Brian Clark Gauch

I am a Ph.D student in Vanderbilt's EECS program. My current interests are primarily machine learning, particularly manifold-based dimensionality reduction. My advisor is Dr. Richard Alan Peters. I am currently a teaching assistant, and I was formerly a research assistant with Dr. Gautam Biswas with the Teachable Agents Group.

I received my Bachelor's in Computer Science from Washington University in St. Louis in 2014.

Email  /  Google Scholar  /  LinkedIn


Animation Generation with a Low-Dimensional Simplicial Complex
Brian Gauch and Richard Alan Peters
ICPRAI (International Conference on Pattern Recognition and Artificial Intelligence), 2018

Given motion capture training data, we control an animated character by reducing the dimensionality of the pose space, finding a simplicial complex using Delaunay triangulation, creating a motion graph using the simplicial complex, and then using the motion graph and simplicial complex for distance estimates and interpolation respectively. Using a simple pathfinding algorithm, we compare the above model to a simpler model where k-NN is used to induce a motion graph and interpolate.


Security Games on a Plane
Jiarui Gan, Bo An, Yevgeniy Vorobeychik, and Brian Gauch
AAAI Conference on Artificial Intelligence, 2017

Most existing models of Stackelberg security games ignore the underlying topology of the space in which targets and defence resources are located. As a result, allocation of resources is restricted to a discrete collection of exogenously defined targets. However, in many practical security settings, defense resources can be located on a continuous plane. Better defense solutions could therefore be potentially achieved by placing resources in a space outside of actual targets (e.g., between targets). To address this limitation, we propose a model called Security Game on a Plane (SGP) in which targets are distributed on a 2-dimensional plane, and security resources, to be allocated on the same plane, protect targets within a certain effective distance. We investigate the algorithmic aspects of SGP. We find that computing a strong Stackelberg equilibrium of an SGP is NP-hard even for zero-sum games, and these are inapproximable in general. On the positive side, we find an exact solution technique for general SGPs based on an existing approach, and develop a PTAS (polynomial-time approximation scheme) for zero-sum SGP to more fundamentally overcome the computational obstacle. Our experiments demonstrate the value of considering SGP and effectiveness of our algorithms.


Behavior Changes across Time and between Populations in Open-Ended Learning Environments
Brian Gauch, Gautam Biswas
International Conference on Intelligent Tutoring Systems, 2016

Open-ended computer-based learning environments (OELEs) can be powerful learning tools in that they help students develop effective self-regulated learning (SRL) and problem solving skills. In this study, middle school students used the SimSelf OELE to build causal models to learn about climate science. We study their learning and model building approaches by calculating a suite of behavioral metrics derived using coherence analysis (CA) that are used as features on which to group students by their type of learning behavior. We also analyze changes in these metrics over time, and compare these results to results from other studies with a different OELE to see determine generalizable their findings are across different OELE systems.


Studying Student Use of Self-Regulated Learning Tools in an Open-Ended Learning Environment
John Siler Kinnebrew, Brian Gauch, James Segedy, Gautam Biswas
International Conference on Artificial Intelligence in Education, 2016

This paper discusses a design-based research study that we conducted in a middle school science classroom to test the effectiveness of SimSelf, an open-ended learning environment for science learning. In particular, we evaluated two tools intended to help students develop and practice the important regulatory processes of planning and monitoring. Findings showed that students who used the supporting tools as intended demonstrated effective learning of the science topic. Conversely, students who did not use the tools effectively generally achieved minimal success at their learning tasks. Analysis of these results provides a framework for redesigning the environment and highlights areas for additional scaffolding and guidance.

Course Projects

Team Creation for League of Legends
Brian Gauch
CS6352: Human-Computer Interaction, 2015
Website  /  Study Results

League of Legends is a very popular video game played on PCs, in which groups of players form a “team” and two teams of the same size fight. Typically teams consist of 5 players. If a player does not yet have a team, it is possible to use in-game “matchmaking” to locate both an enemy team to fight, and the rest of a temporary friendly team. It is generally desirable to have a long-term team. However, there are a number of constraints that make it difficult to form a good team. Naturally, the team should speak the same language and be able to get along. Ideally, all the members of a team are online and willing to play League of Legends at the same time, and all members are roughly the same skill level. There are also some constraints and preferences specific to League of Legends. Because this problem is non-trivial, players have some difficulty self-organizing. Thus, this project developed a website that matches players into long-term teams using features like skill level and preferred game times.


CS6362 - Machine Learning - Fall 2018

CS3251 - Intermediate Software Design - Spring 2018

CS4260 - Artificial Intelligence - Fall 2017

CS2212 - Discrete Structures - Spring 2017

CS2212 - Discrete Structures - Fall 2016

CS1101 - Programming and Problem Solving - Summer 2016

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