Graduate School Newsblog

Tag: Awards

Doctoral Finishing Fellowship – Fall 2023 Recipient – Ponkrshnan Thiagarajan

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Growing up in a township full of scientists and engineers, I have always been curious about how things work. This led me to pursue a bachelor’s in engineering from Nehru Institute of Engineering and Technology affiliated with Anna University, Chennai. I then pursued a master’s from the esteemed Indian Institute of Technology, Madras where I delved into diverse research projects that captivated my interest. Fueled by this newfound interest, I started my journey as a Ph.D. student eager to tackle intriguing and fundamental challenges within the field of engineering.

I started working on my Ph.D. in the Fall of 2019 at the Computational Mechanics and Machine Learning Lab led by Dr. Susanta Ghosh at Michigan Tech. The focus of my research is on understanding the uncertainties associated with the predictions of computational and machine-learning models. Any model, computational or data-driven, is a representation of a physical phenomenon. We develop such models to understand the world around us better. However, predictions of such models are not always reliable due to the uncertainties associated with them. These uncertainties could arise for various reasons such as natural variability in the systems we study, assumptions in developing these models, numerical approximations, lack of data, etc. In order to use these models in real-life scenarios, quantifying these uncertainties is crucial. My research involves developing novel techniques to quantify the uncertainties, use these uncertainties to improve the model’s performance, and explain the reasoning behind the uncertainties. In my first project, we developed a Bayesian neural network-based machine-learning model that can reliably classify breast histopathology images into benign and malignant images. In addition, the model can quantify uncertainties associated with the predictions. We further developed techniques to explain the uncertainties and use them to further improve the model’s performance. In my second project, we developed novel loss functions for Bayesian neural networks and showed their advantages over the state-of-the-art in image classification problems. I am currently working on quantifying uncertainties in computational models that are used to characterize material behavior and extending the first two projects for several other applications.

I would like to thank my advisor Dr. Susanta Ghosh for giving me the opportunity to carry out this exciting research as well as for his immense help and guidance throughout the process. I thank the Graduate Dean Awards Advisory Panel and the dean for recommending me for this award. It is an honor. I thank the graduate school and the Department of Mechanical Engineering-Engineering Mechanics for their constant support.

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Doctoral Finishing Fellowship – Fall 2023 Recipient – Chen Zhao

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I started my Ph.D. journey at Michigan Tech in the fall of 2019 by joining the CS&E Ph.D. program at the Department of Applied Computing. Throughout my time at Michigan Tech, I have had the privilege of working at the Laboratory of Medical Imaging and Informatics under the guidance and supervision of Dr. Weihua Zhou. My Ph.D. research has been dedicated to the development of medical imaging analysis algorithms using deep learning techniques. Specifically, my research has focused on areas such as medical image segmentation utilizing prior knowledge, multiscale information fusion, and topology-based image semantic segmentation through graph neural networks. These algorithms and models that I have developed have been successfully applied to the analysis of coronary artery angiograms, contributing to computer-aided diagnosis and treatment of coronary artery disease.

I would like to express my sincere gratitude to the Department of Applied Computing for providing me with an exceptional research environment and the resources necessary for my research. I am especially grateful to the Graduate School and the Graduate Dean Awards Advisory Panel for recognizing my research efforts and granting me the Finishing Fellowship award. This award allows me to dedicate my time and efforts to the completion of my final research projects and the writing of my dissertation.

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Doctoral Finishing Fellowship – Fall 2023 Recipient – Hanrui Su

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In the fall of 2019, I embarked on my Ph.D. program in Environmental Engineering at MTU, working under the guidance of Dr. Yun Hang Hu. My research focus revolves around environmental pollution control technology, functional materials, and energy conversion systems. Throughout my doctoral journey, I have dedicated my efforts to developing an ultrafast alternative to the sluggish oxide ion transfers observed in conventional solid oxide fuel cells.
Our research endeavors led us to the discovery of a new type of fuel cell, known as a carbonate-superstructured solid fuel cell, which exhibits enhanced efficiency and performance by utilizing hydrocarbon fuel directly. This technology offers numerous advantages, including fuel flexibility, improved durability, and increased energy conversion efficiency at relatively lower operating temperatures. Presently, I am actively engaged in improving the fuel cell performance and exploring the underlying mechanisms. My goal is to contribute to the advancement of sustainable technologies that can shape a greener future and generate a positive impact on society.
I would like to express my sincere gratitude to the Graduate Dean Awards Advisory Panel for awarding me the finishing fellowship. This award will afford me the invaluable opportunity to dedicate my full attention to completing my dissertation and preparing for my defense. I am sincerely appreciating my advisor, Dr. Yun Hang Hu, whose invaluable guidance, conceptual insights, and technical expertise have been instrumental in shaping me into an independent researcher. I also extend my gratitude to my committee members, Dr. Miguel Levy, Dr. John Jaszczak, and Dr. Kazuya Tajiri, as well as my lab members, family, and friends, whose unwavering assistance and support have been integral to my success throughout my doctoral journey.

