based Model for Prediction of Adversity Outcome Following Laparoscopic Cholecystectomy—a Preliminary Report
    Authors: R. Agrawal, S. Hossain, H. Bisht, R. Sista, P.P. Chakrabarti, D. Sheet, U. De
    Journal: Indian Journal of Surgery, Volume 87 (1), Pages 52–59
    Year: 2025
    Citations: 1
  3. Title: Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation
    Authors: A. Srivastava, R. Sista, P.P. Chakrabarti, D. Sheet
    Conference: Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP)
    Year: 2024
    Citations: –
  4. Title: SimCol3D—3D reconstruction during colonoscopy challenge
    Authors: A. Rau, S. Bano, Y. Jin, P. Azagra, J. Morlana, R. Kader, E. Sanderson, …, R. Sista
    Journal: Medical Image Analysis, Volume 96, Article 103195
    Year: 2024
    Citations: 16
  5. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, K. Yuan, …, R. Sista
    Journal: Medical Image Analysis, Volume 89, Article 102888
    Year: 2023
    Citations: 29
  6. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, T. Xia, F. Jia, …, R. Sista
    Journal: Medical Image Analysis, Volume 86, Article 102803
    Year: 2023
    Citations: 61
  7. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, …, R. Sista
    Repository: arXiv, arXiv:2302.06294
    Year: 2023
    Citations: –
  8. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: R. Sista, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP 2022
    Year: 2022
    Citations: 1
  9. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, …, R. Sista
    Repository: arXiv, arXiv:2204.04746
    Year: 2022
    Citations: 1
  10. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: S. Raviteja, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP
    Year: 2022
    Citations: –
  11. Title: Challenges of Decomposing Tools in Surgical Scenes Through Disentangling The Latent Representations
    Authors: S.L. Gorantla, R. Sista, A. Srivastava, U. De, P.P. Chakrabarti, D. Sheet
    Workshop: ICLR Workshop on Challenges in Applied Deep Learning (ICBNB)
    Year: 2025 (Accepted)
    Citations: –

 

Shuai Cao | Computer Science and Artificial Intelligence | Best Researcher Award

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Shuai Cao | Computer Science and Artificial Intelligence | Best Researcher Award

Dr Shuai Cao, School of Automation, Wuhan University of Technology, China

Dr. Shuai Cao is a dynamic researcher in the field of Computational Intelligence, currently pursuing graduate studies at Kunming University of Science and Technology and engaging in joint research at the Guangdong Academy of Sciences. With a focus on enhancing meta-heuristic algorithms, Dr. Cao has contributed significantly to engineering optimization, especially in AGV path planning and offset printing machine design. He is the mind behind the innovative Piranha Foraging Optimization Algorithm (PFOA) and co-author of several impactful SCI/EI publications. His expertise in algorithm improvement, machine learning, and pattern recognition is reflected through funded projects and hands-on roles in top research institutions like the South China Intelligent Robot Innovation Institute. With a remarkable blend of theoretical insight and practical application, Dr. Cao is a promising candidate for the Best Researcher Award, embodying academic rigor and real-world impact.

Publication Profile 

Orcid

Education

Dr. Shuai Cao’s academic journey began at Baotou Rare Earth High-tech No. 1 High School (2014–2017), where he laid a strong foundation in the sciences. He pursued his undergraduate degree in Mechanical and Electronic Engineering at Chongqing University of Humanities, Science and Technology (2017–2021), gaining critical insights into systems design and robotics. Since 2021, he has been a postgraduate student in Electronic Information at Kunming University of Science and Technology, further sharpening his expertise in computational theory and algorithmic systems. Complementing his studies, Dr. Cao has been engaged in a joint training program at the Intelligent Manufacturing Institute of the Guangdong Academy of Sciences since 2022. His coursework includes meta-heuristic algorithms, machine learning, digital signal processing, and pattern recognition, all of which feed directly into his research in Computational Intelligence and engineering optimization. His interdisciplinary background empowers him to tackle complex problems with innovative solutions.

