Ananya Kuri | Engineering | Best Researcher Award

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Ms. Ananya Kuri | Engineering | Best Researcher Award

Scientist | R&D Project Manager at Siemens AG, Germany

Ananya Kuri is an accomplished R&D Project Manager at Siemens AG, specializing in electrical power engineering and grid stability. With over 10 years of experience in the power systems sector, she has played a pivotal role in dynamic performance analysis, inverter-based resource modeling, and power grid optimization. Ananya holds a Ph.D. from FAU Erlangen (dissertation under review) and an M.Sc. in Electrical Power Engineering from RWTH Aachen University. She is known for her leadership in managing complex projects, mentoring teams, and collaborating with global customers. Her expertise lies in enhancing power system stability, modeling and analyzing power plants, and supporting grid compliance efforts. Ananya’s work spans across consulting, R&D, and training, with significant contributions to Siemens’ technology in power systems and microgrids. Her professional journey reflects a blend of innovation, technical excellence, and strong industry engagement, making her a respected figure in the energy sector.

Professional Profile

Education

Ananya Kuri’s academic credentials lay a solid foundation for her extensive career in power systems engineering. She holds a Ph.D. in Electrical Engineering from FAU Erlangen, where her dissertation is currently under review. Prior to this, she completed her M.Sc. in Electrical Power Engineering from RWTH Aachen University, one of Germany’s premier technical institutions. During her time at RWTH Aachen, Ananya developed a deep understanding of electrical power technologies and systems, which has been pivotal in her professional journey. Her B.Eng. in Electrical and Electronics Engineering from M.S. Ramaiah Institute of Technology in Bangalore, India, provided her with early insights into power systems, further shaping her technical expertise. Throughout her academic tenure, Ananya demonstrated a strong commitment to research, resulting in multiple published works and contributions to cutting-edge developments in the power systems domain, paving the way for her successful professional career.

Professional Experience

Ananya Kuri’s professional experience spans a decade of working with Siemens AG, where she has made significant contributions in both consulting and research roles. She began her career as a Senior Power Systems Consultant and Portfolio Element Owner in Siemens’ Digital Grid, focusing on transmission systems, inverter-based resources, and power grid stability. Her technical expertise was key in the modeling and analysis of various Siemens power systems products, including the Power Plant Controller and Microgrid Controller. Ananya has also held leadership roles as an R&D Project Manager, where she led projects like ENSURE Phase 3 for inverter-based resources and kurSyv for corrective system management in distribution networks. She has mentored teams, managed global consulting projects, and played an integral role in Siemens’ advancements in grid compliance, ensuring Siemens’ power systems meet the evolving needs of modern electrical grids. Her extensive work with international clients and R&D initiatives highlights her strong professional impact.

Research Interests

Ananya Kuri’s research interests lie primarily in the areas of power system stability, grid integration, and inverter-based technologies. Her work revolves around enhancing the dynamic performance of power grids, with a focus on transient stability, small-signal analysis, and frequency regulation. Ananya is particularly interested in the modeling and control of inverter-based resources, as these technologies are crucial in supporting the transition to renewable energy sources and the modernization of grid infrastructures. Her research also extends to the development of advanced control strategies for microgrids and power plants, aiming to improve grid stability and resilience. She is actively involved in R&D projects that address the operational challenges of integrating renewable energy into power systems, such as enhanced inverter control techniques. Ananya’s contributions to power system modeling, grid compliance studies, and dynamic simulations aim to drive innovations in power system operations and support the reliable and efficient operation of future grids.

Awards and Honors

Ananya Kuri’s outstanding contributions to the field of power systems engineering have earned her recognition within both the academic and professional communities. She has been actively involved in global research and development initiatives and has contributed to numerous successful consulting projects. Although specific awards are not mentioned, her leadership roles in industry-standard working groups like CIGRE and IEC, along with her involvement in over 35 working groups and 17+ published works, underscore her high standing in the industry. Ananya’s influence extends beyond her immediate work at Siemens, as she is recognized as a key member of international committees shaping the future of power system operations and standards. Her expertise in developing Siemens’ key products, such as the SICAM Power Plant Controller and Microgrid Controller, also highlights her significant contributions to the global energy sector. These honors and recognitions reflect her impact as a thought leader in electrical power engineering.