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Doctoral Finishing Fellowship – Fall 2023 Recipient – Tauseef Ibne Mamun

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I am proud to have been awarded the finishing fellowship for my Ph.D. at Michigan Technological University; my journey has shaped me into a versatile human factors specialist (human factors, in simple terms, involves bridging the gap between humans and field ‘X’ to make that field more accessible and user-friendly for humans) with expertise spanning artificial intelligence, autonomous vehicles, rail safety, and public health. Drawing on my computer science background, I have always been captivated by the advancement of powerful AI systems and their potential to become more accessible, trustworthy, and dependable for humans. My primary research focus centers around explainable artificial intelligence (XAI) and its significance in comprehending the cognitive dynamics between humans and AI in autonomous vehicles.

Beyond my dissertation on XAI and human-AI team cognition in autonomous vehicles, I have actively engaged in research within the transportation and health sectors. This active involvement has substantially enhanced my comprehension of the human factors associated with these domains.

The advent of commercially available AI systems in autonomous vehicles represents remarkable progress. However, similar to other state-of-the-art AI systems, understanding these new AI systems within the context of autonomous vehicles can pose challenges for both vehicle occupants and individuals outside the vehicle. Instead of solely concentrating on explaining ‘why’ AI systems have made specific decisions, I firmly hold the belief that providing explanations on ‘how’ AI systems ‘may behave’ in specific patterns can be more effective. By making these behavioral patterns more understandable for users and drivers, we can elevate human-AI team cognition. To address these research questions, I have adopted a mixed-method approach for my Ph. D. dissertation that combines simulated quantitative behavioral studies with cognitive task analysis methodologies.

I express my gratitude to the graduate dean awards advisory panel for selecting me as the recipient of the finishing fellowship. I am also deeply appreciative of the guidance and support provided by my mentors, Dr. Shane T. Mueller, and Dr. Elizabeth Veinott, as well as the other esteemed members of the Cognitive and Learning Sciences department at MTU. Their contributions have made Houghton feel like a second home to me. I would like to extend my gratitude to Dr. Robert Hoffman for his unwavering support throughout this journey. Finally, I would like to express my heartfelt appreciation to my wife, Dr. Lamia Alam, and my other family members for their unwavering support and understanding throughout the challenging phases of my Ph.D. journey. Their patience and encouragement have been invaluable to me.

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Doctoral Finishing Fellowship – Summer 2023 Recipient – Soheil Sepahyar

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I began my PhD journey in the spring semester of 2019, focusing on the subject of distance perception in virtual reality under the supervision of Dr. Scott Kuhl. My research investigates how people perceive distance in VR, an increasingly popular technology due to its widespread availability and recent advancements. I’ve always been interested in the Virtual Reality and Computer Graphics world since I was 12 years old.

Despite its growing popularity, numerous questions remain about how human perception interacts with virtual reality (VR). Many VR applications either require or benefit from users perceiving and interacting in virtual environments that closely resemble the real world. One of the primary challenges my research addresses is the tendency for people to underestimate distances in VR, as opposed to accurately perceiving them in real-world settings. Distances in VR are often reported as being underestimated by 20-30%, a discrepancy that is significant for many everyday tasks. These issues can lead to serious complications in various applications. For example, homebuyers using VR to virtually tour properties may struggle to accurately assess room sizes. People might also face difficulties in navigating and engaging with virtual worlds effectively. Furthermore, accurate distance perception is crucial for training and education programs involving students and even essential workers, such as astronauts. As a result, my research aims to examine how some of the procedural details might impact the results of previous VR studies regarding distance perception. One detail involves giving participants practice in blindfolded walking prior to the study to gain trust in the experimenter and experience walking while blindfolded. Additionally, to better understand this phenomenon, I have developed a program compatible with modern head-mounted displays (HMDs) that accurately tracks users’ locations and provides valuable data on participant behavior. This enables in-depth analysis of their walking behavior and perception during experiments.