Experience

Dr. Shuai Cao has held impactful roles in prestigious research institutions. From May 2022 to July 2023, he worked at the Intelligent Manufacturing Institute of the Guangdong Academy of Sciences, where he conducted advanced research on AGV handling robots. This included applying improved intelligent algorithms for path planning and optimization scheduling—work closely aligned with his master’s thesis. Since July 2023, he has been with the South China Intelligent Robot Innovation Institute, applying swarm intelligence methods to optimize the structure of high-speed multi-color offset printing machines. Dr. Cao’s work integrates theoretical research with industrial application, setting a benchmark for practical relevance. His involvement in key science and innovation projects also reflects his growing leadership in the field. From optimization algorithms to real-world robotic systems, Dr. Cao’s hands-on approach is shaping the future of intelligent manufacturing.

Awards and Honors

Dr. Shuai Cao has earned distinguished recognition in both academic and research circles for his innovative contributions to engineering optimization. As a lead researcher on multiple government-funded projects—including “Research and Application of Intelligent Scheduling of Mobile Collaborative Robot Clusters for Intelligent Manufacturing” (Project Code: 2130218003022) and the “Foshan Science and Technology Innovation Team Project” (Project Code: FS0AA-KJ919-4402-0060)—he has demonstrated expertise in bridging theory with practical industrial solutions. His pioneering research has been published in high-impact SCI and EI journals and conferences, such as IEEE ACCESS and the International Conference on Robotics and Automation Engineering (ICRAE). A highlight of his work is the development of the Piranha Foraging Optimization Algorithm (PFOA), which has garnered considerable attention in the optimization community for its novelty and effectiveness. Dr. Cao’s sustained dedication to cutting-edge innovation, along with his leadership in collaborative, cross-disciplinary projects, makes him a compelling nominee for the Best Researcher Award.

Research Focus

Dr. Shuai Cao’s research is centered on Computational Intelligence, specifically the improvement and engineering application of swarm intelligence algorithms. His work addresses key challenges in traditional optimization methods, such as premature convergence, low population diversity, and slow optimization speeds. He has successfully designed algorithms that overcome these limitations, notably the Piranha Foraging Optimization Algorithm (PFOA). His research extends to practical applications like automated guided vehicle (AGV) path planning, scheduling in smart factories, and mechanical structure optimization for high-speed printing systems. Through interdisciplinary methods, he combines machine learning, pattern recognition, and digital signal processing to bring theoretical advancements into real-world manufacturing challenges. With a clear aim of enhancing intelligent manufacturing systems, his research contributes to both academic knowledge and industrial innovation. His growing body of work reflects originality, technical rigor, and a strong alignment with modern engineering demands.

Publication Top Notes

Research on the Design and Application of Multi-Port Energy Routers

 

Ebrahim Farrokh | Engineering and Technology | Best Researcher Award

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Ebrahim Farrokh | Engineering and Technology | Best Researcher Award

Assoc. Prof. Dr Ebrahim Farrokh, Amirkabir University of Technology, Iran

Assoc. Prof. Dr. Ebrahim Farrokh is a distinguished expert in rock mechanics and mining engineering, serving as the Head of Rock Mechanics and Mining Engineering at Amirkabir University of Technology. With a career spanning academia and industry, he specializes in tunnel boring machines (TBMs), underground excavation, and rock stability analysis. He has played a key role in major tunneling projects, providing expertise on TBM operations, rock fragmentation, and ground control. His research has led to numerous influential publications, advancing TBM performance prediction and tunnel design methodologies. Alongside his academic role, he consults for Tunnel Saz Machin Co. and has held managerial positions at Hyundai Engineering and Construction. Recognized with prestigious awards, including the Hardy Memorial Award and SME’s NAT Conference Scholarship, his contributions continue to shape the field of mining engineering. His work combines theoretical advancements with practical applications, ensuring safer and more efficient underground construction projects. 🚆💡

Publication Profile

Google Scholoar

Education

  • Ph.D. in Mining Engineering, Penn State University (2009-2012) 🏗️
    Dr. Farrokh earned his Ph.D. at Penn State University, focusing on TBM performance evaluation, advance rate prediction, and rock behavior analysis. His research contributed to innovative methodologies for assessing TBM cutter wear and ground stability.

  • M.Sc. in Mining Engineering, Tehran University (2001-2004) ⛏️
    During his master’s studies, he specialized in underground excavation, tunnel stability, and mine planning. His thesis examined rock fragmentation techniques and their applications in mechanized tunneling.