Conclusion

Ananya Kuri is highly suitable for the Best Researcher Award based on her extensive experience, leadership in R&D, technical expertise, and contributions to global research projects. Her work in inverter control strategies, grid stability, and model development for Siemens’ products directly addresses the challenges facing modern power systems. The only area for improvement would be completing her Ph.D. and further enhancing her public engagement. Overall, she represents the qualities of a forward-thinking researcher with significant industry impact.

Publications Top Noted

Title: Power Dispatch Capacity of a Grid-Forming Control Based on Phase Restoring Principle
Authors: A. Kuri, Ananya; R. Zurowski, Rainer; G. Mehlmann, Gert; M. Luther, Matthias
Journal: IEEE Systems Journal
Year: 2023
Citations: 3

 

Chung-Horng Lung | Engineering | Best Researcher Award

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Chung-Horng Lung | Engineering | Best Researcher Award

Full Professor at Carleton University, Canada

Dr. Chung-Horng Lung is a distinguished professor in the Department of Systems and Computer Engineering at Carleton University, Ottawa. With a career spanning over three decades in academia and industry, he has made significant contributions to software engineering, network security, and artificial intelligence. Recognized as one of the world’s top 2% most-cited researchers (Stanford-Elsevier, 2022 & 2023), his work has influenced various domains, including machine learning-based security systems, intelligent data processing, and network optimization. Prior to joining Carleton University, he held senior engineering positions at Nortel Networks, where he worked on software architecture, network traffic engineering, and MPLS-based communication technologies. His extensive research, mentorship, and interdisciplinary collaborations have earned him a reputation as a leading scholar in his field. Alongside his academic contributions, Dr. Lung is also a Professional Engineer (P.Eng.) in Ontario, further validating his expertise and impact in the engineering community.

Professional Profile

Education

Dr. Lung holds a Ph.D. in Computer Science and Engineering from Arizona State University, Tempe, earned in 1994. His journey in academia began with a Master’s degree in Computer Science and Engineering from the same institution in 1988, following a Bachelor’s degree in Computer Science and Engineering from Chung-Yuan Christian University, Taiwan, in 1982. His academic background provided him with a strong foundation in software engineering, network security, and intelligent computing. During his doctoral studies, he worked extensively on distributed systems and software engineering methodologies, laying the groundwork for his future research. His educational trajectory showcases a commitment to innovation and excellence, equipping him with the expertise needed to bridge academia and industry. Through continuous learning and research advancements, Dr. Lung has remained at the forefront of emerging technologies in computing and engineering.

Professional Experience

Dr. Lung has a rich professional background in both academia and industry. He is currently a Professor at Carleton University, where he has been a faculty member since 2001. Before becoming a full professor in 2015, he served as an Associate Professor in the same department. His industry experience includes senior roles at Nortel Networks, where he worked as a Senior Software Designer and Network Engineer on Optical Packet Interworking and MPLS-based Traffic Engineering. He was also a Senior Software Architecture Engineer at Nortel’s Software Engineering Analysis Lab (SEAL), contributing to critical advancements in software engineering and network technologies. Additionally, he has worked as an Instructor and Research Assistant at Arizona State University and a Software Engineer at Electronics Research & Service Organization in Taiwan. His diverse career path reflects his versatility and expertise in both theoretical and applied computing disciplines.

Research Interests

Dr. Lung’s research focuses on machine learning, cybersecurity, software engineering, and network optimization. His work in machine learning-based intrusion detection systems (IDS) has led to the development of AI-driven security solutions for SCADA and power systems. Additionally, his research on knowledge graphs and unstructured data processing has contributed to advancements in data-driven decision-making. His expertise extends to network traffic analysis, software reliability engineering, and intelligent data sampling, with applications in forest fire detection, industrial automation, and smart city infrastructures. His interdisciplinary approach has fostered collaborations with academic institutions, industry partners, and government agencies, ensuring that his research has real-world impact. By integrating AI, cybersecurity, and software engineering principles, Dr. Lung continues to explore innovative solutions to modern technological challenges.