I am extremely grateful to the Graduate Dean Awards Advisory Panel for granting me the finishing fellowship. I would also like to express my heartfelt thanks to my incredible advisor, Dr. Scott Kuhl, for his unwavering guidance, support, and encouragement throughout my PhD program. Finally, I extend my appreciation to the Computer Science Department and the College of Computing for their exceptional programs and the opportunities they have provided for us.

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Doctoral Finishing Fellowship – Summer 2023 Recipient – Roya Bagheri

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Growing up as a teenager, I always wanted to become a person who could help people around the world. I got the opportunity to start my academic life in health research, which brought me closer to what I have always wanted. As a mechanical engineer with a background in biomedical engineering and biomaterials, helping people and sharing the multidimensional point of view of these fields would be a fantastic opportunity to develop solutions for health-related problems worldwide.


I started my PhD in Mechanical Engineering in the Spring of 2020 at Michigan Technological University. Being part of the MTU family has been an exceptional experience for me. I am very fortunate to work in Dr. Abadi’s lab, who has guided me through research and several aspects of life. My research includes four different projects related to cardiovascular diseases and disorders (i.e., those related to the heart and blood vessels). Cardiovascular diseases are the leading cause of mortality worldwide. Nanomaterials, with their unique morphologies and properties, have great potential for advancing cardiovascular engineering to treat diseases and disorders. My research is in three main categories, ranging from tissue engineering to robotics and medical devices.


Despite being far from my hometown, I feel at home in Michigan Tech. I have had several opportunities to participate in different organizations and competitions. This incredible journey will always hold a special place in my heart. I am so glad that I am close to my dream of obtaining a PhD and being able to help people around the world on a small scale in health.

I am grateful to the Graduate School for awarding me this doctoral finishing fellowship; this fellowship means a lot to me and motivates me to work harder to finish my PhD journey! I am thankful to the people who have supported me on my journey.

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Doctoral Finishing Fellowship – Summer 2023 Recipient – Nazar Gora

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I joined a PhD program in Biochemistry and Molecular Biology at Michigan Technological University in the fall of 2020. My passion lies in the field of chemical biology, which involves using chemical tools to gain insights into the complex interactions between biological molecules. It is fascinating for me to explore the ways in which chemistry can be applied to understand and manipulate biological systems.


While working in Tanasova Lab, I have had the opportunity to develop a diverse set of skills. Starting with organic synthesis to produce molecular probes, I then progressed to performing bioassays and molecular modeling. The multidisciplinary nature of my work allowed me to gain valuable experience in various fields of study. My research focused on small molecule targeting of fructose transport in cancer. Facilitative glucose transporters (GLUTs) play a crucial role in delivering sugars to cells, and their dysregulation is linked to various disorders. In my work, I designed fluorescently labeled sugars to explore the involvement of different transporters in live mammalian cells. By implementing novel small molecules specific to fructose transport, we can develop better targeting strategies for metabolically deprived cancers. My research has the potential to advance our understanding of cancer sugar metabolism and improve our ability to employ sugar transport to undermine cancer.


I am grateful to the Graduate Dean Awards Advisory Panel for awarding me Finishing Fellowship, which provides me with the opportunity to complete my studies for the final research projects and focus on writing my thesis. I would like to express my sincere appreciation to my advisor, Dr. Marina Tanasova, and the Department of Chemistry at Michigan Tech for their support during my PhD journey.

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Doctoral Finishing Fellowship – Summer 2023 Recipient – Manpreet Boora

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I am honored to receive the finishing fellowship in my PhD from Michigan Tech University, and I am grateful for the opportunity to share my personal statement with the university’s blog. My fascination with the “why” and “how” behind everything led me to pursue Physics from an early age, and I have been fortunate to receive unwavering support from my family and friends throughout my academic journey. Being the first in my family to obtain a college degree is a milestone that I am proud of, and I am grateful for the opportunities that it has afforded me. As a Master’s student in Professor Jae Yong Suh’s lab, I developed an interest in studying chiral metamaterials using angle-resolved optical dispersions. This experience led me to pursue a PhD in the field of materials, particularly the synthesis, stacking, characterization, and study of the optical properties of 2D materials. During my doctoral studies, I had the privilege of acquiring advanced skills in cutting-edge tools and techniques, such as microfabrication, chemical vapor transport, and transfer of films with controlled twist angles. These experiences have been invaluable in shaping my research and personal growth. As an NSF-funded Resident Scholar Visitor at Penn State University’s Materials Research Institute, I was able to broaden my horizons by conducting high-end research in the field of 2D materials and forming collaborations with researchers from different backgrounds. This experience has enriched my research, provided me with diverse perspectives, and prepared me for a successful research career.

