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Privacy-utility-efficiency-robustness trade-offs through novel algorithms
MAS: Media Arts and Sciences
Please contact firstname.lastname@example.org to indicate your interest. Please also include your resume.
Collaboration and data sharing among individuals, institutions, regional, national and global entities holds key to increasing the amount of available data to be fed as input to data-hungry machine learning models. But centralization of data is hindered through regulatory, ethical, trust, competition and logistical constraints. The UROP will research on pushing the resource and communication efficiency limits of distributed machine learning while catering to challenges of statistical worst-case guarantees on privacy and non-homogeneity of data sets across clients. They are expected to develop methods to learn non-parametric representations that are one-way from an information theoretic point of view. The ultimate goal is to get the most out of the privacy-utility-efficiency-robustness trade-offs through novel algorithms that are theoretically justified. How can all this be done with as few rounds of communication as possible? this makes it suitable to the edge-device ecosystem. The work would involve one or more of distributed machine learning, statistics, deep learning, optimization, split learning and some experimentation with either of PyTorch/TensorFlow/Keras/Matlab/Python/R. Ideal candidate should be able to proactively contribute with periodic updates. Work around adjacently connected problem areas is also encouraged. Both, hands-on experimental projects and/or theoretical opportunity exists. Desired background: Coding fluency/ research mindset.
coding fluency, algorithmic or mathematical mindset encouraged