Sai Venkatesh Chilukoti | Computer Science | Best Researcher Award

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Mr. Sai Venkatesh Chilukoti | Computer Science | Best Researcher Award

Mr. Sai Venkatesh Chilukoti, University of Louisiana at Lafayette, United States

Sai Venkatesh Chilukoti is a dedicated researcher in Computer Engineering, currently pursuing a Ph.D. at the University of Louisiana at Lafayette under Dr. Xiali Hei. With a stellar academic record and a CGPA of 4.0, he specializes in Deep Learning, Network Security, and Cyber-Physical Systems. His research interests span Federated Learning, Differential Privacy, and Machine Learning applications in healthcare and security. Sai Venkatesh has contributed to multiple peer-reviewed journals and conferences, focusing on privacy-preserving AI and identity recognition. As a Research Assistant in the Wireless Embedded Device Security (WEDS) Lab, he has worked on cutting-edge projects integrating AI, privacy-enhancing techniques, and embedded security. With experience as a Teaching Assistant, he mentors students in Neural Networks, Python, and AI-related fields. His passion lies in translating research into real-world applications, particularly in medical imaging and cybersecurity. Sai is fluent in English, Hindi, and Telugu, and has strong technical skills in Python, PyTorch, and TensorFlow.

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Suitability for the Research for Best Researcher Award – Sai Venkatesh Chilukoti

Sai Venkatesh Chilukoti demonstrates an impressive academic and research portfolio, making him a strong contender for the Research for Best Researcher Award. Currently pursuing a Ph.D. in Computer Engineering at the University of Louisiana at Lafayette with a perfect 4.0/4.0 CGPA, he has developed expertise in deep learning, distributed computing, network security, and cyber-physical systems. His academic credentials are further strengthened by a solid foundation in electronics and communication engineering at the undergraduate level.

His research contributions are notable, particularly in privacy-preserving deep learning, federated learning, and medical AI applications. His work on diabetic retinopathy classification, gastrointestinal cancer prediction, and differential privacy models for healthcare data showcases both technical depth and real-world impact. His involvement in cutting-edge machine learning techniques, including LSTMs, Transformers, and convolutional networks, highlights his ability to innovate within the field. Furthermore, his research assistantship in Wireless Embedded Device Security (WEDS) Lab and role as a teaching assistant demonstrate both research rigor and mentorship capabilities.

🎓 Education 

Sai Venkatesh Chilukoti is currently pursuing a Ph.D. in Computer Engineering at the University of Louisiana at Lafayette (2021-2025, expected), under the supervision of Dr. Xiali Hei, with a perfect CGPA of 4.0. His coursework includes Deep Learning, Network Security, Distributed Computing, and Cyber-Physical Systems. His research focuses on privacy-preserving AI, federated learning, and deep learning model optimization.

He completed his B.Tech. in Electronics and Communication Engineering at Velagapudi Ramakrishna Siddhartha Engineering College (2017-2021), earning a CGPA of 8.53/10. His undergraduate studies encompassed AI, Python, Artificial Neural Networks, and Digital Signal Processing.

Throughout his education, Sai has actively engaged in research projects, including identity recognition using mmWave radar sensors, privacy-aware medical imaging, and deep learning applications in cybersecurity. His academic journey reflects a strong foundation in computational intelligence and a commitment to solving real-world challenges through innovative AI techniques.

💼 Professional Experience

Sai Venkatesh Chilukoti has extensive research and teaching experience, specializing in Deep Learning, Cybersecurity, and Federated Learning. As a Research Assistant at the Wireless Embedded Device Security (WEDS) Lab (2021-present), he has worked on privacy-preserving AI models, security solutions for embedded devices, and deep learning-based medical imaging applications. His work includes designing federated learning frameworks for decentralized AI and developing privacy-aware deep learning techniques.

As a Teaching Assistant for Neural Networks (2024-present), Sai mentors students in probability, calculus, and AI programming using PyTorch and Scikit-learn.

He has led numerous projects, such as statistical analysis of COVID-19 data, AI-driven financial forecasting, and deep learning applications in 3D printing quality control. His expertise extends to programming in Python, C, SQL, and MATLAB, along with experience in cloud computing and AI model deployment. He has also reviewed papers for IEEE Access and other reputed journals.

🏅 Awards and Recognition 

Sai Venkatesh Chilukoti has been recognized for his outstanding contributions to AI research, deep learning, and cybersecurity. He has received multiple conference paper acceptances, including at the Hawai’i International Conference on System Sciences (HICSS-56) and CHSN2021. His work on privacy-preserving AI has been published in high-impact journals like BMC Medical Informatics and Decision Making and Electronic Commerce Research and Applications.

