Tag: Finishing Fellowship

I am deeply honored to receive the Doctoral Finishing Fellowship and would like to extend my sincere gratitude to the Graduate School and the Graduate Dean Awards Advisory Panel for this recognition. I am especially thankful to my advisors, Dr. Darrel Robinette, and Dr. Jeff Naber, for their guidance, mentorship, and unwavering support throughout my Ph.D. journey. I also appreciate the support of the Mechanical Engineering–Engineering Mechanics Department and APS Labs, whose resources and collaborative environment have played a vital role in my research development.

My Ph.D. journey at Michigan Technological University began in Fall 2021 in the Department of Mechanical Engineering–Engineering Mechanics, where I have focused on advancing intelligent and energy-efficient connected and automated vehicle (CAV) systems. It was a wonderful experience to be part of the U.S. Department of Energy ARPA-E NEXTCAR II project, targeting 30% energy savings for PHEV light-duty connected and automated vehicles. My research integrates advanced perception, real-time vehicle modeling, and control strategies to improve the performance and sustainability of electric and plug-in hybrid electric vehicles (EVs/PHEVs) in dynamic driving environments.

A major focus of my work has been the development of dynamic mass and road load learning algorithms for EV and PHEV platforms, achieving estimation accuracy within 3%. I have also implemented automated vehicle following strategies that leverage advanced perception systems, resulting in energy savings of up to 15%. Additionally, my work on optimizing vehicle approach and departure strategies at signalized intersections has demonstrated energy savings of up to 35% per signal, contributing significantly to energy-efficient urban mobility.

As I enter the final phase of my doctoral studies, I look forward to completing my research and pursuing a career at the intersection of intelligent transportation, energy systems, and autonomy—where I aim to contribute to sustainable and transformative solutions in mobility.

I am profoundly grateful to the Graduate Dean Advisory Panel for honoring me with the Doctoral Finishing Fellowship Award. This recognition represents not only acknowledgment of my research contributions but also provides essential support during this critical phase of my academic journey, enabling me to fully dedicate my efforts toward finalizing research that addresses critical safety challenges in high-risk industrial environments.

My academic journey began in the field of mining engineering. Throughout my professional development, I recognized the transformative impact of modern digital technologies on engineering disciplines. This insight highlighted the transformative potential of computational approaches in addressing complex engineering challenges, prompting me to expand my expertise through a master’s degree in computer science in 2022. Acquiring this interdisciplinary skill set deepened my interest in developing computational solutions for complex engineering problems through my research at the National Center of Artificial Intelligence.

To further strengthen my academic foundation and pursue pioneering research in this emerging field, I enrolled in the Ph.D. program in the Department of Geological and Mining Engineering and Sciences at Michigan Tech in August 2022. Under the expert guidance of Dr. Snehamoy Chatterjee, I have been able to pursue advanced research at the intersection of artificial intelligence and mining engineering.

My dissertation focuses on developing specialized large language models designed to comprehend and interpret safety-specific language. This work addresses a significant gap in occupational health and safety management, where current approaches often fail to fully capture the nuanced language present in safety documentation and incident analyses. By applying Large Language Models to safety domains, my research aims to enhance risk assessment protocols, incident analysis methodologies, and hazard identification processes across various industrial sectors.

I extend my sincere gratitude to the Graduate Dean Award Advisory Panel for granting me this Finishing Fellowship. This support enables me to dedicate my full attention to completing my dissertation and advancing the practical applications of my research findings. I am particularly appreciative of Dr. Chatterjee’s mentorship throughout my doctoral studies, as well as the comprehensive support provided by the faculty and staff of the Department of Geological and Mining Engineering and Sciences.

As I conclude this phase of my academic journey, I anticipate continuing to explore the integration of artificial intelligence with engineering practices to enhance workplace safety and operational efficiency in industrial environments.

I began my PhD journey at Michigan Technological University with a desire to make healthcare more accessible through low-cost, reliable diagnostics. My research focuses on developing molecularly imprinted polymer (MIP) biosensors—customizable, shelf-stable devices designed as alternatives to traditional antibody-based systems. By tailoring the polymerization conditions and integrating nanomaterials, I aim to enhance the sensitivity and stability of these sensors for use in point-of-care applications.

This work is driven by the belief that everyone deserves access to timely medical testing. MIP sensors hold great promise for use in underserved areas, where refrigeration or specialized lab equipment may not be available. Their potential to address healthcare gaps in remote or low-resource settings is one of the most exciting parts of my research.

During my time at Michigan Tech, I have published two articles on MIP-based cortisol and lactate sensors, with a third currently pending publication. I’ve had the honor of receiving the Health Research Institute Fellowship and several presentation awards. Beyond research, I’ve served as a Graduate Student Government representative, helping organize events such as the Alumni Poster Session, 3-Minute Thesis, and Graduate Research Colloquium—experiences that have strengthened my commitment to supporting the graduate student community.

One of the highlights of my time at MTU has been serving as a teaching assistant and mentoring students in our lab. These experiences have inspired me to pursue a future career in academia, where I hope to foster curiosity and hands-on learning in the next generation of engineers.

I am sincerely grateful to the Graduate School and the Graduate Dean Awards Advisory Panel for this fellowship. I’d also like to thank my advisor, Dr. Yixin Liu, the Chemical Engineering Department, and my friends and family for their unwavering support.

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.