Advanced Topics in Computer Science: Systems and Machine Learning

In this graduate seminar, we plan to read and discuss recent research papers in the intersection between systems and machine learning (ML). We plan to focus on two types of research papers: systems for ML and ML for systems. The first category includes papers about building efficient hardware (e.g. CPU, GPU, FPGA, ASICs) and software (parallel, distributed, secure, privacy-preserving systems for ML. The second category includes papers about how to use ML in systems software and hardware designs including new data structures and optimization methods.