b'On Human Hand ConfigurationsAlan M. BettisSponsor: Dr. R. Paul Mihail, Department of Computer ScienceThe human hand is complex, with many possible configurations (poses) spanning 27 degrees of freedom. This complexity is further increased by diseases such as rheumatoid arthritis (RA), which can cause deformities that lead to disease-specific poses outside of the normal configuration space. In this project, we use machine learning to map the manifold of natural human hand poses. We use the Leap Motion, a human-computer interaction (HCI) device, which uses infrared light to detect hand pose. Using dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE), the healthy human hand pose is visualized as a point in a low-dimensional space. This low-dimensional representation of hand poses can then be used as input to off-the-shelf machine learning algorithms, to identify poses that could predict RA or other musculoskeletal diseases of the hand.Estimating the Fatality Rate of Covid-19Jon Liu and Joyce LiuSponsor: Dr. Chunlei Liu, Department of Computer ScienceAs the coronavirus disease Covid-19 rages in China and spreads to other countries in the world, good estimations of the diseases epidemiological parameters are crucial to help governments, hospitals, businesses, and the general public to prepare for and to stop the spread of the disease. However, the diseases fatality rate, one of the most important parameters, is not calculated properly in many government websites and research papers and thus is misleading peoples understanding of the severity of the disease. In this study, we compare various definitions or methods to calculate the fatality rate and point out why they are misleading. We also propose a statistical model to analyze patients contraction, recovery, or death process and develop a method to estimate the fatality rate based on available disease statistics.47'