Prof. Pramod Ganapathi

Prof. Pramod Ganapathi is a distinguished Research Assistant Professor at the State University of New York at Stony Brook, where he delves into the intricate world of mathematical and algorithmic puzzles. His expertise extends to the realms of algorithms to discover algorithms, parallel algorithms, cache-efficient algorithms, big data, and high-performance computing. With a rich academic and professional background, Prof. Ganapathi has previously served as an Assistant Professor at the Indian Institute of Technology, Indore, and has also ventured into entrepreneurship as the founder and CEO of an online education startup. Prof. Ganapathi earned his Ph.D. in computer science from Stony Brook University, where his research primarily focuses on the automatic discovery of efficient algorithms for dynamic programming problems. His scholarly contributions include a published book on mathematical and algorithmic puzzles, which has been well-received in academic circles. His work has not gone unnoticed, as he has been the recipient of several awards recognizing his outstanding contributions to the field. At Stony Brook, Prof. Ganapathi is actively involved in teaching a variety of courses, such as data structures, algorithmic problem-solving, and theory of computation. His teaching philosophy is unique, emphasizing a puzzle-centric approach that encourages students to engage deeply with the material. He also champions supervised peer-learning, fostering a collaborative learning environment that benefits both students and educators. Prof. Ganapathi's dedication to education and research is evident in his extensive contributions to the field. He is passionate about inspiring the next generation of computer scientists and is committed to advancing the frontiers of knowledge in his areas of expertise. His work continues to influence both his students and peers, making a significant impact on the academic community. In addition to his academic pursuits, Prof. Ganapathi is known for his innovative approach to problem-solving and his ability to translate complex theoretical concepts into practical applications. His work not only advances theoretical understanding but also has practical implications in the field of computer science, particularly in the development of efficient algorithms and high-performance computing solutions.