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.
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
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- 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
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- 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