Graduate School Newsblog

Tag: Computational Science and Engineering

Finishing Fellowship Award – Spring 2026 – Ali Awad

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Ali Awad
Ali Awad, PhD in Computational Science & Engineering, 2026

Ali Awad is currently pursuing a Ph.D. in Computational Science and Engineering at Michigan Technological University (MTU) in the United States. He earned his M.S. in Computer Engineering from the German-Jordanian University in 2019 and his B.S. in Computer Engineering from Philadelphia University, Jordan, in 2017.

Over the past three years at MTU, Mr. Awad has built a strong research background in computer vision, with a particular focus on underwater image enhancement and object detection. His research aims to optimize image enhancement techniques to improve object detection performance in challenging underwater environments, and he has published multiple papers in this field.

In addition to his academic achievements, Mr. Awad has industry experience as a software/hardware co-design engineer and has worked extensively on personal hardware and embedded systems projects, which have further strengthened his practical and technical expertise.

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Finishing Fellowship Award – Spring 2026 – Md Khairul Islam

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Islam, Md Khairul
Md Khairul Islam, PhD in Computational Science and Engineering, 2026

I am deeply honored to receive the Doctoral Finishing Fellowship and extend my sincere gratitude to the Graduate School and the Graduate Dean Awards Advisory Panel for this recognition. This award offers essential support at this pivotal stage of my Ph.D. journey and affirms the value of my research contributions.

As a Ph.D. candidate in Computational Science and Engineering at Michigan Technological University, my work focuses on advancing bioinformatics, particularly in plant genomics, complex disease associations, and systems biology. I have actively engaged in interdisciplinary collaborations through DOE, NSF, and CDC funded projects, where I developed novel computational frameworks such as PredTORpath, DyGAF, and TGPred. These algorithms combine statistics, machine learning, and biological data analysis to reveal gene regulatory mechanisms across plant and human systems, and are publicly available to support the scientific community.

In addition to research, I have contributed to peer-reviewed publications, mentored graduate helper, and participated in cross-disciplinary initiatives—all of which have shaped my long-term commitment to innovation in computational biology.

I am especially grateful to my advisor, Dr. Hairong Wei, for his unwavering mentorship and guidance, and to my colleagues in both the College of Forest Resources and Environmental Science—where my research is based—and the College of Computing, which hosts my Ph.D. program in Computational Science and Engineering, for their continued support.

This fellowship enables me to focus on completing my dissertation and further pursue impactful research at the intersection of artificial intelligence, genomics, and precision medicine. I am sincerely thankful for this opportunity and remain committed to advancing sustainable agriculture and human health through computational innovation.

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Finishing Fellowship – Spring 2025 – Abel Reyes-Angulo

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Abel Reyes-Angulo, PhD in Computational Science and Engineering, 2025

I began my Ph.D. journey at Michigan Technological University in Fall 2021 in the Computational Science and Engineering program, under the guidance of Dr. Sidike Paheding and more recently co-advising by Dr. Guy Hembroff. My time at Michigan Tech has been incredibly enriching, allowing me to dive deep into my passion for artificial intelligence, particularly in the fields of biomedical image segmentation and computer vision. My research focuses on developing AI-driven solutions for early disease detection, aiming to enhance healthcare outcomes through advanced deep learning techniques.

One of the most rewarding aspects of my work has been developing innovative deep learning models like UPEN++ and MarsLS-Net, which contribute to advancements in biomedical and planetary image segmentation. Presenting my research at top-tier conferences such as CVPR and MICCAI has been both exciting and humbling, providing opportunities to engage with and learn from the broader research community.

I’m incredibly grateful to my advisors, Dr. Paheding and Dr. Hembroff, for their unwavering support and mentorship. Their guidance has been instrumental in shaping my research and academic growth. I also want to extend my heartfelt thanks to the Applied Computing Department and Chair Dr. Dan Fuhrmann for their continuous encouragement. Additionally, I’m grateful to my committee members, Dr. Dukka KC and Dr. Laura Brown, for their valuable insights and support.

Receiving the Doctoral Finishing Fellowship is a true honor, and I sincerely thank the Graduate Dean Awards Advisory Panel and the Dean for this recognition. This fellowship allows me to dedicate myself fully to completing my dissertation and preparing for the next steps in my career.

I’m also thankful to my peers and colleagues at Michigan Tech. The collaborative and supportive environment here has made my doctoral journey both productive and enjoyable. I look forward to contributing further to the field of AI and making a positive impact on healthcare through my research.