I am grateful to the graduate dean awards advisory panel for awarding me the finishing fellowship and to my department chair Dr. Ravindra Pandey for his unfailing support throughout my doctoral studies. I would also like to thank my advisor Dr. Jae Yong Suh for believing in me and fostering my personal and professional growth. Receiving the finishing fellowship is a testament to the hard work, dedication, and passion that I have poured into my research. It is a token of honor and I am excited to see where this journey takes me next.

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Doctoral Finishing Fellowship – Summer 2023 Recipient – Laura Vidal Chiesa

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I first joined MTU in Fall of 2017 as a Masters student in the Humanities Department. In Spring of 2019, I successfully defended my MS project and that following Fall semester, I rejoined the Rhetoric, Theory and Culture program as a PhD student.

In my dissertation, “The Unappreciated and Disposable Wife”: Liminality, Emotional Labor and Feminization in Graduate Student Writing Program Administrators”, I explore the systemic, structural, and rhetorical factors that contribute to the marginalization, feminization, and emotional labor burden required for graduate students in Writing Program Administrator positions (gWPAs). My study has the following goals: First, by interviewing fellow graduate students, I aim to document and compile stories of those who have experience in Writing Program Administration (WPA) in order to model what that experience looks like in the US college context. Second, to understand what emotional labor means in this kind of position and its implications for those involved. Third, to understand the conditions of the precarity of this kind of labor, particularly in the context of feminization. Ultimately, I suggest interventions that would make WPA work more equitable for graduate students who seek to gain administrative experience prior to graduation. This work draws from and contributes to scholarship in disciplines such as rhetoric and composition, feminism and gender studies, and organizational communication.

My experience at Michigan Tech has been quite a journey, and I wouldn’t be here if it wasn’t for my advisor, Dr. Marika Seigel, who helped me realize what my true passion was: teaching. I would also like to thank the rest of my committee, Dr. Patricia Sotirin, Dr. Laura K. Fiss & Dr. Laura R. Micciche, your feedback has been essential for my progress. This PhD degree wouldn’t have been possible without the support and encouragement of my family, my friends (in Houghton, Montevideo & Argentina), and my partner Kevin, thank you for being there unconditionally.

Last but not least, I am extremely grateful to the Graduate Dean Awards Advisory Panel and the Graduate School for awarding me the Finishing Fellowship during the final period of completing and defending my dissertation.

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Doctoral Finishing Fellowship – Summer 2023 Recipient – Evan Lucas

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I began my PhD journey in Fall 2019 in the Electrical and Computer Engineering (ECE) department studying underwater acoustic communication systems. After taking a machine learning course, I decided I wanted to make machine learning and artificial intelligence a larger focus of my studies and ultimately joined a project focused on natural language processing (NLP) technologies for the summarization of dialogue.

My main dissertation chapters primarily focus on text summarization (how to concisely and accurately represent a large body of text with a small one) and segmentation (how to split up long chunks of dialogue into smaller ones). In my favorite chapter contained in the dissertation, I propose a text segmentation metric that goes beyond current segmentation metrics by scoring a segmentation set without requiring a human to provide a reference, which is currently required by all existing segmentation metrics. I continued this segmentation work by considering the case of fuzzy text segmentation, where the boundaries between segments are no longer solid and a sentence within a document can belong partly to multiple segments.

The papers on summarization are still in preparation, with one discussing a small model architecture modification to improve summarization quality and another exploring methods of demonstrating summarization model ownership by adding watermarks to generated content. As language models become more widely used, concerns about their use will continue to grow; one solution to this is to have ways of detecting or proving that the origin of text comes from a language model. In addition to these papers, I have also published on a technique for using limited human feedback in the form of a binary good/bad response to help improve model performance for classification models that contain more than two classes.

I would like to thank my advisor, Dr. Timothy Havens, for his support, encouragement, and guidance throughout my PhD. The last two years of my work would also not have been possible without the support of Bob Friday and Paul Fulton from Visionyze; they have funded my research up until this point and collaborated with me on several projects. I’d also like to thank my committee and the ECE department for their support along the way. Finally, I am incredibly grateful to the Graduate School as well as the Graduate School Awards Advisory Panel for awarding me this fellowship, which will help me complete my dissertation and finish publishing the last papers of my PhD.

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