The Research Option offers students majoring in computer science the opportunity for a substantial, in-depth research experience. Students are strongly encouraged at the end of their experience to work with their faculty mentor to develop a journal publication or conference presentation on the research in addition to the actual thesis.

Recent Submissions

  • Establishing a Home Sensing Platform in the Field of Technological Healthcare 

    Link, Cooper
    This thesis explores how home sensor platforms can be leveraged in the context of care for chronic conditions. In order to understand the needs of such a system, a platform has been developed and deployed at the Georgia ...
  • Search and Rescue Dog Wearable and Mobile Interface 

    Liu, Yunqi
    Search and Rescue (SAR) dogs are important partners in SAR activities since their born talents in olfactory and auditory senses. Traditionally, the SAR dogs are usually released in the last known spot of the target person, ...
  • Investigating Sim-to-Real Transfer and Multi-Agent Learning in Assistive Gym 

    Schaffer, Holden C.
    As the world's population grows older on average and the number of available caregivers decreases, assistive robotics pose an opportunity for older adults or people with disabilities to continue receiving the care that ...
  • PeopleMap: NLP and Visualization Tool for Mapping Out Researchers 

    Saad-Falcon, Jon
    Discovering research expertise at universities can be a difficult task. Directories routinely become outdated, and few help in visually summarizing researchers' work or supporting the exploration of shared interests among ...
  • Learning Neural Networks That Can Sort 

    Dey, Arnab
    This thesis analyzes how neural networks can learn parallel sorting algorithms such as bitonic sorting networks. We discussed how neural networks perform at sorting when given no information or constraints about the allowable ...
  • Using Language Models in Causal Story Generation 

    Li, Siyan
    Story generation remains a challenge because it is still difficult to automatically generate logically coherent yet natural stories. In this thesis, we propose an approach to this problem by combining our previous pipeline ...
  • Enumerating Acyclic Orientations 

    Hathcock, Daniel Cullen
    An acyclic orientation (AO) of an undirected graph is an assignment of direction to each of its edges without introducing a directed cycle. We study enumeration problems regarding AOs. Our results include: an explicit ...
  • Automated Vulnerability Discovery in Botnet Command and Control Infrastructure 

    Asdar, Ehsan Muzaffar
    Systems infected with botnet malware often communicate with command and control (C&C) infrastructure, from which attackers can launch coordinated malicious attacks. Our research explores techniques for discovering ...
  • Deterministic Volume Approximation of Polytopes 

    Cristian, Rares
    Computing the volume of a polytope is an important longstudied question, with applications ranging from combinatorics to machine learning. While there are numerous randomized algorithms that efficiently approximate the ...
  • Penny – personal security assistant 

    Golod, Ilya
    This study presents Penny, a virtual assistant that monitors various parameters and conditions of the user’s machine and notifies them in case it senses a potential vulnerability. Penny also provides the user with ...
  • CHORUS is Porous: Attacking Implementations of Differential Privacy 

    Siva, Amaresh Ankit
    In this work I explore the vulnerability of CHORUS and FLEX, using a side- channel attack. CHORUS and FLEX are differentially private querying mecha- nisms jointly worked on by Uber and UC Berkeley. They aim to provide ...
  • Single-Job Dynamic Parallelism Scaling through Lock Contention Monitoring 

    Khanwalkar, Mahesh
    Harnessing available parallelism resources is an important but complicated task. Lock contention is one such factor that complicates this task and is of major concern, since locks and locking constructs are used heavily ...
  • Snow Coverage Prediction using Machine Learning Techniques 

    He, Ziming
    Snow coverage is often predicted through analysis of satellite images. Two of the most common satellites used for predictions are MODIS and Landsat. Unfortunately, snow coverage predictions are limited either by MODIS ...
  • Robot Calligraphy using Pseudospectral Optimal Control and a Simulated Brush Model 

    Chen, Jiaqi
    Chinese calligraphy is unique and has great artistic value but is difficult to master. In this paper, we make robots write calligraphy. Learning methods could teach robots to write, but may not be able to ...
  • Quantifying Gerrymandering using Markov Chain Monte Carlo Algorithms 

    Wahal, Samarth
    We look at the rules and regulations surrounding redistricting in the United State. We examine Markov Chain Monte Carlo algorithms that are able to sample redistricting plans adhering to these rules. We implement the ...
  • Evaluating Off-Center Head-Worn Display 

    Ramakrishnan, Rohan
    Several studies have highlighted the advantages of using mobile augmented reality systems to assist with various tasks over traditional paper-based methods. However, these interfaces are often located in users’ primary ...
  • Stranger Danger: Educational Game for Cybersecurity Awareness 

    Ren, Ziang
    Cybersecurity is a concern for both organizations and individual users. Although there are a variety of security tools available, the number of cybersecurity incidents is still high. One cause of this phenomenon is that ...
  • Semantic Mapping and Reasoning 

    Chen, Kevin Julian
    Rich, yet efficient knowledge processing is one of the key problems in modern autonomous robotics. The Robot Autonomy and Interactive Learning (RAIL) Lab at the Georgia Institute of Technology has developed a new knowledge ...
  • Improving Model-Predictive Control with Value Function Approximation 

    Chintalapudi, Sahit
    Existing Model Predictive Control methods rely on finite-horizon trajectories from the environment. Such methods are limited by the length of the samples because the robot cannot plan for scenarios beyond this time horizon. ...
  • On Formula Embeddings in Neural-Guided SAT Solving 

    Dumenci, Mert
    Branching heuristics determine the performance of search-based SAT solvers. We note that recently, Neural Machine Learning approaches have been proposed to learn such heuristics from data. The first step in learning a ...

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