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Graduate Student Positions
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Please check back regarding opportunities to join the team.
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Name
email@case.edu | LinkedIn | Google Scholar
Education:
B.S. in XXX, College
M.S. in XX, CWRU
2nd-Year PhD Student in XXX, CWRU
Research Areas:
Thesis Topic:
Hometown:
Background:
Kristen Hernandez
kjh125@case.edu
Education:
B.S., Chemistry, Youngstown State University
M.S., Materials Science & Engineering, CWRU
PhD Student in Materials Science & Engineering, CWRU
Hometown: Twinsburg, OH
Research Areas: XRD Crystallography; Material Characterization and Failure; Advanced Manufacturing Technology; Image Feature Extraction; FAIR Ontology Development
Background: I am a PhD/MS Student in Material Science pursuing higher education. Graduate from Youngstown State University (YSU) in Chemistry with dual minors in Physics and Mathematics, I embarked on an early career with Element Materials Technology, a leading provider of materials and product qualification testing services. The evaluation of materials . I am involved in MDS3 mission as a material scientist that focuses on advanced manufacturing challenges that can be solved using data science methods.
Priyan Rajamohan
priyan@case.edu | LinkedIn | Github
Education:
B.E. (w/ Honors), Computer Engineering, National University of Singapore (NUS)
MS Student, Computer & Data Science Engineering, CWRU
Research Areas: FAIRification of Data, Ontology Development, Knowledge Graphs
Thesis Topic: FAIRification of Research Data
Hometown: Singapore
Background: As a graduate student in Computer Science with a solid foundation in software engineering, I specialize in the intersection of AI, data analytics, and software development. My thesis delves into the FAIRification of data, uncovering the synergies between Software Engineering and AI. In the immediate future, my goal is to make significant contributions to research data management, focusing on enhancing automation. Looking ahead, I aspire to forge a career in cutting-edge research, with a specific interest in collaborating with national labs to address intricate challenges. Ultimately, my overarching objective is to leverage my expertise at the forefront of technology, propelling transformative advancements in the field.
Redad Mehdi
mxm168@case.edu
Education:
B.Tech, Metallurgy & Materials Engineering, IIEST
M.Tech, Materials Science & Engineering, Indian Institute of Technology
PhD Student in Materials Science & Engineering, CWRU
MDS3 Research Areas: FAIR Materials, Synchrotron XRD analysis, Deep neural networks
Hometown: Dehradun, India
Background: I am a Materials scientist by trade and love understanding fundamental atomic phenomena underlying various material behaviors and mechanical properties. As a PhD student at MDS3, I am applying cutting-edge data science and machine learning to simplify the analysis of colossal synchrotron XRD data. I also want to keep solving these fundamental materials science problems using computer and data science advances in the future.
Mingjian Lu
mxl1171@case.edu | LinkedIn | Google Scholar
Education:
B.S. in Computer Science, Tianjin University of Technology
1st-Year PhD Student in Computer and Data Science, CWRU
Research Areas: Image Processing, Graph Mining, Scene Graphs
Background: Mingjian specializes in the intersection of deep learning and material science. His research is primarily focused on applying advanced deep learning techniques to tackle complex challenges in materials science. Mingjian's work involves utilizing sophisticated algorithms in data mining and machine learning to process and analyze large sets of data, particularly in the realm of image processing. His contributions include the application of deep learning methodologies for object detection and segmentation, which are critical in understanding and solving material-related problems. By integrating deep learning with material science, Mingjian is at the forefront of developing innovative solutions for material degradation, life extension, and other crucial aspects of material science. His work not only advances the field of computational data science but also has tangible impacts on the practical applications of materials in various industries.
Weiqi Yue
wxy215@case.edu
Education:
B.S. in Mathematics, University of Miami
M.S. in Applied Statistics, Johns Hopkins University
PhD Student in Computer Science, CWRU
Research Areas: XRD Analysis, Computer vision and deep learning, Federated Learning
Thesis Topic: Deep Learning approaches for phase identification in Ti64 HEXRD pattern
Hometown: China
Background: My research focus lies in the realm of 2D X-ray diffraction data analysis, where I leverage computer vision and deep learning techniques to extract complex features. Additionally, I am actively involved in the development of federated learning frameworks, ensuring collaborative model training across multiple clients while prioritizing privacy protection. My overarching goal is to delve into the theoretical foundations of deep learning algorithms, identifying and optimizing models tailored to specific tasks in diverse areas.