He has also earned certifications in Deep Learning Specialization (Coursera), AI for Everyone (deeplearning.ai), and Applied Machine Learning in Python (University of Michigan). His research in Federated Learning has gained attention for its innovative approach to privacy protection in healthcare AI models. Additionally, Sai has contributed as a reviewer for IEEE Access and Euro S&P, demonstrating his expertise in computer security and AI ethics. His contributions to machine learning, cybersecurity, and privacy-aware AI continue to impact both academic and industrial domains.

🌍 Research Skills On Computer Science

Sai Venkatesh Chilukoti specializes in Federated Learning, Differential Privacy, and Deep Learning model optimization. His expertise spans AI-driven cybersecurity, identity recognition using mmWave radar sensors, and privacy-preserving medical imaging. He has worked extensively with machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.

Sai has developed AI models for secure collaborative learning, utilizing techniques like DP-SGD for privacy preservation. His research also explores transformer-based architectures, convolutional networks, and ensemble learning methods to enhance predictive performance. He has integrated advanced optimization techniques, including adaptive gradient clipping and label smoothing, into deep learning pipelines.

He has hands-on experience with federated learning platforms like Flower and privacy-preserving AI models in medical data analysis. His work in statistical modeling, computer vision, and neural networks has contributed to breakthroughs in security and healthcare AI. Sai’s research aims to advance AI applications while maintaining ethical and privacy standards.

📖 Publication Top Notes

  • A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
      • Authors: Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei
      • Journal: BMC Medical Informatics and Decision Making
      • Volume: 24, Issue 1
      • Article Number: 37
      • Year: 2024
  • Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
      • Authors: Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
      • Conference: 56th Hawaii International Conference on System Sciences
      • Year: 2023
  • Single Image Multi-Scale Enhancement for Rock Micro-CT Super-Resolution Using Residual U-Net
    • Authors: Liqun Shan, Chengqian Liu, Yanchang Liu, Yazhou Tu, Sai Venkatesh Chilukoti
    • Journal: Applied Computing and Geosciences
    • Year: 2024
  • Kernel-Segregated Transpose Convolution Operation
    • Authors: Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu
    • Conference: 56th Hawaii International Conference on System Sciences
    • Year: 2023
  • Modified ResNet Model for MSI and MSS Classification of Gastrointestinal Cancer
    • Authors: Sai Venkatesh Chilukoti, C. Meriga, M. Geethika, T. Lakshmi Gayatri, V. Aruna
    • Book Title: High Performance Computing and Networking: Select Proceedings of CHSN 2021
    • Year: 2022
  • Enhancing Unsupervised Rock CT Image Super-Resolution with Non-Local Attention
    • Authors: Chengqian Liu, Yanchang Liu, Liqun Shan, Sai Venkatesh Chilukoti, Xiali Hei
    • Journal: Geoenergy Science and Engineering
    • Volume: 238
    • Article Number: 212912
    • Year: 2024
  • Method for Performing Transpose Convolution Operations in a Neural Network
    • Inventors: Vijay Srinivas Tida, Sonya Hsu, Xiali Hei, Sai Venkatesh Chilukoti, Yazhou Tu
    • Patent Application: US Patent App. 18/744,260
    • Year: 2024
  • IdentityKD: Identity-wise Cross-modal Knowledge Distillation for Person Recognition via mmWave Radar Sensors
    • Authors: Liqun Shan, Rujun Zhang, Sai Venkatesh Chilukoti, Xingli Zhang, Insup Lee
    • Conference: ACM Multimedia Asia
    • Year: 2024
  • Facebook Report on Privacy of fNIRS Data
    • Authors: M. I. Hossen, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Xiali Hei
    • Preprint: arXiv preprint arXiv:2401.00973
    • Year: 2024

Phong Lam Nguyen Duy | Computer Science | Best Researcher Award

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Mr. Phong Lam Nguyen Duy | Computer Science | Best Researcher Award

👤 Mr. Phong Lam Nguyen Duy, University of Engineering and Technology – Vietnam National University, Vietnam

Phong Lam Nguyen Duy is a motivated undergraduate student in the Computer Science Department at the University of Engineering and Technology, Vietnam National University, Hanoi. Born on July 6, 2004, in Ha Dong, Hanoi, Phong Lam is passionate about exploring cutting-edge technologies in data science and artificial intelligence. His primary research interests include automated data quality assurance, machine learning algorithms, and advancements in large language models. Apart from academics, Phong Lam is actively involved in volunteering, demonstrating a commitment to fostering community development through initiatives like the ICPC Asia Pacific Championship and Hanoi Green Summer programs. A proactive learner and aspiring researcher, Phong Lam has already contributed as a university research assistant at the Intelligence Software Engineering Laboratory, where he leverages his problem-solving skills and technical expertise. Phong Lam aspires to contribute significantly to the field of Computer Science and aims to bridge gaps between theoretical concepts and real-world applications.

