Research ProjectsAs a researcher, I am always involved in projects trying to push the bounds of modern computer science and mathematics in order to discover the next frontier. Below is a list of all the research projects in which I have participated. Since most people prefer to see the more recent works, my current projects are listed first followed by my past research projects.
Current ResearchBelow is a list of projects in which I am currently involved. Following each name is a brief description of the project as well as a list of other people involved. Related materials such as publications and posters will be listed with the associated projects as well.
Generalized Plan and Activity Recognition ModelsMany plan, activity, and intent recognition (PAIR) models have been developed for specific problem domains such as recommender systems and personal desktop assistants. While these models perform extremely well for thier specific problem, they are not necessarily designed to work in other PAIR tasks outside the prescribed domain. We focus on developing integrated plan and activity recognition models applicable to a variety of domains.
Integrated Planning and Plan RecognitionWe are extending a previously established transformation from a plan recognition problem over STRIPS domains to a classical planning problem (Ramírez and Geffner, 2010). In particular, we are adapting the method to real-time applications and integrating the process with planning for a simple interactive system.
Past ResearchBelow is a list of projects in which I was involved at some point. These entries may no longer be up to date.
Learning Physical Therapy by DemonstrationWe are developing a framework for teletherapy where robots learn from physical therapists by demonstration. This will allow remote therapy to be performed with less frequent therapist supervision so that patients receive more care.
Restating the Collatz ConjectureIn mathematics, there are many problems that remain unsolved regardless of how simple they look. One example of such an unsolved problem is the Collatz Conjecture, also called the 3x+1 Problem amongst other names. The simple question is whether or not the following process will always generate a sequence of integers that contains 1 within a finite number of steps:
Using Natural Language Generation to Develop Assistive TechnologiesUsing natural language generation and other artificial intelligence techniques, we are developing assistive technologies for mobile devices to facilitate communication tasks for people with disabilities.
Improving MAP Estimation Using Linear Programming and Quadratic Programming RelaxationWe are developing a new Maximum-a-Posteriori (MAP) estimation algorithm to further reduce/remove the error from current approximation methods. MAP is a method for learning the assignments of variables in a Markov Random Field (MRF) by finding the assignment that maximizes the likelihood of the MRF.
Anytime Lifted Belief Propagation (ALBP)Belief propagation is an inference technique that, for a factor graph representation of a world with evidence, can determine the marginal likelihoods of each variable's value assignments. However, for such domains as decision making, one only needs to know the marginal likelihoods for the assignment of a subset of the variables in the factor graph. ALBP accomplishes this by combining theorem proving techniques with lifted inference. I developed the first implementation of ALBP which we have used to empirically test the algorithm.
Improving Error Correction in Compilers Using Natural Language Processing TechniquesCompilers translate human-written program code into machine-executable programs. However, it cannot complete the translation when there are errors in the code. We proposed a method employing natural language techniques to make the compiler develop a more semantic interpretation of the code. The intent was to improve error correction during the compilation process.
Computer Science Undergraduate Honors Thesis Advisor: Dr. Todd Torgersen
Measuring the SRB Entropy of Variations of the Expanding Map on the CircleThe expanding map on the circle may be visualized as follows: wrap an elastic band around a circle whose circumference is the length of the unstretched band, mark a point on the elastic band, unwrap the band and stretch it by some factor k > 1, rewrap the stretched band around the circle, and note where the marked point is now located on the circle. This process is repeated indefinitely with the next mark at the location of the point after rewrapping the band in the previous iteration. The change in the distribution of all points along the circle is a chaotic discrete dynamical system, and we use entropy to measure how chaotic these systems are from different observed perspectives. The SRB entropy measures chaos through the physical measure which is how we view it in the real world. Due to the intractable computations involved, we use numerical methods to approximate the entropy of the expanding map on the circle when k=2 and we do not uniformly stretch the band (that is, some parts are stretched more and others are strectched less). This work has generated some hypotheses regarding how the variations in the non-uniform stretching affect the SRB entropy.
Mathematics Undergraduate Honors Thesis Advisor: Dr. Miaohua Jiang
RogoSome students learn better from hands-on experience with a physical component. Since most programming courses teach languages such as Java, Python, or C/C++, the physical component of the students' hands-on experience is missing. To bring the results of their programs into the real world, we are developing Rogo, an introductory programming language for the iRobot Create. Inspired by such educational programming languages as Logo the Turtle and Carol the Robot, Rogo will provide very simple commands for controlling the Create including navigation, sound, light, and sensor reading. Students will be able to create more complex functions by combining the commands which will also introduce them to computational thinking (for example, three left turns is one right turn).
Mentors: Dr. William Turkett
Beta Testers: Amanda Chou (Lead Tester) and David Hughes
Related Events: Robotics Demo Day (photographs may be viewed at WFU's CS Google+ page or at WFU's CS Facebook page)
Cardio ViewCardioView is the name of a mobile app informing students about the availability of equipment in the on-campus gym at Wake Forest University. It was created by Rachel Black (now at Booz Allen Hamilton) and me for an independent study course under Dr. Daniel Cañas where we each focused on a different component. Rachel developed the front-end application and interface for the iPhone and continued this component after the semester for her senior honors project. I worked on the back-end reading data from sensors placed on the equipment and using the data to learn a simple classifier for whether or not the corresponding gym equipment was in use.
Project partners: Rachel Black
Mentors: Dr. Daniel Cañas
Multiagent Random Walks along a TorusCyber attacks on networks are dangerous since the malicious software can transport between servers and spread the damage. Thus it is important to locate malicious activity as soon as possible in order to avoid further harm. Malware checks on individual servers are computationally intensive and take a long time which makes them less ideal. A new nature-inspired approach to handling malware search over networks is a swarm multi-agent system called digital ants. I helped with some simple analyses of the effectiveness and efficiency of the digital ants along a torus-shaped network topology.
Mentors: Dr. Errin Fulp
The Information on Communication and Behavioral Study (ICBS)ICBS was a game theoretic study in which human subjects were given short weekly quizzes based on a provided website link. Participants were sent a link to one of two websites containing information about current trends. The goal was to determine how people's behavior was influenced based on the information they were provided. I developed the sign-up website and a system to autonomously carry out the study.
Mentors: Dr. Fred Chen