Her work focuses on the computational geometry of curve similarity computation. She has a broad interest in high-dimensional algorithmic problems involving geometry, motivated by applications in geographic information science and data analysis. Anne’s primary goal is to design algorithms and data structures that are both practical and have provable performance. To this end, she combines classical worst-case analysis with approximation techniques and realistic input models. Her interests also include application of randomization techniques to obtain simple and effective solutions for computationally hard problems. She is especially interested in curve and graph similarity computations, ranging from mapping trajectories to road networks and clustering time series. Courses examine software, algorithms, control systems and theoretical computer science.