## Laura TomaI am an Associate Professor of Computer Science, and, during 2014-2018, also department chair. | ||||

Department of Computer Science
Bowdoin College 8650 College Station Brunswick, ME 04011 |
Office: Searles 219 Email: ltoma@bowdoin.edu Phone: + 1-207-725-3569 Fax: + 1-207-725-3750 |

Publications | Student research and projects | Teaching | CV | Personal

My research is in the theory and practice of cache-efficient algorithms for large data, and in particular applications that involve large, high-resolution data in Geographic Information Systems (GIS). Memory-efficient algorithms and parallel algorithms share many techniques and insights, which has brought me towards high-performance, parallel computing using Bowdoin's HPC grid. Together with some great students, I am exploring efficient and scalable algorithms for modeling fundamental problems on terrains, such as visibility, flooding, sea level rise and least-cost-path surfaces. Our goal is to come up with approaches that are resource-efficient (CPU, IO, cache, parallel), are backed by algorithms that we can theoretically prove efficient while at the same time work well in practice. Ultimately, our goal is to transfer these algorithms into free and open-source software to be used by the community.

I am grateful for the support of NSF award (2007-2013).

I finished my Ph.D in 2003 at Duke University, Department of Computer Science. My thesis advisor was Lars Arge. My dissertation focused on IO-efficient algorithms for modeling flow on very large terrains (terraflow | terrastream), as well algorithms for basic graph problems like IO-efficient breadth-first search and depth-first search, IO-efficient topological sort, IO-efficient minimum spanning trees and IO-efficient shortest paths.

In Fall 2017 I am teaching Algorithms (cs2200) and Algorithms for GIS (cs3225).

- Computational geometry
- Algorithms for GIS
- Spatial data structures
- Computing with massive data
- Algorithms
- Data Structures
- Introduction to Computer Science

- Exploring self-efficacy and its impact in teaching and learning algorithms
- r.viewshed: Computing viewsheds on grid terrains in external memory
- r.terracost: Computing multi-source shortest path surfaces on terrains in external memory
- r.terraflow: Computing flow-related indices on massive grid terrains