Dr. Vijay Srinivas Tida | Computer Science | Excellence in Research
Dr. Vijay Srinivas Tida, College of St Benedict and St John’s university, United States
Dr. Vijay Srinivas Tida is a dedicated researcher and academician currently serving as a Tenure-track Assistant Professor at the College of St. Benedict and St. John’s University, Minnesota. With a strong foundation in Electronics, Computer Engineering, and Deep Learning, he has developed a notable reputation in the fields of differential privacy, federated learning, and FPGA hardware acceleration. His Ph.D. dissertation at the University of Louisiana at Lafayette explored optimizing transpose convolution operations—a critical component in CNNs. Dr. Tida’s academic journey has taken him through top institutions including Illinois Institute of Technology and Koneru Lakshmaiah University, consistently achieving high academic honors. He has actively contributed to privacy-preserving machine learning for healthcare and has authored several journal articles and conference papers. Passionate about teaching, he also mentors students in deep learning and hardware systems, making him a valuable contributor to modern computer science education.
Profile
Suitability for Research for Excellence in Research Award: Vijay Srinivas Tida
Vijay Srinivas Tida stands out as a highly deserving candidate for the Research for Excellence in Research Award due to his exceptional contributions in the fields of deep learning optimization, differential privacy, federated learning, and hardware accelerator design. His academic journey reflects consistent excellence, culminating in a Ph.D. in Computer Engineering with a remarkable GPA of 3.9/4.0 from the University of Louisiana at Lafayette. Complemented by a strong foundation in Electrical and Computer Engineering from Illinois Institute of Technology and Electronics and Communication Engineering from Koneru Lakshmaiah University, his educational background is solid and highly relevant.
Throughout his academic and professional career, Vijay has demonstrated a commitment to pioneering research, particularly focusing on the optimization of deep convolutional neural networks, privacy-preserving machine learning models, and hardware security. His doctoral dissertation on optimizing transpose convolution operations and his multiple research projects emphasize innovative approaches that enhance the efficiency and security of AI models, which are critical in today’s technology-driven healthcare and security domains.
Education
Dr. Vijay Srinivas Tida earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette (2018–2023), under the mentorship of Dr. Sonya Hsu and Dr. Xiali Hei, graduating with an impressive GPA of 3.9/4.0. His dissertation focused on optimizing transpose convolution operations for efficient deep learning computation. Prior to this, he completed his Master’s degree in Electrical and Computer Engineering from Illinois Institute of Technology (2016–2018), working with Dr. Erdal Oruklu and maintaining a GPA of 3.8/4.0. He began his academic journey with a Bachelor of Science in Electronics and Communication Engineering from Koneru Lakshmaiah University (2011–2015), guided by Dr. Nalluri Siddaiah, achieving a perfect GPA of 4.0/4.0. His academic background reflects a blend of theoretical knowledge and practical experience in machine learning, hardware design, and optimization algorithms, which forms the core of his current research and teaching interests.
Professional Experience
Dr. Tida’s professional trajectory spans across academic teaching and innovative research. He currently holds the position of Assistant Professor at the College of St. Benedict and St. John’s University, where he teaches and mentors students in computer science. Previously, he served as a Postdoctoral Research Assistant at the University of Louisiana at Lafayette (May–Aug 2023), contributing to projects in privacy-preserving AI and FPGA-based accelerators. From 2018 to 2022, he was a Graduate Teaching Assistant and Lab Instructor, where he taught courses including Computer Architecture and Computer Engineering Labs. He also held Research Assistant roles across institutions like Illinois Institute of Technology and Koneru Lakshmaiah University, engaging in high-impact projects on energy harvesting, sensor security, and neural networks. Dr. Tida’s teaching is complemented by his commitment to community outreach, where he has conducted programming workshops for high school students and offered deep learning sessions to Ph.D. candidates.
Awards and Recognition
Dr. Tida has been the recipient of numerous honors recognizing both his academic excellence and research contributions. Notably, in 2024, he received $1,750 to attend the prestigious SIGCSE Technical Symposium on Computer Science Education. He was awarded a $6,500 Summer Collaborative Research Grant and $1,000 by the Faculty Development Research Committee for conference travel. In 2023, the College of St. Benedict and St. John’s University provided him with high-performance computing resources worth $16,000. During his doctoral studies, he earned a Dissertation Completion Fellowship and secured consistent Graduate Teaching and Research Assistantships from 2018 to 2022. These accolades reflect his capabilities in leading cutting-edge projects and fostering academic excellence. His continued association with academic conferences such as HICSS and ACM further underscores his recognition within the computing research community.
Research Skill On Computer Science
Dr. Tida’s research skills encompass a dynamic combination of deep learning, optimization, hardware acceleration, and data privacy. His expertise lies in the development and optimization of Convolutional Neural Networks (CNNs), especially with transpose convolution operations—a subject central to his doctoral work. His focus on Differential Privacy and Federated Learning reflects his commitment to secure and ethical AI, particularly for healthcare data applications. He is adept at hardware-level design using Field Programmable Gate Arrays (FPGAs), enabling real-time and efficient AI computations. With a solid command over Natural Language Processing, he has also published in areas like fake news detection and spam classification using models such as BERT. Dr. Tida’s proficiency spans Python, Arduino C, and hardware descriptive languages, supported by his consistent role in mentoring and peer reviewing. His integration of theoretical algorithms with practical systems development defines his impactful presence in modern computational research.
Publication Top Notes
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Universal Spam Detection using Transfer Learning of BERT Model
Author(s): VSTDS Hsu
Citation: 89
Year: 2022 -
A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
Author(s): SV Chilukoti, L Shan, VS Tida, AS Maida, X Hei
Citation: 45
Year: 2024 -
Transduction shield: A low-complexity method to detect and correct the effects of EMI injection attacks on sensors
Author(s): Y Tu, VS Tida, Z Pan, X Hei
Citation: 38
Year: 2021 -
Design and Analysis of High Efficient UART on Spartran-6 and Virtex-7 Devices
Author(s): KH Kishore, CA Kumar, TV Srinivas, GV Govardhan, CNP Kumar, …
Citation: 20
Year: Not specified (likely between 2015–2018 based on journal timeline) -
A unified training process for fake news detection based on fine-tuned BERT model
Author(s): VS Tida, S Hsu, X Hei
Citation: 10
Year: 2022 -
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
Citation: 8
Year: 2023 -
Kernel-Segregated Transpose Convolution Operation
Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
Citation: 5
Year: 2023 -
Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks
Author(s): VS Tida, R Shah, X Hei
Citation: 2
Year: 2022 -
Unified Kernel-Segregated Transpose Convolution Operation
Author(s): VS Tida, MI Hossen, L Shan, SV Chilukoti, S Hsu, X Hei
Citation: Not listed
Year: 2025 -
Differentially private fine-tuned NF-Net to predict GI cancer type
Author(s): SV Chilukoti, IH Md, L Shan, VS Tida, X Hei
Citation: Not listed
Year: 2025