Tamalika Mukherjee is a postdoctoral research scientist at Columbia University working with Rachel Cummings. Her research interests are primarily at the intersection of data privacy and theoretical computer science and its impact on society. Her work has focused on developing efficient privacy-preserving algorithms for fundamental problems in machine learning and combinatorial optimization, as well as developing methods to mitigate potential biases caused by privacy technologies. She earned her Ph.D. in Computer Science at Purdue University under the supervision of Jeremiah Blocki and Elena Grigorescu. During her Ph.D., Tamalika received the Bilsland Dissertation Fellowship and interned at Google Research and Analog Devices. She earned her B.S./ M.S. in Applied and Computational Mathematics from Rochester Institute of Technology.

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AG Audience

English

60 Minutes

Bochum

Collecting user data is crucial for advancing machine learning, social science, and government policies, but the privacy of the users whose data is being collected is a growing concern. Organizations often deal with a massive volume of user data on a regular basis — the storage and analysis of such data is computationally expensive. Thus developing algorithms that not only preserve formal privacy but also perform efficiently is a challenging and important necessity. Since preserving privacy inherently involves some data distortion which potentially sacrifices accuracy for smaller populations, a complementary challenge is to develop responsible privacy practices that ensure that the resulting privacy implementations are equitable.
My talk will focus on Differential Privacy (DP) --- a rigorous mathematical framework that preserves the privacy of individuals in the input dataset, and explore the nuanced landscape of privacy-preserving algorithms through three interconnected perspectives: the systematic design of both time and space-efficient private algorithms, and strategic approaches to creating equitable privacy practices.

Kateryna Panfilova

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