This course covers advanced topics on information retrieval, web search, and related scalable information systems. The topics include search engines, classification, indexing and data serving, ranking, user behavior analysis, privacy, and system support for online services/data processing.
Many computer systems have access to sensitive information nowadays and, consequently, information leakage has become a significant security concern for users. Side-channel vulnerabilities that are based on information gained by observing non-functional properties of computer systems (such as execution time or memory usage) can enable attackers to infer the secret information that the system accesses. In this course, we will discuss static and dynamic analysis techniques for detecting information leakage in computer systems.
The graduate-level course covers the fundamentals of differential privacy (DP), as well as various applications of DP in statistical and machine learning applications. Students will learn the fundamentals of DP and practice how to prove formal differential privacy guarantees. There will also be hands-on training on using “autodp” for state-of-the-art DP computation.
This course will cover deep learning methods for neural machine translation and text generation. It will cover neural models for discrete sequences including Long-short term memory, Transformer, and encoder-decoder frameworks for generating language. It includes training objectives and optimization algorithms for learning those models. It will also include strategy such as back-translation, knowledge distillation. Finally, it will cover multilingual machine translation, low-resource translation, speech translation, and visual translation.
This course studies Markov Chain Monte Carlo (MCMC) algorithms. MCMC algorithms are a widely used tool for sampling from distributions defined over a huge combinatorial set. It is often easy to define a Markov chain with the desired distribution as its equilibrium distribution but it is difficult to design a chain which converges efficiently and to determine convergence. This is a theoretical course focusing on mathematical tools for analyzing the convergence rate of Markov chains. This course is appropriate for both undergraduate and grad
What would the world look like with a bionic eye? This graduate course will introduce students to the multidisciplinary field of bionic vision, with an emphasis on both the computer science and neuroscience of the field. The course will give an overview of current bionic eye technology designed to restore vision to people living with incurable blindness.