Graduate School Newsblog

First, I am deeply grateful to receive this Doctoral Finishing Fellowship. It is both an honor and a recognition of my work over the past four years at MTU, and it motivates me to complete the final stages of my Ph.D. journey.

I began my Ph.D. in Fall 2021 under the guidance of Dr. Xiaoyong (Brain) Yuan in the Department of Computer Science. Inspired by the rapid development of AI and machine learning, my research focuses on the security and privacy challenges in machine learning systems—particularly in protecting user privacy. During my Ph.D., I have concentrated on privacy risks in collaborative inference, a widely used framework for deploying large neural networks. Our research investigates reconstruction attacks, where adversaries attempt to recover private inputs, such as facial or biometric data. Additionally, we explore privacy concerns in federated learning and aim to improve the efficiency of training models in distributed environments.

Finally, I would like to sincerely thank my advisor, Dr. Yuan, and our department chair, Dr. Wang, for their continuous support throughout my graduate studies. I also extend my appreciation to my committee members and the entire Computer Science Department at MTU. With your guidance, support, and encouragement, I am confident in successfully completing my Ph.D. program.

I started my Ph.D. in Chemistry at Michigan Technological University in the fall of 2021, marking the beginning of an exciting academic journey. Throughout my doctoral studies, I have focused on addressing critical challenges in the field of bioimaging through the development of novel fluorescent probes.

My research centers on designing and synthesizing specialized fluorescent compounds that allow for real-time monitoring of key biological parameters such as pH, NAD(P)H levels, and intracellular viscosity in living cells and organisms. These tools offer valuable insights into cellular function and disease progression, with particular relevance to cancer biology and mitochondrial health.

I am truly honored to receive the Doctoral Finishing Fellowship from the Graduate School. I would like to express my sincere thanks to the Graduate School and the Graduate Dean Awards Advisory Panel for this generous support. This fellowship will allow me to fully focus on completing my dissertation and preparing for the next steps in my research career.

I am especially grateful to my advisor, Dr. Haiying Liu, for his steady guidance and encouragement throughout my Ph.D. journey. I also deeply appreciate the support of my committee members, lab teammates, collaborators, and the faculty and staff of the Chemistry Department. A heartfelt thanks goes to my friends and peers at Michigan Tech, whose constant support made this journey both meaningful and memorable.

With this fellowship, I look forward to continuing my research and making a positive impact in the fields of healthcare and biomedical science.

I am a Ph.D. Candidate in Environmental Engineering in the Civil, Environmental, and Geospatial Engineering Department. I have always been intrigued by the interactions of humans with the environment. My research focuses on phosphorus transport and transformation in aquatic ecosystems in the context of the role of beaver dam-building in shaping hydrologic and biogeochemical cycles across North America. Working to understand the cumulative impact of beaver activity on sediment, phosphorus retention, and wetland formation. This work highlights how animal-driven landscape processes influence large-scale environmental systems—bridging ecology, environmental engineering, or human-ecology interactions.

Throughout my time at Michigan Tech, I have taught courses including Water Resources Engineering, Soil Mechanics for Engineers, and Environmental Process & Simulation labs. I’ve also worked as an Educational Scientist with the Center for Educational Outreach, supporting community education initiatives and teaching about lake ecology and the scientific process. These roles have deepened my passion for teaching and mentoring students across disciplines.

I have worked hard and received several awards for my work, including the Graduate Student Service Award, the Outstanding Graduate Student Teaching Award, and the David Hand Excellence in Lab Award. I also served as the Margaret A. Davidson Fellow for the Lake Superior National Estuarine Research Reserve, where I worked at the intersection of research and applied ecosystem management using numerical models for phosphorus transport.

I am very grateful and honored to receive the Doctoral Finishing Fellowship and would like to thank the Graduate School and the donors who make this fellowship possible. I would also like to thank my advisors, Brian Barkdoll and Xinyu Ye, and my committee, Amy Marcarelli, Cory McDonald, and Noel Urban, for their encouragement and guidance throughout this journey as I finish up my doctoral studies.

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 PhD journey began at Michigan Tech in 2020, amid a global pandemic. I was not aware back then, but moving to the Keweenaw proved to be the right decision given how little it was affected by COVID and how much I like winters (now). I became a Husky during that summer, working under Dr. Rüdiger Escobar-Wolf on a project funded by the National Science Foundation (NSF) focusing on lahars: volcanic hazards threatening communities near Volcán de Fuego in Guatemala. A few months later, Dr. Greg Waite joined the project as my co-supervisor, helping broaden the scope of this study.

For my PhD, I use an interdisciplinary approach to study lahars, which are destructive flows of volcanic debris. Over the past few years, I’ve collected monitoring data across various disciplines, including geophysics and hydrology. I completed field trips to Volcán de Fuego, collaborating with experts at INSIVUMEH (the local geophysical agency) and researchers from Boise State University. Using these datasets, I am characterizing lahar events and applying machine learning methods to develop detection systems that provide automatic alerts when these flows are active. In late 2023, I secured additional funding from the National Geographic Society, complementing the NSF support, to enhance hazard mitigation tools for communities in Guatemala, with potential applications in other volcanic settings around the globe.

I am grateful to the Graduate School and the Dean’s Advisory Panel for awarding me this fellowship. This work would not have been possible without the support and expertise of my co-supervisors, Drs. Escobar-Wolf and Waite. I also extend my gratitude to Dr. Luke Bowman for his invaluable mentorship, which has been crucial to the success of graduate students in the Department of Geological and Mining Engineering and Sciences (GMES), and to the faculty and staff at GMES for their continued support.

My experience at Michigan Tech began in 2013 as an undergraduate student. At that time, I was majoring in Electrical Engineering, but I quickly discovered my passion for programming and chose Computer Engineering as my second major. In my third year, I was fortunate to join the Human–Computer Interaction Lab and begin my research journey. Inspired by my undergraduate experiences, I determined that I wanted to further pursue research in computer science. In 2018, I officially began my Ph.D. in Computer Science with my advisor, Prof. Zhenlin Wang, and later, Prof. Junqiao Qiu joined as my co-advisor.

My doctoral research focuses on parallel computing, computer architecture, and systems. I am particularly interested in developing integrated systems that bridge hardware and software components. With the support of my advisors, I successfully accelerated a foundational computational task—finite automata processing—on the General Purpose Graphics Processing Unit (GPGPU) hardware acceleration platform. I am hopeful that my research contributions will inspire future advancements in the field and encourage further exploration by myself and fellow researchers. This journey has profoundly motivated me to continue challenging my limits.

I am honored to have been awarded the Finishing Fellowship, and I sincerely thank the Graduate School and the Graduate Dean Awards Advisory Panel. I am also deeply grateful to my advisors, Prof. Wang and Prof. Qiu; without their patient guidance and support, I would not have achieved what I have today. I also extend my thanks to the Michigan Tech community, which has provided me with a warm home to pursue both my academic and personal interests.