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

Syed Mohammod Minhaz Hossain | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Syed Mohammod Minhaz Hossain | Computer Science | Best Researcher Award

👤 Assoc. Prof. Dr. Syed Mohammod Minhaz Hossain, Premier University, Bangladesh

Syed Mohammod Minhaz Hossain is a passionate researcher and IT professional dedicated to advancing the field of Computer Science and Engineering. He is currently pursuing a Ph.D. in Computer Science & Engineering at Chittagong University of Engineering & Technology (CUET). With a strong academic background, he earned his M.Sc. and B.Sc. in Computer Science & Engineering from CUET, securing notable positions. Hossain is committed to skillful learning and aims to create a synergy between industry and academia. He has published numerous research papers and contributed significantly to the scientific community, particularly in the areas of AI, machine learning, and environmental studies. Apart from his academic journey, he is a fervent advocate of education, believing in the power of teaching to shape well-rounded professionals who can contribute to society’s progress.

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 🌟  Suitability of Syed Mohammod Minhaz Hossain for the Research for Best Researcher Award:

Syed Mohammod Minhaz Hossain demonstrates strong academic and professional qualifications, making him a highly suitable candidate for the Research for Best Researcher Award. His dedication to academic excellence and research is reflected in his substantial academic achievements, including a Ph.D. in Computer Science and Engineering from Chittagong University of Engineering & Technology (CUET), and his outstanding undergraduate and postgraduate performance. His consistent recognition, such as the UGC Ph.D. Fellowship and multiple scholarships, underscores his commitment to research and academic growth.

Hossain has made notable contributions to the research community, particularly in the fields of artificial intelligence, machine learning, and environmental science. His extensive publication record includes numerous articles in high-impact journals such as PLoS ONE, Chemosphere, and Annals of Data Science, with a variety of topics ranging from water quality assessments to disease classification and COVID-19 detection using deep learning. His research not only focuses on technological advancements but also addresses pressing societal challenges, such as public health, environmental sustainability, and cybersecurity.

🎓  Education

Syed Mohammod Minhaz Hossain’s academic journey is marked by consistent excellence. He is currently pursuing his Ph.D. in Computer Science & Engineering at Chittagong University of Engineering & Technology (CUET). Prior to that, he completed his M.Sc. in Computer Science & Engineering at CUET in 2022, where he earned a CGPA of 3.42. He also holds a B.Sc. in the same field from CUET, securing a remarkable CGPA of 3.56. His foundation in education started at Chittagong Collegiate School, where he excelled with a GPA of 4.63 in his SSC and later earned a GPA of 4.50 in his HSC at Chittagong College. Throughout his academic career, Hossain has received multiple scholarships, including the UGC PhD Fellowship (2021-2022) and various merit-based awards, underlining his dedication and outstanding performance in the field of Computer Science.

💼 Professional Experience

Syed Mohammod Minhaz Hossain’s professional experience blends academia and industry, underscoring his passion for teaching and research. As a faculty member at Premier University, Bangladesh, Hossain conducts web system and program applications courses, integrating real-world industry skills into the classroom. His expertise is further demonstrated through his role in various research projects, focusing on areas such as artificial intelligence, deep learning, and environmental science. Hossain’s experience includes collaborating with international researchers, contributing to high-impact journals and conferences. His role in designing and developing academic curricula reflects his commitment to fostering future IT professionals who are not only skilled but also socially responsible. Additionally, Hossain’s involvement in the University of Technology, Sydney (UTS) College’s academic programs highlights his global outlook and the application of advanced research in practical teaching settings.

🏅 Awards and Recognitions 

Syed Mohammod Minhaz Hossain’s journey is characterized by numerous academic and research accolades. He received the prestigious UGC PhD Fellowship for 2021-2022, showcasing his commitment to advancing knowledge in Computer Science. Hossain earned the fourth position in his B.Sc. at CUET and was a recipient of the Board Scholarship in his HSC in 2003. He was also honored with the Junior Merit Scholarship in 1998 and the Primary Merit Scholarship in 1995, underlining his consistent academic excellence from an early age. His research contributions have been widely recognized, with multiple publications in high-impact journals such as PLoS ONE, Annals of Data Science, and Chemosphere. Furthermore, Hossain’s work on machine learning models for health-related issues and his involvement in international book chapters reflect his growing influence in the global research community.

🌍 Research Skills On Computer Science

Syed Mohammod Minhaz Hossain possesses a broad range of research skills that span artificial intelligence, machine learning, deep learning, and data science. His expertise includes applying these advanced technologies to solve complex problems in areas like health diagnostics, environmental monitoring, and cybersecurity. Hossain has developed proficiency in using deep neural networks, self-attention mechanisms, and convolutional models, as seen in his research on plant leaf disease recognition and heart disease prediction. Additionally, he has contributed to studies focused on the detection of COVID-19 fake news, Parkinson’s disease classification, and coastal water quality assessment. His research methodology includes leveraging large datasets, conducting statistical analyses, and employing advanced algorithms to create efficient and scalable solutions. Hossain’s ability to integrate interdisciplinary knowledge into his projects further enhances his capability to make impactful contributions to both academic and practical fields.

📖 Publication Top Notes

  • Cyber Intrusion Detection Using Machine Learning Classification Techniques
    • Authors: H Alqahtani, IH Sarker, A Kalim, SMM Hossain, S Ikhlaq, S Hossain
    • Citations: 189
    • Year: 2020
  • A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection
    • Authors: RC Ripan, IH Sarker, SMM Hossain, MM Anwar, R Nowrozy, MM Hoque
    • Citations: 66
    • Year: 2021
  • Rice Leaf Diseases Recognition Using Convolutional Neural Networks
    • Authors: SMM Hossain, MMM Tanjil, MAB Ali, MZ Islam, MS Islam, S Mobassirin
    • Citations: 49
    • Year: 2021
  • Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models
    • Authors: SMM Hossain, K Deb, PK Dhar, T Koshiba
    • Citations: 34
    • Year: 2021
  • Amassing the Covid-19 Driven PPE Wastes in the Dwelling Environment of Chittagong Metropolis and Associated Implications
    • Authors: MJ Abedin, MU Khandaker, MR Uddin, MR Karim, MSU Ahamad
    • Citations: 22
    • Year: 2022
  • Assessment of Coastal River Water Quality in Bangladesh: Implications for Drinking and Irrigation Purposes
    • Authors: MR Uddin, MU Khandaker, S Ahmed, MJ Abedin, SMM Hossain
    • Citations: 13
    • Year: 2024
  • Spam Filtering of Mobile SMS Using CNN–LSTM Based Deep Learning Model
    • Authors: SMM Hossain, JA Sumon, A Sen, MI Alam, KMA Kamal, H Alqahtani
    • Citations: 13
    • Year: 2021
  • Plant Leaf Disease Recognition Using Histogram-Based Gradient Boosting Classifier
    • Authors: SMM Hossain, K Deb
    • Citations: 13
    • Year: 2021
  • Content-Based Spam Email Detection Using an N-gram Machine Learning Approach
    • Authors: NJ Euna, SMM Hossain, MM Anwar, IH Sarker
    • Citations: 9
    • Year: 2023
  • Trash Image Classification Using Transfer Learning-Based Deep Neural Network
    • Authors: D Das, A Sen, SMM Hossain, K Deb
    • Citations: 9
    • Year: 2022