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Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
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Synthetic data generation has drawn growing attention due to the lack of training data in many application domains. It is useful for privacy-concerned applications, e.g. digital health applications
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Privacy-Enhancing Technologies (PETs) are a set of cryptographic tools that allow information processing in a privacy-respecting manner. As an example, imagine we have a user, say Alice, who wants
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existing tokenization frameworks, analyzing potential risks, and developing novel security protocols to protect sensitive data and ensure the integrity of tokenized assets. Applicants will investigate
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to examine vast amounts of textual data, identifying keywords, phrases, and sentiment that may indicate extremist views or intentions. Analysing audio involves techniques such as speech recognition, keyword
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to avoid system bottlenecks, and ensuring low-latency performance. Energy-Efficient Operations with Carbon-Aware Scheduling: Example: For non-urgent data processing, SmartScaleSys (S3) could prioritize tasks
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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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environments—such as MOOCs, online degrees, and data-intensive Learning Management Systems—necessitate scalable solutions to provide timely, high-quality feedback. However, existing AI-powered assessment systems
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Multi-View Learning in CV and NLP Robust Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design from Analytical Spectra Hybrid Quantum–Classical Algorithms