Rachel Yamamoto
rsy6@case.edu | LinkedIn
Education:
4th-Year BS/MS Student, Computer & Data Science Engineering, CWRU
Research Areas: Computing Infrastructure & Distributed Computing, Advanced Manufacturing
Hometown: Saratoga, CA
Background: My current research interests include parallel and distributed computing, networks, and security. I currently work on manipulating large datasets with Pyspark.
Qingzhe Guo
gxg88@case.edu
Education:
B.S. in Software Engineering, Northeastern University (China)
M.S. in Computer Science, CWRU
PhD Student in Computer Science, CWRU
Research Areas: Distributed Machine learning
Background: My main research interest is distributed machine learning, specializing in massively parallel computing for complex machine learning models. My work is deeply involved in enhancing communication protocols between distributed nodes to minimize latency and maximize resource utilization. My ultimate goal is to design efficient algorithms, communication protocols and build basic framework components to fully utilize resources to achieve efficient computation on large models and large-scale data sets.
Max Ligget
maliggett@knights.ucf.edu | LinkedIn
Education:
B.S. in Applied Physics, Ursinus College
3rd-Year PhD Student in Materials Science & Engineering, UCF
Research Areas: FAIR Materials, Interdigitated Combs
Hometown: Denver, CO
Thesis Topic: Front contact Metallization of Fielded Silicon Photovoltaics
Background: As a PhD student with a foundation of physics, I have always been interested in hands on research related to root cause analysis. My research focus is on the reliability of silicon photovoltaics on a material level with a focus on metallization. I aspire to contribute useful characterization tools and technical approaches for such characterization that can be used in the future. Ultimately, I aim to work towards a career in research and development or in a capacity where I can contribute to innovation or development of solar cell technologies.
Finley Holt
frh10@case.edu | LinkedIn
Education:
B.S.E in Mechanical Engineering, CWRU
1st Year M.S. student in Aerospace Engineering, CWRU
Research Areas: XRD Images
Hometown: Jamestown, NY
Background: My primary research focus is the rapid analysis of two-dimensional x-ray diffractograms by employing advanced techniques such as computer vision mapping of ellipses to rings. I aim to iterate upon our ellipse detection methods and incorporate a surrogate model into the analysis process to determine valuable information from diffractograms in a computationally efficient manner. In pursuing this research, my overarching goal is to develop as an aerospace engineer with a unique interdisciplinary perspective, incorporating principles from both materials science and data science.
Thomas Ciardi
tgc17@case.edu | LinkedIn
Education:
B.A. in Computer Science & Cognitive Science, CWRU
2nd-Year PhD Student in Computer & Data Sciences, CWRU
Research Areas: Deep Learning, 2-4D Segmentation (XCT, AFM, SEM Imaging), st-GNN, Computing Infrastructure
Thesis Topic: Deep Learning Framework for the Spatiotemporal Graph Representation of Terabyte-scale 4D XCT Datasets
Hometown: New Fairfield, CT
Background: My research sits at the intersection of machine learning and materials science. I am fundamentally interested in 1) how models work, 2) their extension to real-world problems, 3) frameworks to translate Tera/Petabyte scale datasets into digestible representations, and 4) interpretability mechanisms that enable scientific understanding in model decisions. I focus on the adaptation of vision-based deep learning, spatiotemporal GNNs, and generative models to materials science challenges and modalities: XCT of AlMg SCC, FKM crystallization growth kinetics, LPBF defect detection, PV power forecasting. I also contribute to our group’s computing and data infrastructure, designing APIs to make high performance and distributed computing accessible to researchers.