  • B.Sc. in Mining Engineering, Yazd University (1997-2001) 🌍
    He completed his undergraduate degree at Yazd University, gaining foundational knowledge in rock mechanics, mineral extraction, and geotechnical engineering. His early research explored TBM operational parameters and ground convergence in tunneling projects.

Experience

  • Associate Professor & Head, Rock Mechanics & Mining Engineering, Amirkabir University of Technology (2018-present) 🎓
    Leads research and academic initiatives in TBMs, tunnel stability, and underground mining.

  • Consultant, Tunnel Saz Machin Co. (2018-present) 🏗️
    Provides technical expertise in TBM operations, ground support, and excavation efficiency.

  • TBM Specialist & Manager, Hyundai Engineering & Construction (2013-2017) 🚜
    Managed TBM operations in major tunneling projects, optimizing performance and reducing downtime.

  • Research Assistant, Penn State University (2009-2012) 📊
    Conducted cutting-edge research on TBM cutter wear, penetration rate estimation, and tunnel convergence.

Awards and Honors 🏆

  • Outstanding Business Performance Award, Hyundai Engineering & Construction (2015) 🌟
    Recognized for leadership in TBM project execution and efficiency improvements.

  • Outstanding Research Award, Hyundai Engineering & Construction (2014, 2015) 🏅
    Awarded for contributions to TBM performance evaluation and geotechnical risk mitigation.

  • NAT Student Conference Scholarship Award, SME (2012) 🎓
    Acknowledged for excellence in mining engineering research and academic achievements.

  • Hardy Memorial Award, Penn State University (2010) 🏆
    Prestigious recognition for outstanding research contributions in mining and rock mechanics.

Research Focus

Dr. Farrokh’s research focuses on Tunnel Boring Machines (TBMs) 🚜, specializing in performance evaluation, advance rate prediction, and cutterhead design optimization. In Rock Mechanics 🏗️, he investigates rock properties, ground convergence, and stability assessment for underground projects. His work in Mining Engineering ⛏️ explores underground mining methods, rock fragmentation, and geotechnical risk analysis. By integrating theoretical advancements with real-world applications, Dr. Farrokh enhances the efficiency and safety of tunneling and mining operations. His research contributes to optimizing excavation processes, reducing operational risks, and advancing sustainable underground construction. 📊🔬

Publications Top Notes

  1. Tunnel Face Pressure Design and Control 📊 (2020)
  2. Concrete Segmental Lining: Procedure of Design, Production, and Erection of Segmental Lining in Mechanized Tunneling 📚 (2006)
  3. Study of Various Models for Estimation of Penetration Rate of Hard Rock TBMs 📊 (2012)
  4. Effect of Adverse Geological Conditions on TBM Operation in Ghomroud Tunnel Conveyance Project 🌎 (2009)
  5. Correlation of Tunnel Convergence with TBM Operational Parameters and Chip Size in the Ghomroud Tunnel, Iran 📊 (2008)
  6. A Discussion on Hard Rock TBM Cutter Wear and Cutterhead Intervention Interval Length Evaluation 💡 (2018)
  7. Evaluation of Ground Convergence and Squeezing Potential in the TBM-Driven Ghomroud Tunnel Project 🌎 (2006)
  8. Study of Utilization Factor and Advance Rate of Hard Rock TBMs 📊 (2013)
  9. A Study of Various Models Used in the Estimation of Advance Rates for Hard Rock TBMs 📊 (2020)
  10. Analysis of Unit Supporting Time and Support Installation Time for Open TBMs 🕒 (2020)

Banumathi S | Engineering and Technology | Women Researcher Award

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Banumathi S | Engineering and Technology | Women Researcher Award

Dr Banumathi S, M.Kumarasamy College of Engineering, India

Dr. Banumathi S. is a distinguished academic and researcher in Electrical Engineering ⚡, serving as a Professor at M. Kumarasamy College of Engineering with over 26 years of experience 🏫. She earned her Ph.D. from Anna University, Chennai 🎓. Dr. Banumathi has published 28 research papers 📚, including 11 in SCIE journals, and authored a Scopus-indexed book chapter. Her innovative contributions include 4 granted patents and 7 published patents 🧑‍🔬. A mentor for multiple funded projects 💡, she has guided Ph.D. scholars and contributed significantly to sustainable technologies 🌍 and advanced energy applications ⚙️.

