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Shankar Gangisetty
Researcher - Professor - Computer Vision - Point Cloud - Machine/Deep Learning - NLP - Data Mining - Mentor - Consultant
Professional Background
Shankar Gangisetty is a distinguished researcher and professor at KLE Technological University in Hubballi, India, specializing in the fields of computer vision and machine learning. He leads innovative projects within the Computer Vision and Graphics Lab, where his focus lies primarily on the development of advanced learning systems and data processing methodologies. As a principal investigator, he has spearheaded two groundbreaking research projects sponsored by the Department of Science and Technology, Government of India, which concentrate on crowdsourcing frameworks for archiving and preserving cultural heritage sites and underwater digital restoration of these sites utilizing cutting-edge data processing and three-dimensional reconstruction techniques.
Education and Achievements
Dr. Gangisetty's educational journey is impressive and marked by significant accomplishments. He earned his Doctorate in Computer Vision with a focus on 3D Vision and machine learning at the prestigious B V B College of Engineering and Technology in Hubballi, under the mentorship of Prof. Uma Mudenagudi. His PhD dissertation, titled "Representation and Analysis of 3D Point Cloud Data," was funded by the Indian Digital Heritage Space initiative of the Government of India and has yielded valuable insights that contribute to the 3D reconstruction of digital cultural heritage sites, particularly focusing on the UNESCO World Heritage Site of Hampi Temples in India. Notably, his research earned him the esteemed best doctoral symposium award at the International Conference on Computer Vision, Graphics, and Image Processing (ICVGIP) 2016, hosted by IIT Guwahati.
In addition to his doctoral degree, Dr. Gangisetty holds a Master of Technology (M.Tech.) in Computer Science and Engineering from the National Institute of Technology Karnataka and a Bachelor of Engineering (B.E.) in Computer Science and Engineering from B V B College of Engineering and Technology, both of which have laid a solid foundation for his career in research and academia.
Career Progression
Dr. Gangisetty's professional journey began with his role as a Research Assistant at the Networking Lab of the CSE Center at NITK Surathkal, where he honed his skills in data analytics and scientific research. He later served as a trainee at the Center for Visual Information Technology at IIIT Hyderabad, further enriching his expertise in visual intelligence.
His academic career continued to flourish as he took on various positions including Senior Lecturer and Associate Professor at B V B College of Engineering and Technology. Eventually, his commitment to advancing the field of computer science led him to join KLE Technological University, initially as an Associate Professor and now as a leading figure within the Center of Excellence in Visual Intelligence. In his current role as a professor in the School of Computer Science and Engineering, he is dedicated to educating the next generation of data scientists and computer engineers, teaching subjects such as Database Management Systems, Data Mining, and Machine Learning.
Research and Contributions
Dr. Gangisetty's research contributions to computer vision extend beyond theory; his projects are deeply rooted in real-world applications, particularly concerning the conservation of cultural heritage. His interdisciplinary approach bridges technology and cultural preservation, making a significant impact in the field of digital heritage. By utilizing machine learning and deep learning techniques, he has developed substantial frameworks and methodologies that assist in the archiving and restoration of heritage sites, ensuring that the cultural narratives of these locations are preserved for future generations.
In addition to his technical expertise, Dr. Gangisetty actively participates in the academic community, sharing knowledge and insights through conferences, workshops, and collaborative projects aimed at advancing computer vision and data science, thus contributing to the growing body of literature in these pivotal areas of study.