Jarod Kaltenbaugh
ja391950@ucf.edu | LinkedIn
Education:
B.S. in Materials Sciences Engineering, UCF
1st-Year PhD Student in Materials Sciences Engineering, UCF
Research Areas: FAIRification of Electronic Components, Interdigitated Combs
Hometown: Del Rio, TX
Background: My main interests are combining data sciences and material analysis to create robust data-enabled processes for determining lifetime and reliability of various materials. My specific focus is analyzing lifetime and reliability of PV materials using data-driven models based on IV curve data. I would like to work at a national lab at some point after my Ph.D. program is over, with the goal of continuing data-enabled analysis of materials. Eventually I would like to return to academia as a professor in some capacity.
Vibha Mandayam
vsm21@case.edu | LinkedIn
Education:
3rd-Year B.S. student in Data Science & Analytics, CWRU
Research Areas: Geospatial Modeling
Hometown: Stow, OH
Background: As an undergraduate student in Data Science with special interests in geospatial modeling, I have developed strong technical skills through my research experience. I have worked with geostatistical modeling techniques, such as kriging, and gained an understanding of the relationship between materials and climate. During my undergraduate career, I aim to contribute to studies of geospatial data and spatiotemporal GNNs. In the future, I would like to collaborate with national labs to further explore data science applications to the development and improvement of renewable energy. Ultimately, I aim to utilize data science methodologies to solve issues in sustainability and climate change.
Hein Htet Aung
hxh483@case.edu | LinkedIn
Education:
B.S. Mechanical Engineering, Applied Data Science Minor, CWRU
2nd-Year PhD Student in Materials Science & Engineering, CWRU
Research Areas: Data-driven digital Twins, Direct Ink Write, Advanced Manufacturing, Study Protocol, Degradation Modeling (netSEM)
Thesis Topics: Data-Driven Approach for Study of Acrylic Polymer Degradation (MS); Direct Ink Write (PhD)
Hometown: Yangon, Myanmar
Background: With a background in Mechanical Engineering and Materials Science, Hein loves applying data science to gain insights from systems at macro and micro levels. For his Master’s research, he worked on data-driven modeling for degradation of acrylic polymers and released an R software package. After his internship with Lawrence Livermore National Lab during his Ph.D. program, Hein is working to expand his research in DIW systems to model the error behavior of the build platform during the printing process and contribute towards the development of data-driven digital twins. Hein aspires to work in an interdisciplinary research area where he can explore novel ideas through data science.
Sameera Nalin Venkat
sxn440@case.edu | LinkedIn | Google Scholar
Education:
Integrated M.Sc in Chemical Sciences, University of Hyderabad
M.S., Materials Science & Engineering, CWRU
5th-Year PhD Student in Materials Science & Engineering, CWRU
Research Areas: FAIR Materials, Graph Neural Networks, Deep Learning
Thesis Topic: Graph-Based Machine Learning for Assessing Similarity of Particle Growth in Material Systems
Hometown: Hyderabad, India
Background: I enjoy learning and utilizing data science tools to solve materials science problems. My research focus is to understand growth kinetics and behavior of different material systems using deep learning methods, such as graph neural networks. During my virtual internship with Lawrence Livermore National Laboratory in summer 2022, I had the opportunity to explore image analysis techniques for atomic force microscopy datasets. My long-term goal is to continue working on interdisciplinary research problems using state-of-the-art data science methods.
Hayden Caldwell
hwc11@case.edu | LinkedIn
Education:
3rd-Year BS Student, Computer Science (Software Engineering Track), CWRU
Research Areas: Computing Infrastructure, FAIR Materials
Hometown: Lexington, KY
Background: My research interests include development for Microsoft HoloLens, cloud computing, and Python packaging. I currently work with developing the FAIR FindTheDocs site, a search engine containing files, tools, and resources related to SDLE FAIROntology research.
