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Statistical Machine Learning in Data Processing: Challenges and Opportunities
February 9 @ 4:00 pm - 5:00 pm
The primary objective of data processing applications is to derive statistical inferences from data and subsequently perform statistical tasks, addressing communication and computation constraints, as well as privacy and security concerns. In this presentation, I will begin by discussing statistical approaches applied to the perceptual quality of images and videos in the context of compression tasks. This is particularly relevant in applications such as online video streaming, where statistical tools play a crucial role in extracting information from newly available frames in a casual manner and propagating it for future reconstructions. I will explore two perception loss functions, highlighting their differences based on the reconstructions of consecutive frames. Next, I will delve into statistical inference within large networks of agents, emphasizing the necessity for adaptive decision-making to handle data under communication constraints. Additionally, I will briefly touch upon privacy and security challenges inherent in these two networks. To conclude, I will summarize the presentation and propose research directions aimed at addressing both statistical and privacy/security challenges.
Speaker(s): Sadaf Salehkalaibar,
11:00 AM Start of talk
12:00 PM Conclusion