Mapping Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise. However, the complex and sparse nature of observational data, particularly information about the bed topography under the ice sheet, significantly increases the uncertainty in model projections. Bed topography is traditionally measured by airborne ice-penetrating radars that measure the ice thickness directly underneath the aircraft, leaving data gaps of tens of kilometers in between flight lines. This study introduces a deep learning framework, DeepTopoNet, that integrates radar-derived ice thickness observations and BedMachine Greenland data through a novel dynamic loss-balancing mechanism. Among all efforts to reconstruct bed topography, BedMachine has emerged as one of the most widely used datasets, combining mass conservation principles and ice thickness measurements to generate high-resolution bed elevation estimates. The proposed loss function adaptively adjusts the weighting between radar and BedMachine's bed, ensuring robustness in areas with limited radar coverage while leveraging the high spatial resolution of BedMachine's bed estimates. Our approach incorporates gradient-based and trend surface features to enhance model performance and utilizes a convolutional neural network (CNN) architecture (i.e., BedTopoCNN) designed for subgrid-scale predictions. By systematically testing on the Upernavik Isstrøm) region in West Greenland, the model achieves high accuracy (MAE: 12.49 m, RMSE: 19.38 m, and R2: 0.99), outperforming baseline methods in reconstructing subglacial terrain. This work demonstrates the potential of deep learning in bridging observational gaps, providing a scalable and efficient solution to inferring subglacial topography. This framework paves the way for improved predictions of ice sheet flow and sea level rise.

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel ‘Big-data REU Integrated Development and Experimentation’ (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research.

The increasing complexity of multidimensional spatiotemporal data presents significant challenges for clustering techniques, particularly in capturing intricate temporal, spatial, and heterogeneous patterns. This paper proposes a novel Hybrid Ensemble Deep Graph Temporal Clustering (HEDGTC) algorithm that integrates homogeneous and heterogeneous ensemble clustering models, leveraging both traditional and deep learning-based clustering approaches. The algorithm utilizes graph neural networks (GNNs) to effectively combine the strengths of multiple clustering models and enhance the clustering performance. The ensemble models are designed to handle diverse data characteristics, while the deep learning components capture complex non-linear relationships within the data. GNNs are employed to derive the final clustering outcomes by preserving spatial and temporal dependencies, making the approach well-suited for complex multidimensional spatiotemporal data. Experimental results from three real-world multivariate spatiotemporal data demonstrate the effectiveness of HEDGTC in accurately clustering and analyzing spatiotemporal patterns, outperforming state of the art ensemble models as well as traditional and individual deep clustering methods in terms of clustering performance and accuracy. The proposed method offers a robust framework for a wide range of applications, including climate modeling, geospatial analysis, and dynamic system forecasting.

Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learned equations. Accuracy and simplicity of the learned equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods.