Tag: Statistics

I am deeply honored to receive the Finishing Fellowship Award from the Graduate School and the Graduate Dean Awards Advisory Panel. I sincerely thank the Department of Mathematical Sciences for the academic advising and continued support. I am especially grateful to my advisor, Dr. Yeonwoo Rho, for her unwavering encouragement and guidance throughout my doctoral program. I also extend my thanks to the faculty in the math department for their professional instruction and feedback throughout my coursework.

I have had the privilege of working under my advisor, Dr. Yeonwoo Rho, on functional data analysis. I would like to express my sincere gratitude to Dr. Rho for her invaluable guidance and mentorship, from shaping my research to encouraging my professional development. My research focuses on statistical methods for detecting change points in the mean or variance in high-dimensional or functional time series using random projections, with applications in finance, climate science, and biomedical science.

Alongside my research, I have worked as a Graduate Teaching Instructor for undergraduate math courses. I would like to thank my teaching supervisor, Ann Humes, who provided comprehensive training and helped me develop an instructional framework. I am also grateful to my teaching mentors, Tim Wagner and MaryFran Desrochers, for their guidance on effective teaching practices. These experiences have strengthened my commitment to high-quality instruction.

In addition, I have been proud to serve as the department representative in the graduate student government. I appreciate my colleagues for their participation and engagement, which have contributed to a supportive community. This service has also strengthened my leadership and collaboration skills.
I look forward to continuing to contribute through research, teaching, and service. This fellowship will provide crucial support as I complete my dissertation and prepare for the next stage of my academic career.

I am honored to receive the Finishing Fellowship Award from the Graduate School and the Graduate Dean’s Advisory Panel. This award grants me the time and focus needed to complete my dissertation. I would like to thank my advisor, Dr. Qiuying Sha, for her encouragement and invaluable guidance throughout this journey. Her advice and support have shaped me into an independent researcher and prepared me for new challenges. I also wish to express my gratitude to the Department of Mathematical Sciences, the departmental chair, Dr. Melissa Keranen, and the faculty and staff for their unwavering support.

My journey at Michigan Tech began in 2019 when I had the privilege of working under Dr. Qiuying Sha in Statistical Genetics. My research focuses on developing algorithms to test the associations between multiple phenotypes, genes, and genetic variants. Gene-based tests are vital in genetic research because they aggregate signals from multiple variants within a gene, enhancing the ability to detect associations between genes and complex traits. Similarly, multiple phenotype tests improve SNP discovery by leveraging the correlations among traits, allowing researchers to identify pleiotropic genetic effects and gain deeper biological insights.

I have been a Graduate Teaching Instructor in the Department of Mathematical Sciences. I genuinely appreciate the mentorship and support I received from my teaching mentors. I am deeply grateful to my teaching supervisor, Ann Humes, for her unwavering support, guidance, and encouragement throughout my teaching journey. Her mentorship has significantly shaped my growth as an educator and intensified my passion for serving students in academia. I aspire to pursue a career in academia, where I can serve students by sharing knowledge and fostering their growth. As part of my research goals, I aim to develop gene-based and multiple phenotype testing methods to advance the discovery of genetic associations and contribute to a deeper understanding of complex traits. I am eager to leverage the technical knowledge and leadership skills I have gained at Michigan Tech to further research in statistical genetics and functional genomics.

I am deeply honored to receive this Fellowship from the Graduate School and the Graduate Dean Awards Advisory Panel. My doctoral journey at Michigan Technological University began in 2019, when I entered the Ph.D. program in Statistics within the Department of Mathematical Sciences. Under the dedicated mentorship of my advisor, Dr. Kui Zhang, I have specialized in Bioinformatics and Statistical Genetics, particularly the integration of multi-omics data.

A central part of my work involves introducing Cross-Tissue Learner (CTL)—a novel multi-tissue transcriptome-wide association study (TWAS) framework that combines imputed gene-expression data from 49 tissues. We also developed an empirical distribution approach to refine the tissue-integration process, employing a unique weighting strategy to exclude irrelevant tissues. Building on this foundation, we introduced TWAS G-Boosted CTL, which incorporates GWAS-specific information, further enhancing statistical power across diverse datasets. In parallel, I have explored non-linear techniques (machine and deep learning) to improve predictive modeling across tissues. These methodologies promise transformative progress in bioinformatics, statistical genetics, and public health, enabling rapid gene discovery for disease risk assessment and therapeutic interventions.

I extend my sincere thanks to Dr. Qiuying Sha, Dr. Xiao Zhang, and Dr. Hairong Wei for serving on my advisory committee, as well as the Department of Mathematical Sciences’ faculty, chair, and staff for their unwavering support. Above all, I offer profound gratitude to Dr. Kui Zhang—his mentorship has shaped my capacity to design and execute rigorous research in Bioinformatics and Statistical Genetics. Thanks to this Fellowship, I can finalize my dissertation and devote greater attention to the broader implications of my work.

My long-term aspiration is to continue advancing computational genomics in a research-focused academic setting. I am honored to begin a two-year role as an Assistant Professor at Macalester College in St. Paul starting in Fall 2025. This position will allow me to refine my teaching, mentor students, and produce publications in bioinformatics and statistical genetics journals. Through this work, I aim to foster interdisciplinary collaborations that drive meaningful breakthroughs in genetic epidemiology, precision medicine, and biomedical data science.

My journey at Michigan Technological University began when I joined the masters program at Mathematical Sciences at Michigan Tech in 2019. I soon transitioned to the Ph.D. program in Statistics, where I have had the privilege to explore my passion for statistical modelling and analysis. My research focuses on computational statistics, particularly developing novel frameworks for factor analysis and dimensionality reduction, and model-based clustering. Currently, I am working on designing innovative algorithms that simultaneously cluster high-dimensional data and reduce its dimensions—tools with wide applications, from understanding complex biological systems to addressing societal challenges in climate and public health. Building on my advisor’s work, I have developed a fast sophisticated algorithm for performing clustering analysis along with data reduction. The algorithm, which will soon be available open-source, has been applied in the diagnosis and identification of the nature of breast tumors and has demonstrated impressive performance through rigorous simulation studies.

When not immersed in academic work, my time at Michigan Technological University has been enriched by engaging in sports and recreational activities. The outstanding sports facilities on campus have fueled my enthusiasm for swimming, a pastime that I now deeply enjoy. Additionally, I have actively participated in the annual broomball tournament during the winter season—a thrilling tradition that makes me eagerly anticipate the arrival of winter each year.
I am deeply grateful to the Graduate School and the Graduate Dean Awards Advisory Panel for this Fellowship. It will afford me the time and focus to wrap up my research and dissertation. I would like to especially appreciate my advisor, Dr Fan Dai, for her encouragement and invaluable mentorship throughout this journey. Her guidance has been instrumental in shaping me into an independent researcher, ready to take on the challenges of real-world data problems. I also extend my gratitude to my committee members (Dr Qiuying Sha, Dr Byung-Jun Kim and Dr Nathir Rawashdeh) for their support, the departmental chair (Dr Melissa Keranen), and the faculty and staff of the Department of Mathematical Sciences for their unflinching support so far.
As I approach the culmination of this phase of my academic journey, I look forward to applying the technical expertise and leadership skills I have honed here to advance statistical and machine learning methodologies and contribute to solving pressing global technology issues.

I am truly honored and deeply grateful to receive the Finishing Fellowship Award from the Graduate School and the Graduate Dean’s Advisory Panel. I would also like to express my sincere appreciation to the Department of Mathematical Sciences and my advisor, Prof. Kui Zhang, for their steadfast support throughout my doctoral journey.
My journey at Michigan Tech began in 2020, where I had the privilege of working under Prof. Kui Zhang in Statistical Genetics research. I am deeply appreciative of Prof. Zhang’s guidance and mentorship, which have been instrumental in shaping my academic path, fueling my research interests, and refining my critical thinking skills. My research focuses on developing novel statistical methods and creating efficient computational and bioinformatics tools to solve scientific problems in the biomedical research fields. In 2024, under the guidance of Prof. Kui Zhang on my initial project, I had the honor of receiving a prestigious $3,000, 12-month grant from the Blue Cross Blue Shield of Michigan Foundation. This funding supports my development of novel statistical methods for Type I diabetes research and recognizes my commitment to advancing genetic research, particularly in identifying genetic variants linked to diabetes.
In addition to my research experience at MTU, my teaching achievements are highlighted by the MTU Outstanding Graduate Student Teaching Award I received in 2023 and I was also being identified as one of only 80 instructors who received an exceptional “Average of 7 Dimensions” student evaluation score for Fall Semester 2023. Score was in the top 10% of similarly sized sections university-wide. The interactions and communications with my students are quite enjoyable and Engaging with my students has been a rewarding experience for both them and me. To encourage my students’ enthusiasm for learning Statistics, we worked together to write insightful statistical poems and design creative homework and projects that applied statistical concepts.