Publication Profile

google scholar

Academic Achievements

Dr. Banumathi earned her Ph.D. in Electrical Engineering from Anna University in 2015 and holds an M.E. in Power Electronics and Drives with First Class distinction (82%) 🎓⚡. With a stellar career spanning over 26 years in academia 📚, she has made significant contributions to teaching, research, and mentoring young minds 🌟. Currently, she serves as a Professor at M. Kumarasamy College of Engineering 🏫, inspiring future engineers with her expertise in electrical systems and power electronics 🔌⚙️. Her dedication to education and innovation continues to shape the next generation of technology leaders. 💡📈

Research Excellence

Dr. Banumathi has made notable contributions to sustainable technologies, electrical insulation materials, and green energy solutions ⚡🌱. She has published 28 journal papers, including SCIE and Scopus-indexed articles 📚✨, and authored a book chapter 📖. Her research has been presented at prestigious IEEE conferences 🌐, showcasing her expertise in innovative energy solutions. With 138 citations on Scopus and 171 on Google Scholar 📊📈, her work has gained significant recognition and impact. Dr. Banumathi’s dedication to advancing sustainable technologies and green energy underscores her commitment to building a cleaner, more sustainable future 🌍💡.

Experience

Dr. Banumathi S., a distinguished educator, boasts a remarkable 28 years of teaching experience in electrical and electronics engineering. She currently serves as a Professor at M. Kumarasamy College of Engineering, Karur, since 2016, contributing 8.7 years of expertise 🌟📚. Previously, she was an Associate Professor and Head at King College of Technology, Namakkal, for 5.10 years 🏫✨. Her journey includes roles as an Assistant Professor at Selvam College of Technology (1.11 years), HOD/EEE at PGP Polytechnic College (7.2 years), and Lecturer at M.P.N.M.J Polytechnic (2.9 years) 🛠️🎓. Dr. Banumathi’s dedication inspires countless students 💡🙌.

Achievements

Dr. Banumathi S is a distinguished researcher with 15 journal publications, including SCI/E, WoS, and Scopus-indexed papers 📚✨. She holds Anna University Supervisor Recognition and has successfully guided one Ph.D. scholar, with two more in progress 🎓. Her innovative contributions include 4 granted patents and 7 published patents 💡📜. As a subject expert, she supported the AICTE MODROB-Rural Scheme (2019-2020) and mentors two MSME IdeaHackathon projects worth ₹12.75 lakhs 💼🚀. Dr. Banumathi authored a Scopus-indexed book chapter and a book 📖. She has organized national and international conferences 🌏, showcasing her dedication to academic excellence.

Research Focus

Dr. Banumathi’s research focuses on advanced materials and technologies for electrical and energy applications ⚡🔬. Her work spans photocatalytic materials, green synthesis of nanoparticles 🌱🧪, and innovative dielectric mediums, including vegetable oils 🌿🛢️ and biopolymers, for high-voltage insulation. She explores partial discharge characteristics, aging effects, and breakdown strengths of alternative insulating mediums, contributing to sustainable and efficient energy systems 🌍🔋. Her studies on supercapacitors and microgrid energy management highlight her multidisciplinary approach. Through cutting-edge investigations in material science and electrical engineering, Dr. Banumathi significantly advances sustainable energy solutions and dielectric innovations. 📈⚙️💡

Publication Top Notes

Rapid sun-light driven photocatalytic functions of 3D rGO/ZnO/Ag heterostructures via improved charge transfer kinetics

Green Synthesis of ZnMn2O4 Nanoparticles for Supercapacitor Applications

Analysis of Breakdown Strength and Physical Characteristics of Vegetable Oils for High Voltage Insulation Applications

Analysis of Partial Discharge Characteristics of Olive and Castor Oil as Dielectric Medium for HV Applications

Investigation of dielectric and mechanical properties of Lignocellulosic Rice Husk Fibril for high and medium voltage electrical insulation applications

Investigations on PD characteristics of vegetable oils for high voltage applications

Power Management For Pv-Wind And Hybrid Energy Storage Integrated Micro Grid