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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 – Spring 2022 Recipient – Zhuo He

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I started my Ph.D. in 2018 and transferred to the Department of Applied Computing at Michigan Tech in the fall of 2019. I obtained my Bachelor’s degree from Central South University in China. 

At Michigan Tech, I work at the Laboratory of Medical Imaging and Informatics under Dr. Weihua Zhou’s supervision. We focus on the application of improving the treatment outcome of cardiac resynchronization therapy (CRT) using artificial intelligence, which is a life-saving treatment for patients with failing hearts. Like any therapy, CRT is not suited for all patients, and patient selection is critical to achieving optimal performance. My dissertation work intelligently fuses mechanical dyssynchrony with electrical dyssynchrony to identify the right patient for CRT, thereby significantly improving the outcome of CRT.

I would like to thank the Graduate School, the Department of Applied Computing, and the Graduate Dean Awards Advisory Panel for providing me with the Finishing Fellowship award. This award will allow me to focus on wrapping up my dissertation and preparing for my defense.

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Michigan Space Grant Consortium graduate fellowship application support

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The Graduate School is offering support services to assist graduate students in applying for the Michigan Space Grant Consortium’s Graduate Fellowship, including a workshop and one-on-one writing support.

MSGC’s Graduate Fellowship opportunity supports graduate students from affiliate
institutions who are conducting research and public service projects relevant to NASA’s strategic interests as expressed in NASA’s 2014 & 2018 Strategic Plans, specifically, research focused on aerospace, space science, and earth system science. Graduate students working in other, related science, technology, engineering, and mathematics fields are also eligible to apply. Starting this year, MSGC is piloting an expanded definition of STEM to include support for interdisciplinary projects that include art, so graduate students conducting research and projects relevant to NASA’s strategic interests in disciplines not traditionally considered STEM, such as the humanities or social sciences, are likewise encouraged to apply.

Fellowship recipients are awarded $5,000. To be eligible, applicants must be U.S. nationals, have a good academic record, and be in good academic standing. Women, under-represented minorities, and persons with disabilities are strongly encouraged to apply. Students currently receiving MSGC Fellowships are eligible to reapply.

Workshop information: Overview and tips from an MSGC Fellowship reviewer
Date and Time: Friday, September 17th, from 11:00 AM – 11:50 AM
Location: Admin 404
Presenter: Will Cantrell, Associate Provost and Dean of the Graduate School
Host: Sarah Isaacson, GLAS Program Director, sisaacso@mtu.edu
Register here: https://forms.gle/RSPYtUHVD6Yjimou6
A recording of the workshop will be available beginning September 21st.

Deadlines:
Wednesday, Nov. 3 at noon — Internal deadline for undergraduate and graduate fellowship proposals
Wednesday, Nov. 10 at 5 p.m. — Final materials, after review and approval by SPO, must be uploaded to MSGC by the applicant

For more information and specific application instructions, visit the MSGC website and the MTU Graduate School’s MSGC web page.

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NSF Graduate Research Fellowship Program Application Support

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The Graduate School is offering support services to assist graduate students in applying for the National Science Foundation’s Graduate Research Fellowship Program, including workshops and one-on-one writing support.  Fellowship recipients earn an annual stipend of $34,000.  To be eligible, applicants must be a U.S. citizen, national, or permanent resident, have never previously applied to GRFP while enrolled in a graduate degree program, have never earned a master’s or professional degree in any field, or completed more than one academic year in a graduate degree-granting program.  Applications are due October 18th – 22nd.  See https://www.nsfgrfp.org/ for full benefits and eligibility details.

Workshop 1: Overview and tips from a former NSF program manager and reviewer
Date and Time: Friday, September 3rd, from 9:00 AM to 10:30 AM
Presenter: Dr. Pushpalatha Murthy, former NSF program manager
Co-hosts: Dr. Debra Charlesworth, former NSF GRFP reviewer, and Sarah Isaacson, NSF GRFP Support Coordinator
Zoom meeting link: Please make sure to sign in with your MTU account before joining the meeting to be admitted.
Join from PC, Mac, Linux, iOS or Android: https://michigantech.zoom.us/j/83018958000

Workshop 2: Crafting your statements: Content and organization
Date and Time: Friday, September 10th, from 10:00 AM to 11:30 AM
Presenter: Sarah Isaacson, NSF GRFP Support Coordinator
Zoom meeting link: Please make sure to sign in with your MTU account before joining the meeting to be admitted.
Join from PC, Mac, Linux, iOS or Android: https://michigantech.zoom.us/j/82410509516

Personalized writing support:
Applicants will receive support via an NSF GRFP Canvas course as well as individualized writing support on application drafts from qualified staff members.

