JURSW Volume 7
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/230
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Browsing JURSW Volume 7 by Author "Han, David"
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Item Optimal Dynamic Treatment Regime by Reinforcement Learning in Clinical Medicine(UTSA Office of Undergraduate Research, 2020-12) Song, Mina; Han, DavidPrecision medicine allows personalized treatment regime for patients with distinct clinical history and characteristics. Dynamic treatment regime implements a reinforcement learning algorithm to produce the optimal personalized treatment regime in clinical medicine. The reinforcement learning method is applicable when an agent takes action in response to the changing environment over time. Q-learning is one of the popular methods to develop the optimal dynamic treatment regime by fitting linear outcome models in a recursive fashion. Despite its ease of implementation and interpretation for domain experts, Q-learning has a certain limitation due to the risk of misspecification of the linear outcome model. Recently, more robust algorithms to the model misspecification have been developed. For example, the inverse probability weighted estimator overcomes the aforementioned problem by using a nonparametric model with different weights assigned to the observed outcomes for estimating the mean outcome. On the other hand, the augmented inverse probability weighted estimator combines information from both the propensity model and the mean outcome model. The current statistical methods for producing the optimal dynamic treatment regime however allow only a binary action space. In clinical practice, some combinations of treatment regime are required, giving rise to a multi-dimensional action space. This study develops and demonstrates a practical way to accommodate a multi-level action space, utilizing currently available computational methods for the practice of precision medicine.Item Predicting the Next Big Impact: Modelling the Rate of Massive Meteorite Strikes(UTSA Office of Undergraduate Research, 2020-12) Woods, Ethan; Han, DavidMeteorites are solid pieces of debris from an astronomical object such as a comet, asteroid, or meteoroid that originates in outer space and survives its passage through the atmosphere to reach the surface of a planet. Although rare, a collision between massive astronomical objects, known as an impact event, can have measurable effects, and physical and biospheric consequences. In this work, we investigate the distributional trend of heavy meteorites that strike the earth and determine if any probability distributions can serve as effective predictive models. NASA meteorite data from 1980 to 2012 were imported into R after pre-processing. Pre-processing activities involved the following: removal of missing data, irrelevant features to meteorite mass or the year of meteorite impact. Statistical analysis was then restricted to meteorites at or above the 98th percentile of mass. It was found that while the distribution of mass for all meteorites is lognormal, the distribution for the top 2% is severely right-skewed, indicating that an extreme-value distribution could be used to model them. Furthermore, the rate of impact for these massive meteorites can be modelled with a zero-inflated negative binomial distribution.Item Quantum Computation, Quantum Algorithms and Implications on Data Science(UTSA Office of Undergraduate Research, 2020-12) Kim, Nathan; Garcia, Jeremy; Han, DavidQuantum computing is a new revolutionary computing paradigm, first theorized in 1981. It is based on quantum physics and quantum mechanics, which are fundamentally stochastic in nature with inherent randomness and uncertainty. The power of quantum computing relies on three properties of a quantum bit: superposition, entanglement, and interference. Quantum algorithms are described by the quantum circuits, and they are expected to solve decision problems, functional problems, oracular problems, sampling tasks and optimization problems so much faster than the classical silicon-based computers. They are expected to have a tremendous impact on the current Big Data technology, machine learning and artificial intelligence. Despite the theoretical and physical advancements, there are still several technological barriers for successful applications of quantum computation. In this work, we review the current state of quantum computation and quantum algorithms, and discuss their implications on the practice of Data Science in the near future. There is no doubt that quantum computing will accelerate the process of scientific discoveries and industrial advancements, having a transformative impact on our society.Item Statistical Perspectives in Teaching Deep Learning from Fundamentals to Applications(UTSA Office of Undergraduate Research, 2020-12) Kim, Nathan; Han, DavidThe use of Artificial Intelligence, machine learning and deep learning have gained a lot of attention and become increasingly popular in many areas of application. Historically machine learning and theory had strong connections to statistics; however, the current deep learning context is mostly in computer science perspectives and lacks statistical perspectives. In this work, we address this research gap and discuss how to teach deep learning to the next generation of statisticians. We first describe some backgrounds and how to get motivated. We discuss different terminologies in computer science and statistics, and how deep learning procedures work without getting into mathematics. In response to a question regarding what to teach, we address organizing deep learning contents and focus on the statistician’s view; form basic statistical understandings of the neural networks to the latest hot topics on uncertainty quantifications for prediction of deep learning, which has been studied in the Bayesian frameworks. Further, we discuss how to choose computational environments and help develop programming skills for the students. We also discuss how to develop homework incorporating the idea of experimental design. Finally, we discuss how to expose students to the domain knowledge and help to build multi-discipline collaborations.Item Stochastic SIR-based Examination of the Policy Effects on the COVID-19 Spread in the U.S. States(UTSA Office of Undergraduate Research, 2020-12) Song, Mina; Belle, Macy K.; Medlovitz, Aaron; Han, DavidSince the global outbreak of the novel COVID-19, many research groups have studied the epidemiology of the virus for short-term forecasts and to formulate the effective disease containment and mitigation strategies. The major challenge lies in the proper assessment of epidemiological parameters over time and of how they are modulated by the effect of any publicly announced interventions. Here we attempt to examine and quantify the effects of various (legal) policies/orders in place to mandate social distancing and to flatten the curve in each of the U.S. states. Through Bayesian inference on the stochastic SIR models of the virus spread, the effectiveness of each policy on reducing the magnitude of the growth rate of new infections is investigated statistically. This will inform the public and policymakers, and help them understand the most effective actions to fight against the current and future pandemics. It will aid the policy-makers to respond more rapidly (select, tighten, and/or loosen appropriate measures) to stop/mitigate the pandemic early on.