Chandra Sekhar Kolli | Data Science | Best Faculty Award

Dr. Chandra Sekhar Kolli | Data Science | Best Faculty Award

Associate Professor at Aditya University, India

Dr. Chandra Sekhar Kolli is a dedicated academician and researcher with extensive experience in computer science and engineering. With a strong commitment to excellence in teaching, research, and institutional development, he has contributed significantly to various domains, including machine learning, cybersecurity, and data science. His expertise spans over a decade in academic institutions, where he has mentored numerous students and engaged in innovative research initiatives. His contributions to academia have earned him recognition, including the prestigious Best Teacher Award.

Profile

Scopus

Education

Dr. Kolli holds a Ph.D. in Computer Science from GITAM (Deemed to be University), Visakhapatnam, obtained in 2021. He earned his Master of Engineering (M.E.) in Computer Science from HITS (Deemed to be University), Chennai, in 2011 with a CGPA of 7.99. His academic journey began with a Master of Computer Applications (MCA) from Andhra University in 2008, followed by a B.Sc. in Computer Science from the same institution in 2005. His foundational education includes an Intermediate MPC qualification from Govt. Junior College, West Godavari, and an SSC from ZPH School, West Godavari.

Professional Experience

Dr. Kolli has been serving as an Associate Professor at Aditya University, Surampalem, since November 2024. Prior to this, he worked as an Associate Professor at Shri Vishnu Engineering College for Women, Bhimavaram, from June 2021 to October 2024. His earlier roles include Assistant Professor positions at Koneru Lakshmaiah Education Foundation (Deemed to be University), Vijayawada (2017–2021), and Madanapalle Institute of Technology & Science, Madanapalle (2010–2017). Throughout his career, he has actively contributed to curriculum development, research supervision, and accreditation processes.

Research Interests

Dr. Kolli’s research interests include artificial intelligence, deep learning, federated learning, cybersecurity, data science, and cloud computing. His work primarily focuses on privacy-preserving AI models, fraud detection mechanisms, and optimization techniques for machine learning algorithms. His studies in these areas have resulted in impactful contributions to reputed international journals and conferences.

Awards and Recognitions

  • Best Teacher Award (2019-2020) – Recognized for academic excellence and student mentorship in the Computer Science and Engineering Department at Koneru Lakshmaiah Education Foundation.
  • WIPRO Certified Faculty (2020) – Successfully qualified in the Wipro Talent Next Global Certification program.

Selected Publications

Kolli, C. S., Seelamanthula, S., Reddy V, V.K. et al. (2024). Privacy-enhanced course recommendations through deep learning in federated learning environments. International Journal of Information Technology. Cited by 15 articles.

Kolli, C. S., Krishna Reddy, V. V., Reddy, T. S., et al. (2024). Deep learning-based privacy-preserving recommendations in federated learning. International Journal of General Systems. Cited by 12 articles.

Nalavade, J. E., Kolli, C. S., and Kumar, S. N. P. (2023). Deep embedded clustering with matrix factorization for collaborative recommendation. Expert Systems with Applications. Cited by 10 articles.

Tatireddy, S. R., Krishna Reddy, V. V., Vijaya Kumar Reddy, R., et al. (2023). SHBO-based U-Net for image segmentation and FSHBO-enabled DBN for classification. The Imaging Science Journal. Cited by 8 articles.

Kolli, C. S., and Tatavarthi, U. D. (2022). Hybrid optimization and deep learning for detecting fraud transactions in banking. International Journal of Information Security and Privacy. Cited by 9 articles.

Bhingarkar, S., Revathi, S. T., Kolli, C. S., et al. (2022). Optimization-enabled deep learning for malicious behavior detection in cloud computing. International Journal of Intelligent Robotics Applications. Cited by 7 articles.

Kolli, C. S., and Tatavarthi, U. D. (2021). Fraud detection in bank transactions using wrapper models and deep recurrent neural networks. Kybernetes. Cited by 6 articles.

Conclusion

Dr. Chandra Sekhar Kolli is a seasoned academician with a strong background in computer science, research, and professional mentorship. His contributions to AI, deep learning, and cybersecurity have significantly impacted academia and industry applications. With an unwavering commitment to research and teaching excellence, he continues to shape the future of AI-driven technological advancements. His extensive publication record and recognition in academia highlight his dedication to innovation and knowledge dissemination. His expertise makes him a valuable contributor to the field of computer science and engineering.

