Hwai-Liang Tung

Hwai-Liang Tung is a dedicated researcher with a keen interest in statistical inference and MCMC algorithms. Currently affiliated with the Computer Science department at Rutgers University-New Brunswick, Tung has demonstrated a strong commitment to advancing the field of computational statistics. Their academic journey includes participation in the 2021 DIMACS REU program, where they collaborated on a project centered around statistical inference on infection processes over graphs. During the DIMACS REU program, Tung worked under the mentorship of Min Xu, focusing on enhancing an MCMC Metropolis-within-Gibbs algorithm. The primary goal was to improve the algorithm's ability to identify patient zero in infection scenarios, a critical aspect of understanding and controlling infectious diseases. Tung's approach involved a comprehensive understanding of the existing algorithm, engaging in discussions with their mentor to brainstorm potential improvements, and implementing novel algorithms to address identified challenges. Tung's research extended beyond algorithmic enhancements, as they also explored first passage percolation theory to gain deeper insights into the infection processes. This theoretical exploration was complemented by practical testing of the algorithm's performance, ensuring that the proposed improvements were both effective and efficient. Tung's dedication to their research was evident in their efforts to extend the algorithm's capabilities to handle noisy observations, a common issue in real-world data. Throughout the program, Tung demonstrated a strong ability to synthesize information from relevant literature, integrating new knowledge into their work. Their commitment to making the algorithm more scalable was a testament to their forward-thinking approach, recognizing the importance of adaptability in computational methods. Tung's project culminated in the successful implementation and testing of a new algorithm, which they presented in a comprehensive final report and presentation. Hwai-Liang Tung's work during the DIMACS REU program highlights their potential as a researcher in the field of statistical inference and MCMC algorithms. Their ability to tackle complex problems, coupled with a passion for innovation, positions them as a promising contributor to the advancement of computational statistics. As they continue their academic and research pursuits, Tung remains committed to exploring new methodologies and contributing to the broader scientific community.