Alexander Harding Bradley
ach159@case.edu | LinkedIn | GitHub
Education:
B.S. in Computer Science & Mathematics, CWRU
2nd-Year Masters student in Computer Science, CWRU
Research Areas: FAIRification of Data, Ontology Development, Image Analysis for Radiography
Hometown: Dublin, Ireland & San Jose, CA
Background: I am currently pursuing a masters degree in Computer Science with a concentration in Artificial Intelligence. I am fascinated by the intersection of computer science and different material-science domains. Currently, I am working on an automated FAIRification pipeline for experimental data as well as different scripts to analyze and segment radiography images. While this has been the focus of my research experience, I am also open to exploring different domains within material science as it applies to computer science. Long term, I hope to gain some further research experience in a national lab and possibly further my research experience in the form of a PhD.
Taylor Currie
taylor.currie@ucf.edu | LinkedIn | Google Scholar
Education:
B.S., Chemistry, University of Central Florida
M.S., Chemistry, University of Central Florida
5th-Year PhD Student in Chemistry, University of Central Florida
MDS3 Research Areas: FAIR Materials, Interdigitated Combs
Hometown: Pompano Beach, FL
Thesis Topic: Synthesis, Characterization, and Application of Mo- and W-Based Precursors for CVD and ALD Applications
Background: Taylor’s research interests are at the intersection of inorganic synthesis, thin film growth, and heterogeneous catalysis. She uses chemical vapor deposition (CVD) and atomic layer deposition (ALD) to grow transition metal dichalcogenide (TMDC) materials, which she then uses in (photo)catalytic applications. As such, she also has a research interest in studying and developing novel transition-metal- based molecular precursors in service of ALD and CVD applications. As a soon-to-be graduate, Taylor aspires to continue research in materials chemistry as a postdoctoral researcher in a national lab.
Galya Vicnansky
Galya.Vicnansky@ucf.edu | LinkedIn
Education:
4th-Year BS Student, Materials Science & Engineering, UCF
Research Areas: Electronic Components
Background:I am an undergraduate student studying materials science and engineering with a focus on renewable energy and electronic materials. My senior design project deals with automating in situ electrical measurements of electronic devices. Upon graduation, I hope to join a company or national lab and acquire a solid foundation in materials characterization, synthesis, and processing. Ultimately, my long-term career goal is to contribute to the advancement of renewable energy technologies, bridging the gap between scientific research and practical applications for a more sustainable future.
Ben Pierce
bgp12@case.edu | LinkedIn | GoogleScholar | GitHub | Website
Education:
B.S. Computer and Data Sciences, CWRU
1st-Year PhD Student in Materials Science & Engineering, CWRU
Research Areas: Photovoltaic System Modeling, Statistical Learning, Computer Vision, Digital Twins
Hometown: North Royalton, OH
Background: Ben’s research focus is on combining statistical/machine learning with materials domain knowledge, with a current emphasis on application to photovoltaics. Ben received his BS in computer science from Case Western Reserve University in 2021, and worked as a Member of the Technical Staff at Sandia National Labs until 2023. Currently, Ben works on a variety of topics in PV, such as single axis trackers and module degradation, and is also conducting research into generalized data-driven digital twin models. He is also interested in self-supervised generative models for paired image-vector data, and is supported by the NSF GRFP.
Rohan Rajappan
rxr628@case.edu | LinkedIn | GitHub
Education:
1st-Year B.S. student in Computer Science, CWRU
Hometown:Simsbury, CT
Background: My research interests are in the intersection of materials science and artificial intelligence. I am currently working in additive manufacturing and image segmentation with laser powder bed fusion. I am also interested in areas of propulsion and rocketry, and I hold an L1 rocketry certification. My long-term goals include pursuing a PhD. I hope to work in a wide variety of environments, including national labs, to gain experience.
Maliesha Sumudumalie
mxs2087@case.edu | LinkedIn
Education:
B.S. Electrical and Electronic Engineering, University of Peradeniya (Sri Lanka)
1st year PhD Student in Materials Science & Engineering, CWRU
MDS3 Research Areas: Deep Learning, Image Processing, XCT Imaging, Advanced Manufacturing, Data segmentation, EBSD
Hometown: Matara, Sri Lanka
Background: Maliesha's research interests lie at the intersection of machine learning and materials science. During her undergraduate studies, she focused on enhancing face recognition methods by integrating a neural network into the established FaceNet framework, surpassing existing benchmarks. Transitioning to her career, Maliesha initially served as an image processing engineer and later as a software engineer. Currently, she is engaged in developing an automated pipeline for segmenting and characterizing pitting corrosion in aluminum using XCT images, alongside setting a benchmark for image segmentation in materials science. Looking ahead, her aim is to persist in tackling interdisciplinary research challenges employing cutting-edge data science methodologies.
