| Honors Theses
Following is a list of honors theses produced by recent graduates of
the Computer Science Bachelor of Science program. Abstracts and advisor
names are included. The full thesis text is linked as a PDF file from
the paper's title, when available.
May 2007
Erik Andersen
Real-time Path Planning and Simulation of Large Human Crowds
Advisors: Ming Lin and Dinesh Manocha
Human crowds are everywhere in the real world, and therefore they are
important to simulate in virtual environments. We present a novel approach
for real-time path planning of multiple virtual agents in complex dynamic
scenes. Our algorithm is used for multi-agent planning in pursuit-evasion
and crowd simulation scenarios consisting of hundreds or thousands of
moving agents, each with a distinct goal.
Kris Jordan
Mixed-Initiative Access Control: Optimizing the Data Guardian's Role
Advisor: Prasun Dewan
Robust access control capabilities are found in every modern operating-system
yet end-users rarely take advantage of them. Mixed-initiative access
control offers a more intuitive model for arbitrating rights between
users than traditional access control.
Adam Roberts
Inferring Missing Genotypes in Large SNP Panels
Advisors: Leonard McMillan and Wei Wang
This project involved the development of an efficient technique and
algorithmic implementation for inferring (filling in) missing values
in Single Nucleotide Polymorphism datasets.
Joel Sutherland
Object Resource Manager and
its Application to a Sea Turtle Virtual Environment
Advisor: Diane Pozefsky
This project consists of two parts. First an Object Relational Mapper
was developed in PHP and second, the Mapper was used as a part of a
framework to create an Experiment Management Application for the Lohmann
Turtle Lab.
Adi Unnithan
Improving Search Relevancy through
Human Indexing and Data Mining
http://www.adiunnithan.com/flashcard/
Advisor: Diane Pozefsky
Abstract: Popular search engines today index pages on a defined heuristic,
such as the number of links to a page. We can improve upon this measure
and display more relevant results by utilizing social networks and their
repositories of information and applying data mining techniques to them,
such as association rule finding and clustering. In addition, we can
improve the searching experience by providing non-linearity in the user
interface. |
