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Tag: Finishing Fellowship

Doctoral Finishing Fellowship – Fall 2023 Recipient – Hrishikesh Gosavi

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Since I began learning the basics of science, the effects of vibrations on environments has always fascinated me. As Nikola Tesla said, “If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.” It was with this aim that I started my Ph.D journey in Fall 2020.

My research has been in regards with “Metastructures”. These are unique structures which absorb vibrations in a system in particular frequency range, often called as ”bandgap”. It is because of this bandgap phenomenon that metastructures are widely used to mitigate vibration effects. Owing to large number of applications, it becomes important to estimate bandgaps in a metastructure to predict the frequency range in which the vibrations will be absorbed so that metastructures can be designed for various applications.

Through my research, I have aimed to developed new methods to estimate these bandgaps. The current methods available in the literature require a physics-based model of the metastructure (analytical model, finite/spectral element model) in order to estimate bandgaps. However, for various anisotropic materials, the material properties are difficult to quantify accurately which makes the physics-based model inaccurate. My research aims to overcome these limitations by developing methods which estimate bandgaps using purely experimental data. We have used the experimental data to study how a vibration wave is propagating through the metastructure (dispersion curve) and estimated bandgaps. Various other techniques such as substructuring, data-driven modeling algorithms were utilized. The developed techniques considerably reduced the design efforts required and made the entire design process much easier.

The funding provided by this fellowship will truly be helpful for me in putting all my energies in finishing my thesis in time and complete my Ph.D.!
I am grateful to the Graduate School for granting me this fellowship.
My advisor, Dr. Sriram Malladi has been more than helpful in guiding me through various ups and downs throughout my Ph.D journey. I am truly thankful for the relentless support he and his family has given me. Last but certainly not the least, I am thankful to my family i.e. my wife and my parents for their support in every aspect of my journey!

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Doctoral Finishing Fellowship – Fall 2023 Recipient – Susan Mathai

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I started as a PhD student in Atmospheric Sciences at Michigan Tech in August 2018. My interest in Atmospheric Sciences began during an elective course I took while pursuing my master’s degree in physics at the National Institute of Technology (NIT Calicut). Since then, my interest in Atmospheric Sciences has grown, and I have been eager to learn more about it.
My doctoral research focuses on investigating the physical, chemical, and optical properties of aerosols, which are particles suspended in the atmosphere, specifically those emitted from biomass burning. Over the course of five years, with the support of my advisor and colleagues at Michigan Tech, I have gained valuable knowledge and experience that will undoubtedly benefit me in my future endeavors. Additionally, I had the opportunity to expand my exposure and understanding of the subject through an internship at Pacific Northwest National Laboratory (PNNL), where I worked with Dr. Swarup China. During my internship, I estimated the optical properties of tar ball particles that are formed during biomass combustion. I also studied the physical and chemical properties of aerosols from an Urban polluted region that is highly influenced by biomass burning.
I am grateful to the Graduate Dean Awards Advisory panel for granting me the finishing fellowship award and to my advisor, Prof. Claudio Mazzoleni, for his unwavering support and guidance throughout my PhD journey. I also thank my mentor at PNNL, Dr. Swarup China for his hard work and dedication in helping me complete my PhD. Additionally, I express my thanks to both my current and former research group members for engaging in excellent discussions and fostering a spirit of teamwork. I eagerly anticipate defending my thesis and advancing along my chosen career path.

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Doctoral Finishing Fellowship – Fall 2023 Recipient – Mehnaz Tabassum

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Ever since my early days as an undergraduate student, I have been captivated by the potential of technology to revolutionize our daily lives. Michigan Technological University has provided an enriching environment for my research endeavors. The collaborative spirit among faculty members and the vibrant research community have fostered an environment for innovative ideas and cross-disciplinary collaborations. Engaging in stimulating discussions with brilliant minds and participating in cutting-edge projects have amplified my intellectual growth and fortified my passion for pushing the boundaries of knowledge in vehicular networking.

