PhD Computer & Information Sciences
Shovan Bhowmik is a first year Computer Science PhD student working as a Research Assistant (RA) under the supervision of Professor Dr. Cathy Wu and Professor Dr. Vijay Shanker. His research interests focus in the wide area of data science including Machine Learning, Natural Language Processing, Computational Biology, etc. Currently, his research involves impact relation extraction such as Protein Protein Interaction (PPI), Post Translational Modifications (PTMs), Biological Processes, Chemical-Gene Interactions from the bio-domain literature by mining texts.
Previously, he completed his BSc in Computer Science from Bangladesh and published multiple research papers in the field of data science where he received two best paper awards in the international conferences – one for the investigation of N-gram tokens impact in sentiment analysis and another was for the prediction of early colorectal cancer by analyzing colon polyps from images using transfer learning technologies.
PhD Physics & Astronomy
Riley received a B.S. in Physics from Western Washington University and M.S. in Physics from the University of Delaware, where he is working on completing his Ph.D under supervision of Dr. Federica Bianco studying rapid brightening of stars called “flares”. He is a member of the Rubin LSST Transients and Variable Stars Science Collaboration and works on enabling the study of stellar flares in the LSST Wide Fast Deep survey by taking advantage of atmospheric refraction.
PhD Political Science & International Relations
James Korman is a PhD Student at the University of Delaware in Political Science & International Relations with a concentration in Comparative Politics & Quantitative Methodologies. Within Comparative Politics, his research focuses on issues surrounding corruption, development, Latin American politics, political economy, & state capture. Methodologically, he uses & conducts research in applied statistics & computational social science w/ a focus on multilevel modeling & panel data analysis. His work has been published in the Emory International Law Review, The Biden School Journal of Public Policy, amongst other scholarly outlets.
PhD Biden School of Public Policy & Administration
Kyungmin Lee is a Ph.D. candidate in Energy and Environmental Policy at the University of Delaware under Dr. Gregory Dobler. Her research interests lie at the intersection of energy, climate change, urban development, and social sustainability. With a broad research interest covering areas of environmental planning in international and regional contexts, she is interested in understanding dynamic interactions between humans and the environment. Her current research focuses on the impact of human behaviors and the built environment on energy use and heat in cities. With a specific interest in quantitative and spatial analysis methods, she has also developed an interest in big data analysis for a public policy using machine learning, image processing, and computer vision techniques. Prior to joining the Ph.D. program, she worked in the field of international environmental development and cooperation at the government research institute, governmental agency, and United Nations. She received a Bachelor of Arts in Economics from Sungshin Women’s University and a Master of City Planning in Environmental Studies from Seoul National University in South Korea.
PhD Computer & Information Science
Tang Li is currently pursuing Ph.D. in Computer Science. He is working under the supervision of Dr. Xi Peng from the department of Computer and Information Science. His current research interest is Machine Learning , Deep Learning, Explainable Machine Learning, and Computer Vision.
PhD School of Marine Science & Policy
Mark Lundine is a Unidel Distinguished Graduate Fellow as well as a PhD candidate in Oceanography. His main area of research is coastal geomorphology. Mark has spent extensive time in the field on the coastal plain and beaches of Delaware, Maryland, Virginia, and Tuscany (Italy), collecting topographic and sedimentological datasets to help understand coastal morphodynamics (how the shapes of coasts change over time). Additionally, he has assisted in a variety of seafloor mapping projects, with a variety of autonomous and human-controlled platforms, in the Delaware Bay, the mid-Atlantic, the Great Lakes, and the Mediterranean Sea.
His dissertation is focused on applying machine learning models to publicly available remote sensing datasets in order to map and characterize a variety of abundant coastal features. In 2021, he was the lead author on a paper aimed at developing neural networks that could map coastal plain depressions known as Carolina Bays. These are somewhat enigmatic depressions that are found throughout the East Coast of the United States. In 2022, he was also the lead author on a paper using similar methods to automatically map seabed-fluid-escape depressions called pockmarks. Currently, he is developing a machine learning framework to collect shoreline change datasets from satellite imagery and then use those datasets to develop data-driven predictive models of sandy beaches.
PhD Educational Statistics and Research Methods
Olushola Soyoye is pursuing a Ph.D. in Educational Statistics and Research Methods. His research interests include data mining, missing data, as well as the application of artificial intelligence (AI) to data in education, health and social policy. He received his bachelor’s and master’s degrees in mathematics education from Tai Solarin University of Education, Ijebu-Ode, Nigeria and the University of Illinois, Urbana-Champaign, respectively.
PhD Financial Services Analytics
Swati is a 3rd PhD candidate in the Financial Services Analytics department at the University of Delaware, where I am focused on researching fairness and bias in language models. In addition to my academic pursuits, I also have a wealth of experience in analytics, software development, and product management. My background in these areas has given me a well-rounded skill set and the ability to approach problems from multiple angles. I am passionate about using my skills to make a positive impact in the world and am excited to continue my research and make meaningful contributions to the field of technology and financial services analytics.
PhD Geography & Spatial Sciences
Matthew is a PhD candidate in the Department of Geography and Spatial Sciences under Dr. Pinki Mondal. He is interested in using Geographic Information Science (GIS) and remote sensing as tools to monitor the impact of humans on natural and built environments and inform the decision-making process. He has experience in applying machine learning techniques to large remote sensing datasets to map the extent of various environmental issues including invasive species, wetland degradation, land cover change, and unsustainable agriculture. In his current research, Matthew is studying the equitable distribution of green space throughout US cities using an environmental justice lens by pairing remote sensing, social media, and demographic data.
PhD Geography & Spatial Sciences
Dongyang is a PhD candidate in the Department of Geography and Spatial Sciences at the University of Delaware. Under the surpervision of Dr. Kyle Frankel Davis, she focuses on sustainable food systems and the trade-offs between environmental, nutritional, and resilience factors. Before joining UD, she was a Research Associate at the University of Reading (UK). During her research, she utilized a combination of programming and mathematical methods to investigate the relationships between environmental and climatic variables. Dongyang achieved a master’s degree in Ecosystem and Environmental Change from Imperial College London (UK) and a bachelor’s degree from Shanghai Jiao Tong University (China).
PhD Biden School of Public Policy & Administration
Lan is an Urban Data Scientist and PhD candidate at the University of Delaware in Energy and Environmental Policy. Before coming to Udel, she worked as a Data Scientist at Tongji University for almost two years.
Currently, her work uses observational methods and data from the “Urban Observatory” (http://MUONetwork.org) to analyze images of city skylines through the application of computer vision and machine learning techniques to study energy consumption and lighting technology in cities, as well as inform operations and energy distribution policies. In detail, she explores three core topics in urban energy: outage detection and grid stability evaluated through urban lighting variability, energy efficient lighting changeover rates, and energy end-use behavior.