Graduate School Newsblog

I began my PhD program in Fall 2020 in the Biomedical Engineering (BME) department under the guidance of Dr. Bruce Lee. Over the past five years, my research has focused on developing smart adhesives whose adhesion can be reversibly controlled through electrochemical approaches. With a background in biomedical engineering and materials science, I was excited to expand my knowledge in the development of novel materials.

From the moment I arrived in Houghton, I knew this was the place where I wanted to pursue my PhD. Over the last five years, I’ve had the opportunity to collaborate with brilliant students and principal investigators from multiple universities, present my work at conferences, and co-author cutting-edge journal articles. I was also captivated by the vibrant outdoor life here, which helped me develop a strong sense of work-life balance.

Through this journey, I’ve gained valuable skills and experiences that I will carry forward in my future career. I am deeply grateful to the Graduate Dean Awards Advisory Panel for awarding me this Finishing Fellowship, which provides crucial support as I complete my dissertation. I would like to sincerely thank my advisor, Dr. Bruce Lee, for his mentorship and for helping me grow as a researcher. I’m also thankful to my lab mates, the staff, and the faculty of the BME department for their continued support throughout my PhD program.

I look forward to making further contributions to the development of innovative materials in the years to come.

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.

From an early age, I was interested in automobiles and machines. So, when the time came to decide on which path to choose for my undergrad degree, I chose Mechanical Engineering without any doubt. That eventually led me to Michigan Tech to pursue a master’s degree in mechanical engineering. At Michigan Tech, I got the opportunity to work with Dr. Darrell Robinette on Connected Vehicle research as part of the Department of Energy’s ARPA-E NEXTCAR project. This project opened a whole new world of opportunities into the possibilities and the impact vehicle connectivity and automation could have on our daily lives. So, with some inspiration from Dr. Robinette and Dr. Jeffrey Naber, as my co-advisors, I decided to pursue a doctoral degree in Mechanical Engineering with a focus on leveraging vehicle connectivity and automation to improve energy efficiency of passenger vehicles.

On my PhD journey, I got the opportunity to work on the Department of Energy’s “Energy Optimization of Light and Heavy-Duty Vehicle Cohorts of Mixed Connectivity, Automation and Propulsion System Capabilities via Meshed V2V-V2I and Expanded Data Sharing” where we demonstrated on-track, the energy savings potential of multi-vehicle cohorts using connectivity and automation. I was also lucky to be part of the Department of Energy’s “Connected and Automated Vehicle Model Validation” project and the General Motors sponsored SAE AutoDrive Challenge II. Along with this, I also got the opportunity to spend six months as a Research Intern at General Motors’ Software Defined Vehicle Research Laboratory, where I worked on implementing perception systems and multi-object tracking algorithms for a simulation environment.

For my PhD research, I investigated the energy saving potential of various vehicle cohort combinations that could have while driving on a limited access highway, leveraging connectivity to optimize powertrain operation on various hybrid electric vehicles and train a neural network to use connected vehicle information to chart a course through an urban driving environment.

I want to use this opportunity to express my sincere gratitude towards Graduate School, dean and the Graduate Dean Awards Advisory Panel for awarding me the Finishing Fellowship for the Summer of 2025. This fellowship will greatly help me complete my doctoral research and extend my professional career. Along with that, I would like to thank the Department of Energy’s ARPA-E and Vehicle Technologies Office for providing support throughout my PhD along with General Motors and the Society of Automotive Engineers. I am also deeply grateful to my co-advisors, Dr. Darrell Robinette and Dr. Jeffrey Naber for their support and guidance throughout the years at Michigan Tech.

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.