Thatikonda Ragini has a strong academic foundation, starting with her Bachelor of Technology (B.Tech.) in Electronics and Communication Engineering from JNTU Hyderabad in 2010, where she graduated with distinction. She then pursued a Master of Technology (M.Tech.) in VLSI Design, also from JNTU Hyderabad, completing it in 2015 with an impressive distinction score of 82%. Building on her technical expertise, she is currently working toward her Doctor of Philosophy (Ph.D.) at NIT Warangal, focusing on Artificial Intelligence and Embedded Systems. Having submitted her thesis, she is set to complete her Ph.D. in 2024. Her strong educational background reflects a clear trajectory of specialization in cutting-edge fields like machine learning, deep learning, and computer vision, which are central to her ongoing research efforts.
Professional Experience
Ragini’s professional journey spans both academia and research. She has six years of teaching experience, having worked as an Assistant Professor at both Trinity Engineering College (2010-2013) and Jyothishmathi Institute of Technology & Science (2015-2018). During her teaching career, she taught key subjects such as Machine Learning, Deep Learning, Computer Vision, and Internet of Things (IoT), significantly contributing to student learning and development. Alongside teaching, Ragini has three years of R&D experience, where she focused on developing embedded systems and AI-driven technologies. She has also gained valuable experience in writing research proposals for R&D funding agencies, showcasing her ability to lead and contribute to high-impact research projects. Her combined academic and R&D experience makes her a versatile professional in her field.
Research Interests:
Ragini’s research interests lie at the intersection of machine learning, deep learning, and computer vision. Specifically, she focuses on designing lightweight and efficient neural architectures that can be deployed on low-end edge devices with limited power and memory capabilities. Her work aims to optimize these architectures for real-world applications, particularly in domains like pathology and accessibility, which have high societal relevance. Ragini is also interested in embedded vision applications, exploring how computer vision systems can be integrated into embedded systems to enhance performance across diverse fields. Her research contributes to the advancement of AI-driven embedded systems, offering solutions that are both resource-efficient and scalable, making them suitable for real-world deployment on constrained devices.
Research Skills:
Ragini possesses a diverse set of research skills that position her as a highly capable researcher. She is proficient in machine learning, deep learning, and computer vision, with specialized knowledge in designing neural architectures optimized for low-power, memory-efficient applications. Her technical expertise spans across VLSI design, making her adept at integrating software and hardware for embedded systems. Ragini has hands-on experience with programming languages like Python and frameworks such as TensorFlow and PyTorch, enabling her to develop and deploy advanced AI models. Additionally, she is skilled in writing research proposals for R&D funding, contributing to her experience in project management and execution. Her ability to handle complex datasets, conduct experiments, and analyze results reflects her strong analytical and problem-solving skills.
Awards and Honors:
Ragini’s academic and research accomplishments have been recognized through several accolades. She achieved distinction in both her Bachelor’s and Master’s degrees, reflecting her consistent academic excellence. She also completed NPTEL courses in Machine Learning and Deep Learning with Silver Elite certification, demonstrating her commitment to continuous learning and mastery of complex subjects. Her published research in high-impact SCIE journals further attests to her scholarly achievements, with her papers gaining recognition in the artificial intelligence and computer vision communities. Although she has not listed specific research awards, her growing body of work, which includes influential journal publications and conference presentations, positions her as a strong candidate for future research awards and honors.
Conclusion
Thatikonda Ragini has a strong research portfolio with an impressive focus on embedded systems, machine learning, and computer vision. Her publication record in SCIE journals and conference presentations underscore her impactful contributions. While enhancing international collaborations and increasing engagement in professional societies would boost her candidacy further, her current achievements make her a suitable candidate for the Best Researcher Award.