Vikram Waradpande
Vikram is a Computer Science master's student at Columbia University with a focus on Machine Learning. He completed his bachelor's in Computer Science and Mathematics in India from BITS Pilani. Before Columbia, he was a part of the engineering and strategy team at Goldman Sachs, where he built scalable and efficient trading tools. He has also had research experience working at TU Leibniz, Germany in the area of Reinforcement Learning and Parallel Programming. He presented his research at the International Conference on Mining and Learning on Graphs in 2020 in Vienna, Austria. He was a teaching assistant for three courses during his academic career, which involved conducting seminars (NumPy, Pytorch, etc.), organizing technical meetings and organizing research fairs. He has also tutored for the website 'Chegg' for more than two years where he taught Math and Programming to high school and university students.
Session
As we are in an era of big data where large groups of information are assimilated and analyzed, for insights into human behavior, data privacy has become a hot topic. Since there is a lot of private information which once leaked can be misused, all data cannot be released for research. This talk aims to discuss Differential Privacy, a cutting-edge technique of cybersecurity that claims to preserve an individual’s privacy, how it is employed to minimize the risks with private data, its applications in various domains, and how Python eases the task of employing it in our models with PyDP.