See https://www.nsfgrfp.org/ for more details. Questions? Contact Sarah Isaacson, NSF GRFP Support Coordinator: sisaacso@mtu.edu

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KCP Future Faculty/GEM Associate Fellow – Karen Colbert

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Karen Colbert is a 2nd year PhD student in Computational Science & Engineering. Karen has received extensive training in Data Visualization, Social Network Analysis (SNA), and Predictive Analytics. She specializes in Race, Ethnicity, and Quantitative Methodologies. Currently, Karen incorporates all those skills in her role as a Research assistant with the MTU NSF ADVANCE team to help study and improve outcomes in diversity and equity efforts for MTU faculty.

Karen has over 5 years of experience working through different capacities to bridge the STEM equity gap for both faculty and students of color in the Tribal College community (TCU). She serves on TCU data assessment teams and as a faculty mentor to environmental science capstone students at the Keweenaw Bay Ojibwa Community College (KBOCC). 

Karen also serves as an adjunct math faculty at KBOCC. In the most recent 3 years, Karen has worked with Carnegie Math Pathways, Achieving the Dream, and the American Indian College Fund to develop math curriculum with Indigenous contextual content using the Growth Mindset. As a result, KBOCC has seen drastic improvements in the retention and persistence of tribal college students in their math courses over the last 3 years. As she continues her work with TCUs, she incorporates SNA and other quantitative methods to develop assessment tools used for reporting to accrediting agencies.

Karen hopes to see her burden for bridging the STEM equity gap for people of color (POC) create amazing opportunities and results in the higher learning educational environment for years to come.

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Doctoral Finishing Fellowship Summer 2020 Recipient- Eassa Hedayati

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I am a fourth-year Ph.D. Candidate in Computational Science and Engineering living in the Electrical and Computer Engineering (ECE) Department.  The multidisciplinary nature of my field of study is imposing a special kind of variation in my research area. However, I tried to keep my research around finding sparsities light-field (LF). In doing so, I have been involved simulating LF and compressing it. In pursuing my research, I have used machine learning techniques to further enhance the quality of my research. My research heavily involves computation and use of algorithms, therefore, I had to devote some parts of my time to obtain a Masters in Computer Science.

I am extremely grateful to the Graduate Dean Awards Advisory Panel and dean for recommending me for the Finishing Fellowship for the summer 2020 semester. Furthermore, I am obliged to the Graduate School for providing this generous support. I will make use of the extra time in summer to finish writing my dissertation and add to my publication records. I am looking forward to defending my dissertation in the summer of 2020. I am also grateful to Dr. Jeremy P. Bos for his guidance throughout my Ph.D. studies and to the ECE Department for supporting my academic efforts since I joined the Department in 2017.

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Doctoral Finishing Fellowship Spring 2019 Recipient – Robert Zupko

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Robert Zupko
Computational Science & Engineering

I am a PhD candidate in Computational Science & Engineering (CS&E) at Michigan Tech, affiliated with the Department of Social Sciences. My departmental affiliation reflects the non-departmental and interdisciplinary nature of CS&E in which complex problems are explored.  My doctoral research focuses on the applications of computer modeling to coupled human and natural systems with an emphasis on assessing the sustainability of biofuels in the Western Upper Peninsula region of Michigan. The development of biofuels is interesting since they can bring new industry into the region and help to address climate change; however, the need for feedstocks means that that their development may interfere with other regional concerns. Computer modeling techniques, such as agent-based modeling, offer as a means to explore how the region could respond to the introduction biofuels and conduct sustainability assessments of environmental, economic, and social concerns. Ultimately, the goal of this research is not only to conduct these assessments, but to develop a generalizable computational technique for the study of coupled human and natural systems.

I am extremely grateful to Dr. Mark Rouleau, in the Department of Social Sciences, and the Michigan Tech Graduate School for the opportunity to pursue this research. Likewise, I am humbled by the Finishing Fellowship which will allow the opportunity to dedicate my time solely to completing my doctoral studies.

 

 

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