I am thrilled to share my remarkable journey as a doctoral candidate at Michigan Technological University. I started my PhD in Fall 2018 in the Electrical and Computer Engineering department. Throughout my doctoral journey, I have dedicated myself to unraveling the complexities of vehicular networking, exploring its intricacies one discovery at a time. By delving into areas such as intelligent transportation systems, vehicle-to-vehicle (V2V) communication, and infrastructure-to-vehicle (I2V) interactions, I aim to contribute to the seamless integration of vehicles into our evolving smart cities.

I am immensely grateful for the support of my advisor, Dr. Aurenice Oliveira, whose guidance, expertise, and unwavering encouragement have been instrumental in shaping my research trajectory.

To all aspiring researchers and technologists, I urge you to embrace your passions and fearlessly pursue your dreams.

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Doctoral Finishing Fellowship – Fall 2023 Recipient – Komal Chillar

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I joined the Ph.D.in Chemistry program at Michigan Tech in Fall 2018. Prior to this, I obtained a Bachelor’s degree in Chemistry from Miranda House, University of Delhi, New Delhi, India in 2016 and Master’s in Organic Chemistry from Maharshi Dayanand University, Rohtak, India in 2018. During the course of my Ph.D., I honed multiple skills needed for the organic laboratory work, and developed various interpersonal skills including communication, presentation, critical thinking, problem-solving, collaboration, leadership qualities and many more. These skills have not only contributed to my research success but have also shaped me into a confident and capable professional.

As an organic chemist, to accomplish the research objectives, I successfully synthesized various small and macromolecules that served as a monomer for oligodeoxynucleotides. This process involved the utilization of various instrumental analysis techniques. During my research, I focused on the synthesis of sensitive oligodeoxynucleotides under mild deprotection and cleavage conditions. Sensitive oligodeoxynucleotides are the DNA nucleosides that are unstable to harsh deprotection and cleavage conditions. The results of my work have been published in the New Journal of Chemistry in 2023. Furthermore, I developed a method for the direct quantification of the oligodeoxynucleotides using the HPLC peak area. This method not only eliminated the need for additional steps in quantification and purification but also saved valuable time for the researchers. The details of this method were published in PeerJ Analytical Chemistry in 2022. Additionally, I was able to achieve the 49 bases long oligodeoxynucleotides which could retain the sensitive groups under mild deprotection and cleavage conditions. These sensitive groups are believed to be the modifications present in the human genome resulting in disease-cause. The manuscript on this accomplishment is under review in a prestigious Peer-Reviewed Journal.

I would also like to express my sincere gratitude towards the Graduate Dean Awards Advisory Panel and the Dean for providing me the Doctoral Finishing Fellowship for Fall 2023. This fellowship will help me to focus on my research goals while accomplishing all the degree completion timelines, including writing and defending my dissertation to graduate timely. Finally, I would like to sincerely thank my advisor Dr. Shiyue Fang whose unwavering support, guidance, and mentorship have been invaluable throughout my Ph.D. journey to help me to expand my knowledge and professional growth in the field.

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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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Spring 2024 Finishing Fellowship Nominations Open

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Applications for Spring 2024 finishing fellowships are being accepted and are due no later than 4pm on October 18, 2023 to the Graduate School. Please email applications to gradschool@mtu.edu.

Instructions on the application and evaluation process are found online. Students are eligible if all of the following criteria are met:

  1. Must be a PhD student.
  2. Must expect to finish during the semester supported as a finishing fellow.
  3. Must have submitted no more than one previous application for a finishing fellowship.
  4. Must be eligible for candidacy (tuition charged at Research Mode rate) at the time of application.
  5. Must not hold a final oral examination (“defense”) prior to the start of the award semester.

Finishing Fellowships provide support to PhD candidates who are close to completing their degrees. These fellowships are available through the generosity of alumni and friends of the University. They are intended to recognize outstanding PhD candidates who are in need of financial support to finish their degrees and are also contributing to the attainment of goals outlined in The Michigan Tech Plan. The Graduate School anticipates funding up to ten fellowships with support ranging from $2000 to full support (stipend + tuition). Students who receive full support through a Finishing Fellowship may not accept any other employment. For example, students cannot be fully supported by a Finishing Fellowship and accept support as a GTA or GRA.

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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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