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@article{URS,
  abbr={COMP},
  title={An Analysis on Imbalance Workload Pattern in the Cloud Cluster Trace},
  author={<NAME>., & <NAME>.},
  abstract={Comparing trace publicly released from Google Borg in 2011 and 2019, this research was conducted to observe imbalanced workload patterns that can be described via clustering, anomaly detection, and other data mining methods. Corporates and industries are often overwhelmed by large datasets, while researchers in academics are often confronted with a lack of applicable resources. The trace analysis enables researchers to explore patterns underlined on scheduling workloads in large-scale compute clusters and discover any potential adjustments that may need to increase further efficiency. Inspired by the paper "Borg: The Next Generation" which solely focus on Borg's performance, the goal of this research is to complete documentation assessing the reproducibility of data analysis on Borg traces, analyze workload patterns with multiple data mining technique, and observe characterization and improvements made of new production traces being released by cloud providers, such as Microsoft Azure and Alibaba.},
  journal={Manuscript in preparation,},
  year={2022},
  month={Aprl}
}
@article{Fraud,
  abbr={STAT},
  title={Transaction Fraud Detection using Random Forest and Logistic Regression Methods},
  author={<NAME>., & <NAME>.},
  abstract={Since the explosion of wire transactions and online banking, financial fraud has been on the rise. According to a report by TransUnion, a consumer credit reporting agency, global digital fraud attempts in the financial services sector have risen 149% in the first four months of 2021 compared to the last four months of 2020. In the U.S., digital fraud attempts have risen 109% in the first four months of 2021. Furthermore, a recent study from Juniper Research stated that businesses in eCommerce, airline ticketing, money transfer, and banking services would have an expected loss over $200 billion between 2020 and 2024. In addition, Juniper Research found that digital money transfer fraud is growing, especially in emerging markets. Due to advances in technology, fraudsters have increased their avenues of attempting fraud as well as increased sophistication in their attempts. As the financial services industry continues to find new ways to interact with consumers, fraud analytics is an increasing area of importance. Our study is designed to explore transaction fraud in the context of banking services by promoting a statistical model that can make binary decisions on whether transactions are fraudulent based on key characteristics.},
  journal={Unpublished manuscript,},
  pdf={fraud.pdf},
  year={2021},
  month={Dec}
}
@article{Insurance,
  abbr={STAT},
  title={A Predictive Model for Personal Medical Insurance Charge},
  author={<NAME>., <NAME> & <NAME>.},
  abstract={Medical insurance is an essential part of people’s self-protection plan as it covers a person’s medical expenses for illness or injury. However, various factors of each individual may affect their medical insurance charges, such as age, body condition, and more. We constructed a prediction model to estimate insurance charges while providing more insights into the effect of each factor. This project utilized an insurance dataset obtained from the book Machine Learning with R by <NAME>. We analyzed the data using JMP to examine the relationship between features indicating individual characteristics and the corresponding health insurance charges.},
  journal={Unpublished presentation,},
  pdf={insurance.pdf},
  year={2021},
  month={Dec}
}
@article{SRE,
  abbr={STAT},
  title={Data Simulation and Evaluation of Variable Selection Methods using Prediction Performance and Estimation Metrics},
  author={<NAME>., <NAME>., <NAME>., <NAME>., & <NAME>.},
  abstract={We ran a simulation study with the goal of assessing the performance of four regression methods: forward selection, ridge regression, lasso regression, and principal component regression. We chose to assess the quality of these methods using prediction, estimation, and variable selection metrics. We generated the data under the four standard linear regression assumptions, as well as in four additional situations in which each assumption was violated. We also generated the data under a variety of parameters in order to compare how the methods perform in different situations. We focused on 3 main cases for analysis: 1) The difference between the standard and violated assumptions when the sample size and number of covariates are held constant. 2) The difference between 4 combinations of sample size and number of covariates when the other parameters are held constant and the assumptions are not violated. 3) The different combinations of pairwise covariate correlation and size of coefficients when the sample size and number of covariates was held constant and the assumptions were not violated.},
  Journal={Unpublished manuscript,},
  year={2021},
  month={Aug},
  pdf={SRE.pdf},
  selected={true}
}
@article{DeepFake,
  abbr={COMP},
  title={Employing Ensemble Learning Techniques to Create a Modular and Updatable DeepFake Detection Model},
  author={<NAME>., & <NAME>.},
  abstract={Since the advent of Generative Adversarial Networks (GANs), the DeepFake community has exploded in popularity and has become a point of contention in politics and society. The increase in activity has spawned many generators based on GAN (ProGAN, StyleGAN, BigGAN, etc.), which are well documented and easy to use. As a result, most of the research on DeepFake detection has jumped straight to Deep Neural Networks (DNNs). This approach allows them to make monolithic all-in-one models to detect forgeries of any generator, but often come with significant downsides. We propose, instead, to take a more modular approach, using techniques borrowed from Ensemble Learning. By having dedicated models trained to the output of individual generators, we can effectively use patterns left by individual generators that are not utilized in DNNs. Then, this data can be aggregated by vote, and effectively capture overarching patterns as well as minutiae. Our goal is to evaluate whether such a system can be more efficient in training as compared with DNNs, while also being as accurate.},
  Journal={Unpublished manuscript,},
  year={2020},
  month={May},
  pdf={DeepFake.pdf},
  selected={true}
}
@article{Chi21,
  abbr={COMP},
  title={Automatically Grading Rey-Osterrieth Complex Figure Tests using Sketch Recognition},
  author={Lara-<NAME>., <NAME>., <NAME>., <NAME>., <NAME>., & <NAME>.},
  abstract={The Rey-Osterrieth Complex Figure Test (ROCF) is among the most widely used neuropsychological examinations to analyze visual spatial constructional ability and memory skills, but grading the patient’s sketched complex figure is subjective in nature and can be time consuming. With increasing demand for tools to help detect cognitive decline, there is a need to leverage sketch recognition research to assist in detecting fine details within an ROCF’s inherently abstract figure. We present a series of recognition algorithms to detect all 18 official ROCF details using a top-down sub-shape recognition approach. This automated grader transforms a sketch into an undirected graph, identifies and isolates detail sub-shapes, and validates sub-shape neatness via a point-density matrix template matcher. Experimental results from hand-drawn ROCFs confirm that our approach can automatically grade ROCF Tests on the same 18-item sketch detail checklist used by neuro-psychologists with marginal error margin.},
  Journal={Unpublished manuscript,},
  year={2020},
  month={May},
  pdf={ROCF.pdf},
  selected={true}
}