Arafath Nihar
axn392@case.edu | LinkedIn | Github | GoogleScholar
Education:
PhD Student in Computer Science, CWRU
MDS3 Research Areas: Deep Learning, Data engineering, Big Data, MLOps
Hometown: Colombo, Sri Lanka
Ayorinde Emmanuel Olatunde
aeo49@case.edu | LinkedIn
Education:
B.S. Statistics, Obafemi Awolowo University (Nigeria)
M.S. Statistics, Kyungpook National University (South Korea)
PhD Student in Applied Mathematics, CWRU
MDS3 Research Areas: Uncertainty Quantification, Advanced Manufacturing, Study Protocol, Interdigitated Combs
Hometown: Ise Ekiti, Nigeria
Background: As a PhD student in Applied Mathematics with a background in Statistics, Ayorinde is currently researching with MDS3, focusing on Uncertainty Quantification and extensions to other topical areas where statistical solutions for Material Data Science problems are needed. While collaborating with other field experts, he employs statistical methodologies and machine learning to optimize processes and contribute to interdisciplinary research initiatives.
Jia Kambo
jxk1403@case.edu | LinkedIn
Education:
B.S. student in Computer Science, CWRU
MDS3 Research Areas: FAIRfication, Python Package Development, Website Development (not research, but I am working on that right now for SDLE), ontology development, and distributed database management
Hometown: India
Background: My curiosity brought me to the USA as a 16-year-old pursuing my education and passion for making a positive impact using technology. Through my curiosity, I joined SDLE, hoping to learn more about the applications of computer science in making a positive real-world impact. I am an enthusiastic learner, hoping to learn more and expand my horizons; I do so through my engagements on this campus, my internships with L&T, Progressive Insurance, and Dell Technologies, as well as a research assistant at SDLE. I am particularly interested in the fields of AI, distributed computing, and robotics.
Nathaniel Hahn
nrh51@case.edu | LinkedIn
Education:
B.S. student in Electrical Engineering & Computer Science, CWRU
MDS3 Research Areas: XRD Image Analysis, Deep Learning
Hometown: Los Altos, California
Background: I’ve always been fascinated by computers, including both hardware and software. My aim is to fully understand these systems, which is why I am currency pursuing both electrical engineering and computer science. Ultimately, I plan to earn a PhD in electrical engineering and conduct research in the field.
Ethan Fang
ewf22@case.edu | LinkedIn
Education:
B.S. student in Computer Science & Math, CWRU
MDS3 Research Areas: XRD Image Analysis, Cybersecurity
Hometown: San Francisco, California
Background: Very interested in machine learning and data analysis. After graduating from CWRU, I hope to obtain a master’s degree in financial engineering.
Preston DeLeo
pcd42@case.edu | LinkedIn | GitHub | Google Scholar
Education:
B.S. student in Computer Science, CWRU
MDS3 Research Areas: Deep Learning, Graph Machine Learning
Hometown: St. Augustine, FL
Background: My main interests revolve around machine learning, artificial intelligence, and data science. I am currently pursuing an undergraduate degree in computer science. I later intend to complete a Ph.D. in computer science and become a machine learning research scientist. My long term goal is to eventually become a professor.
Santiago Salazar
sxs2928@case.edu | LinkedIn
Education:
B.S. student in Chemical Engineering, CWRU
Hometown: Northvale, NJ
Background: My research interests involve materials science and data science. Currently, I am working in advanced manufacturing with a direct ink writing printer and analyzing its properties using R. I hope to expand my expertise by integrating materials science and data analytics techniques to optimize manufacturing processes and contribute to technological advancements.