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Suitability for the “Research for Best Researcher Award”

Summary of Suitability:
Phong Lam Nguyen Duy demonstrates remarkable potential as a candidate for the “Research for Best Researcher Award.” Currently pursuing undergraduate studies in the Computer Science Department at Vietnam National University, Hanoi, Phong has already begun contributing to cutting-edge research fields, including automated data quality assurance, machine learning, and large language models. These areas are highly relevant and impactful in today’s rapidly evolving technological landscape, showcasing his alignment with contemporary research priorities.

Phong’s involvement as a university research assistant at the Intelligence Software Engineering Laboratory since February 2024 highlights his active engagement in research at an early stage of his academic career. His recent publication, “Leveraging Local and Global Relationships for Corrupted Label Detection” (2025), reflects his ability to contribute to academic discourse and address challenges in machine learning—a field critical for advancements in artificial intelligence.

🎓 Education 

Phong Lam Nguyen Duy is pursuing his undergraduate degree in Computer Science at the University of Engineering and Technology, Vietnam National University, Hanoi. Since his enrollment in September 2022, he has been immersed in a rigorous academic curriculum focused on Information and Communication Technologies. The program emphasizes critical areas such as software development, data analysis, and systems design, providing him with a robust foundation in computer science. The university’s strong research culture has further fueled his interest in machine learning and automated data quality assurance. Phong Lam has actively engaged in research initiatives and academic projects, allowing him to apply his theoretical knowledge in practical contexts. The vibrant academic environment at Vietnam National University has cultivated his technical skills and problem-solving abilities, enabling him to stay at the forefront of technological advancements. He views his education as the stepping stone to a thriving career in computer science and artificial intelligence.

💼 Professional Experience 

Phong Lam Nguyen Duy is currently a research assistant at the Intelligence Software Engineering Laboratory, located in Hanoi, Vietnam. Since February 2024, he has been collaborating with faculty and fellow researchers to tackle challenges in automated data quality assurance and machine learning. His work primarily involves developing methodologies that improve data accuracy and reliability while optimizing machine learning models for large-scale datasets. Phong Lam’s role includes conducting literature reviews, designing experiments, and implementing cutting-edge algorithms to solve complex problems. His contributions are instrumental in advancing projects that integrate theoretical computer science with practical applications. As a research assistant, he has honed his analytical, programming, and communication skills, fostering his growth as a budding researcher. This professional experience has not only solidified his technical expertise but also instilled a passion for lifelong learning and innovation, preparing him for future endeavors in the rapidly evolving field of artificial intelligence.

🏅 Awards and Recognition 

Phong Lam Nguyen Duy has been recognized for his academic excellence, volunteer contributions, and research potential. His participation as a volunteer for the prestigious ICPC Asia Pacific Championship 2024 earned him commendations for his organizational skills and dedication to promoting computer science education. Additionally, his involvement in the Hanoi Green Summer 2023 showcased his commitment to community service, where he actively participated in environmental sustainability initiatives. Phong Lam’s academic achievements at Vietnam National University include consistent top performance in his courses, particularly in areas related to machine learning and data science. His appointment as a research assistant at the Intelligence Software Engineering Laboratory further highlights his aptitude and potential for innovation in the field. Through these accolades, Phong Lam has established himself as a well-rounded individual, excelling academically while contributing to society and pursuing impactful research in computer science.

🌍 Research Skills On Computer Science

Phong Lam Nguyen Duy possesses a strong skill set in computational research and data science. His expertise includes automated data quality assurance, where he develops methodologies to identify and correct errors in datasets, ensuring reliability for machine learning applications. Phong Lam has a keen understanding of machine learning algorithms and their optimization, with experience in designing and training models for diverse applications. His research focus also encompasses advancements in large language models, where he explores their capabilities for natural language processing tasks. As a research assistant, he has gained hands-on experience in experimental design, data preprocessing, and implementing scalable solutions. Proficient in programming languages like Python and R, Phong Lam is adept at leveraging tools such as TensorFlow and PyTorch for deep learning projects. His analytical mindset and problem-solving abilities make him an invaluable contributor to the ever-evolving landscape of artificial intelligence and computer science research.

📖 Publication Top Notes

Title: Leveraging local and global relationships for corrupted label detection
  • Journal: Future Generation Computer Systems
  • Year: 2025