Liupeng Zhao | Data-Driven Decision Making | Best Researcher Award

Dr. Liupeng Zhao | Data-Driven Decision Making | Best Researcher Award

Lecturer at Jilin University, China

Liupeng Zhao is a distinguished researcher and lecturer at Jilin University, specializing in gas sensors and flexible electronics. His academic journey has been marked by significant contributions to the field of sensor technology, with a strong focus on the development of oxide gas sensors. His research endeavors have led to numerous publications in high-impact journals and have earned him recognition at international conferences. Through innovative research and collaborations, Zhao has been at the forefront of advancements in sensing materials, device fabrication, and system development, establishing himself as an emerging expert in his domain.

Profile

Google Scholar

Education

Zhao pursued his master’s and doctoral studies at Jilin University in the Advanced Sensing Technology Laboratory, where he developed expertise in oxide gas sensor fabrication, mechanisms, and applications. During his graduate studies, he honed his skills in materials design and modification, leading to the development of high-performance gas sensors. His academic training provided him with a strong foundation in sensor technologies, enabling him to explore new frontiers in flexible electronics and sensor arrays. His educational background has played a pivotal role in shaping his research trajectory and contributions to the field.

Experience

With a robust background in sensor technology, Zhao has actively participated in several national-level research projects, contributing to the development of novel gas sensing systems. He has played a crucial role in the design and optimization of sensing materials, focusing on enhancing sensitivity and selectivity. His experience extends to working with leading researchers and institutions, including collaborations with Professor TAN Swee Ching from the National University of Singapore and ongoing research with Professor Chen Jun from UCLA. His practical experience in sensor system development and deep knowledge of material properties have enabled him to push the boundaries of gas sensor applications.

Research Interests

Zhao’s research interests encompass gas sensors, flexible electronics, sensor arrays, density functional theory (DFT) calculations, and machine learning. His studies focus on understanding the mechanisms behind oxygen partial pressure effects on SnO₂ sensors, the development of tactile sensors, and smart gloves for gesture recognition. His interdisciplinary approach integrates material science, computational modeling, and artificial intelligence to enhance sensor performance. By leveraging advanced fabrication techniques and innovative materials, Zhao aims to improve sensor efficiency and reliability, making significant contributions to the field of electronic sensing technologies.

Awards

Zhao’s contributions to sensor technology have earned him notable accolades, including the Best Oral Presentation Award at the International Meeting on Chemical Sensors (IMCS). Additionally, he has been honored with the “Wiley China Excellent Author Program,” recognizing his outstanding research contributions. His recognition in these prestigious platforms highlights the impact of his work on the scientific community and the advancements he has brought to gas sensing technology. His achievements reflect his commitment to pushing the frontiers of research and developing cutting-edge sensor applications.

Publications

Zhao has published 42 SCI-indexed journal papers, demonstrating his research productivity and impact. Below are some of his key publications:

Zhao L., et al. (2023). “Enhanced Sensitivity of SnO₂-Based Gas Sensors via Oxygen Partial Pressure Control.” Advanced Functional Materials. Cited by 75.

Zhao L., et al. (2022). “Machine Learning-Assisted Optimization of Flexible Sensors.” ACS Sensors. Cited by 64.

Zhao L., et al. (2021). “Tactile Sensor Arrays for Smart Glove Applications.” Nano-Micro Letters. Cited by 58.

Zhao L., et al. (2020). “Gas Sensor Networks for Air Quality Monitoring.” InfoMat. Cited by 50.

Zhao L., et al. (2019). “Flexible Electronics for Wearable Gas Sensing.” ACS Sensors. Cited by 46.

Zhao L., et al. (2018). “DFT Analysis of Gas Sensor Materials.” Advanced Functional Materials. Cited by 41.

Zhao L., et al. (2017). “Nanostructured Metal Oxides for Sensing Applications.” ACS Sensors. Cited by 37.

Conclusion

Liupeng Zhao’s dedication to advancing gas sensor technology and flexible electronics has established him as a key contributor in his field. His research has led to significant developments in sensor materials, device fabrication, and system applications, with a strong emphasis on improving sensor performance through material engineering and computational modeling. His numerous publications and collaborations with top researchers have reinforced his standing in the scientific community. As he continues to explore new frontiers in sensing technologies, his work is poised to influence future advancements in smart and wearable sensor applications.