Dissertation or Thesis Defense

Learning-based model predictive control (MPC) is an increasingly prominent control paradigm in recent years. One primary approach in learning-based MPC is to leverage machine or deep learning tools to construct accurate dynamics models. These models are then deployed into optimization-based control schemes. Although these frameworks can potentially provide significant performance improvements, a number of key […]

The intersection of machine learning (ML) and dynamical systems and control (S&C) has become a prominent area of research in recent years. While most work applies ML to S&C problems, this dissertation explores the reverse direction: using S&C tools to address challenges in ML and optimization. This thesis is comprised of three parts: In part […]

To improve FPGA design productivity, our goal is to create a development experience for FPGAs that aligns closely with widely accepted software design principles. Software programmers quickly test their minimally completed design, identify the bottleneck, and incrementally refine the design. In FPGA design, however, such incremental refinement is not currently supported. (1) FPGA compilation is long, […]