Awards and Honors

Dr. Lung has received numerous accolades throughout his career, with his most notable recognition being listed among the world’s top 2% most-cited scholars (Stanford-Elsevier, 2022 & 2023). This honor reflects the global impact of his research and his contributions to computer science and engineering. Additionally, he is a registered Professional Engineer (P.Eng.) in Ontario, demonstrating his adherence to the highest professional standards in engineering. Over the years, he has received multiple best paper awards, research grants, and industry recognitions for his work in machine learning, cybersecurity, and network optimization. His mentorship of students and early-career researchers has also been acknowledged through teaching excellence awards and faculty recognitions. With a distinguished academic and professional career, Dr. Lung continues to push the boundaries of innovation in computing and engineering, solidifying his position as a leading researcher in the field.

Conclusion

Dr. Chung-Horng Lung is a highly qualified and impactful researcher, making significant contributions in Computer Science, Machine Learning, and Network Engineering. His strong publication record, industry experience, and citation impact make him a strong contender for the Best Researcher Award. Addressing minor gaps in funding details, patents, and international collaborations could further strengthen his case.

Publications Top Noted

📖 Journal Articles

1️⃣ In-Network Caching for ICN-Based IoT (ICN-IoT): A Comprehensive Survey 🏆

  • Author(s): Zhang, Z., Lung, C.-H., Wei, X., Chatterjee, S., Zhang, Z.
  • Year: 2023
  • Citations: 41 🔥
  • Published in: IEEE Internet of Things Journal

2️⃣ iCache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks 🧠

  • Author(s): Zhang, Z., Wei, X., Lung, C.-H., Zhao, Y.
  • Year: 2023
  • Citations: 17 📈
  • Published in: IEEE Internet of Things Journal

3️⃣ Knowledge Graph Generation and Application for Unstructured Data Using Data Processing Pipeline 🤖

  • Author(s): Sukumar, S.T., Lung, C.-H., Zaman, M., Panday, R.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE Access

🎤 Conference Papers

4️⃣ A Federated Learning Framework Based on Spatio-Temporal Agnostic Subsampling (STAS) for Forest Fire Prediction 🔥

  • Author(s): Mutakabbir, A., Lung, C.-H., Ajila, S.A., Sampalli, S., Ravichandran, T.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE COMPSAC 2024

5️⃣ Comparative Analysis of Real-Time Data Processing Architectures: Kafka versus MQTT Broker in IoT 📡

  • Author(s): Ho, C.L.D., Lung, C.-H., Mao, Z.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE ICEIB 2024

6️⃣ DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering – an Experiment Report 🛡️

  • Author(s): Marleau, S., Rahman, P., Lung, C.-H.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: IEEE ICEIB 2024

7️⃣ Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling 🔥📊

  • Author(s): Tavakoli, F., Naik, K., Zaman, M., Lung, C.-H., Ravichandran, T.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: International Conference on Agents and Artificial Intelligence

8️⃣ Forest Fire Prediction Using Multi-Source Deep Learning 🌲🔥

  • Author(s): Mutakabbir, A., Lung, C.-H., Ajila, S.A., Purcell, R., Sampalli, S.
  • Year: 2024
  • Citations: 0 (New Publication) 🚀
  • Published in: LNICST Conference Proceedings

9️⃣ A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction 🌍🔥

  • Author(s): Kaur, P., Naik, K., Purcell, R., Zaman, M., Mutakabbir, A.
  • Year: 2023
  • Citations: 1 📊
  • Published in: IEEE Big Data 2023

🔟 Unstructured Transportation Safety Board Findings Categorization Using the Knowledge Graph Pipeline 🚗📊

  • Author(s): Panday, R., Lung, C.-H.
  • Year: 2023
  • Citations: 1 🏆
  • Published in: IEEE Big Data 2023