UM-CIP Program
Maryam Rishehri
This research investigates subsurface flow organization, solute transport, and residence-time dynamics in urban watersheds using an integrated modeling and observation framework. The project combines three-dimensional variably saturated flow simulations with the ParFlow-CLM hydrologic model, Lagrangian particle tracking using EcoSLIM, and field-based monitoring data to evaluate how storm events, land cover, and subsurface heterogeneity influence groundwater recharge and contaminant mobility in developed landscapes.
Because these simulations generate extremely large datasets, the fellowship supports the development of scalable high-performance computing workflows, including GPU-enabled simulations, parallel data processing, and distributed geospatial analysis pipelines. These computational advances will enable efficient processing of particle-trajectory datasets, execution of long transient simulations, and implementation of scenario testing across multiple urban watersheds, thereby supporting the core analytical goals of the dissertation.
DS & AI Program
Evaluating ML Models for Predicting Global Deep Ocean Temperature and Salinity Profiles Trained on Deep Argo Data (Ellie Davidson)
This project evaluates and refines machine learning models, specifically deep neural networks and random forests, that have been pre-trained to predict deep ocean Temperature and Salinity (T/S) profiles below 2000 meters using upper ocean (0-2000m) data and other predictors. The evaluation is conducted by applying these models to a gridded Argo climatology, assessing the accuracy of the predicted deep-ocean profiles against established climatologies and reanalysis datasets. The project also involves working with large, multi-dimensional gridded ocean datasets and applying dimensionality reduction techniques such as principal component analysis (PCA) to support model evaluation and refinement.
Scalable, Secure and AI-ready Platform for Personal Informatics Project for Data Science Education (Chris Song)
This project focuses on the design and development of a web-based application to support students and instructors conducting Personal Informatics projects in the introductory data science course IS 296. Using a human-centered design approach, the system is designed to facilitate the collection, analysis, and reflection on personal data within the course environment. The application is engineered for scalable deployment beyond IS 296 and designed to support future integration of AI-powered capabilities that enable human–AI collaboration for personalized and adaptive learning experiences. The system also incorporates privacy and security protections appropriate for handling student-generated personal data.
Understanding Students’ Learning Journey in Personal Informatics Project: Implications for Design (Emma Shay)
This project analyzes field data collected from the Personal Informatics project in the introductory data science course IS 296 to inform the human-centered design of an agentic AI system to support personal informatics to a broader audience. The data from Spring 2025 and Fall 2025, which includes students’ planning documents, end-of-project reflections (both qualitative and quantitative), students’ weekly reflection submissions, and creative artefacts, are used to conduct a qualitative analysis, contributing to developing a coding scheme and coding result.
Building data infrastructure and a pipeline to support a personal informatics project (Amreen Adams)
This project develops a data infrastructure to support IS 296 students’ Personal Informatics projects as part of the TEEM (Teaching to Empower) program. The infrastructure expands the types of data students can collect about themselves by providing Python libraries that connect to raw data from social media takeouts, wearable sensors, and UMBC student data. The project begins with the use of data from main social media platforms such as Instagram and YouTube, with the potential to expand to other data sources. The resulting libraries enable students to process raw data into structured, tabular formats suitable for analysis in introductory data science projects.
Deepfake Legislation Policy Page (Henry Sonti)
This project analyzes and updates existing deepfake legislation, with reference to policies presented on the CISAAD project’s policy webpage. It focuses on building an international perspective, with particular attention to audio deepfakes, and compares legislative approaches across countries. The project also examines the effectiveness and scope of current policies, contributing to a clearer understanding of global strategies for regulating deepfake technology.