Defense Notices

Emerging applications in next-generation wireless networks, such as augmented and virtual reality (AR/VR) and autonomous vehicles, demand significant computational and communication resources at the network edge. This PhD research focuses on developing joint communication–computation solutions while incorporating various network-, application-, and user-imposed constraints. In the first thrust, we examine the problem of energy-constrained computation offloading to edge servers in a multi-user, multi-channel wireless network. To develop a decentralized offloading policy for each user, we model the problem as a partially observable Markov decision process (POMDP). Leveraging bandit learning methods, we introduce a decentralized task offloading solution in which edge users offload their computation tasks to nearby edge servers over selected communication channels. 

The second thrust focuses on user-driven requirements for resource-intensive applications, specifically the Quality of Experience (QoE) in 2D and 3D video streaming. Given the unique characteristics of millimeter-wave (mmWave) networks, we develop a beam alignment and buffer-predictive multi-user scheduling algorithm for 2D video streaming applications. This algorithm balances the trade-off between beam alignment overhead and playback buffer levels for optimal resource allocation across multiple users. We then extend our investigation to develop a joint rate adaptation and computation distribution framework for 3D video streaming in mmWave-based VR systems. Numerical results using real-world mmWave traces and 3D video datasets demonstrate significant improvements in video quality, rebuffering time, and quality variations perceived by users.

Finally, we develop novel edge computing solutions for multi-layer immersive video processing systems. By exploring and exploiting the elastic nature of computation tasks in these systems, we propose a multi-agent reinforcement learning (MARL) framework that incorporates two learning-based methods: the centralized phasic policy gradient (CPPG) and the independent phasic policy gradient (IPPG). IPPG leverages shared information and model parameters to learn edge offloading policies; however, during execution, each user acts independently based only on its local state information. This decentralized execution reduces the communication and computation overhead of centralized decision-making and improves scalability. We leverage real-world 4G, 5G, and WiGig network traces, along with 3D video datasets, to investigate the performance trade-offs of CPPG and IPPG when applied to elastic task computing.

Customer churn is a critical challenge for subscription-based businesses, as it directly impacts revenue, profitability, and long-term customer loyalty. Because retaining existing customers is more cost-effective than acquiring new ones, accurate churn prediction is essential for sustainable growth. This work presents a machine learning based framework for predicting and analyzing customer churn, coupled with an interactive Streamlit web application that supports real time decision making. Using historical customer data that includes demographic attributes, usage behavior, transaction history, and engagement patterns, the system applies extensive data preprocessing and feature engineering to construct a modeling-ready dataset. Multiple models Logistic Regression, Random Forest, and XGBoost are trained and evaluated using the Scikit-Learn framework. Model performance is assessed with metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to identify the most effective predictor of churn. The top performing models are serialized and deployed within a Streamlit interface that accepts individual customer inputs or batch data files to generate immediate churn predictions and summaries. Overall, this project demonstrates how machine learning can transform raw customer data into actionable business intelligence and provides a scalable approach to proactive customer retention management.

Latency–accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving, in particular, safety depends on both prediction quality and the end-to-end delay from sensing to actuation. We observe that (1) when latency is accounted for, the latency-optimal network configuration varies with scene context and compute availability; and (2) a single fixed-resolution model becomes suboptimal as conditions change.

We present a multi-resolution, end-to-end deep neural network for the CARLA urban driving challenge using monocular camera input. Our approach employs a convolutional neural network (CNN) that supports multiple input resolutions through per-resolution batch normalization, enabling runtime selection of an ideal input scale under a latency budget, as well as resolution retargeting, which allows multi-resolution training without access to the original training dataset.

We implement and evaluate our multi-resolution end-to-end CNN in CARLA to explore the latency–safety frontier. Results show consistent improvements in per-route safety metrics—lane invasions, red-light infractions, and collisions—relative to fixed-resolution baselines.

Reliable cellular connectivity is essential for modern services such as telehealth, precision agriculture, and remote education; yet, measuring network performance in rural areas presents significant challenges. Traditional drive testing cannot access large geographic areas between roads, while crowdsourced data provides insufficient spatial resolution in low-population regions. To address these limitations, we develop an open-source UAV-based measurement platform that integrates an onboard computation unit, commercial cellular modem, and automated flight control to systematically capture Radio Access Network (RAN) signals and end-to-end network performance metrics at different altitudes. Our platform collects synchronized measurements of signal strength (RSRP, RSSI), signal quality (RSRQ, SINR), latency, and bidirectional throughput, with each measurement tagged with GPS coordinates and altitude. Experimental results from a semi-rural deployment reveal a fundamental altitude-dependent trade-off: received signal power improves at higher altitudes due to enhanced line-of-sight conditions, while signal quality degrades from increased interference with neighboring cells. Our analysis indicates that most of the measurement area maintains acceptable signal quality, along with adequate throughput performance, for both uplink and downlink communications. We further demonstrate that strong radio signal metrics for individual cells do not necessarily translate to spatial coverage dominance such that the cell serving the majority of our test area exhibited only moderate performance, while cells with superior metrics contributed minimally to overall coverage. Next, we develop several machine learning (ML) models to improve the prediction accuracy of signal strength at unmeasured altitudes. Finally, we extend our measurement platform by integrating non-terrestrial network (NTN) user terminals with the UAV components to investigate the performance of Low-earth Orbit (LEO) satellite networks with UAV mobility. Our measurement results demonstrate that NTN offers a viable fallback option by achieving acceptable latency and throughput performance during flight operations. Overall, this work establishes a reproducible methodology for three-dimensional rural network characterization and provides practical insights for network operators, regulators, and researchers addressing connectivity challenges in underserved areas.

Python's dynamic nature makes performance enhancement challenging. Recently, a JIT compiler using a novel copy-and-patch compilation approach was implemented in the reference Python implementation, CPython. Our goal in this work is to study and understand the performance properties of CPython's new JIT compiler. To facilitate this study, we compare the quality and performance of the code generated by this new JIT compiler with a more mature and traditional meta-tracing based JIT compiler implemented in PyPy (another Python implementation). Our thorough experimental evaluation reveals that, while it achieves the goal of fast compilation speed, CPython's JIT severely lags in code quality/performance compared with PyPy. While this observation is a known and intentional property of the copy-and-patch approach, it results in the new JIT compiler failing to elevate Python code performance beyond that achieved by the default interpreter, despite significant added code complexity. In this thesis, we report and explain our novel experiments, results, and observations.

Recent research in Electronic Health Records (EHRs) has enabled personalized and longitudinal modeling of patient trajectories for health outcome improvement. Despite this progress, existing methods often struggle to capture the dynamic, heterogeneous, and interdependent nature of medical data. Specifically, many representation methods learn a rich set of EHR features in an independent way but overlook the intricate relationships among them. Moreover, data scarcity and bias, such as the cold-start scenarios where patients only have a few visits or rare conditions, remain fundamental challenges in clinical decision support in real-life. To address these challenges, this dissertation aims to introduce an integrated machine learning framework for sophisticated, interpretable, and adaptive EHR representation modeling. Specifically, the dissertation comprises three thrusts:

1. A time-aware graph transformer model that dynamically constructs personalized temporal graph representations that capture patient trajectory over different visits.

2. A contrasted multi-Intent recommender system that can disentangle the multiple temporal patterns that coexist in a patient’s long medical history, while considering distinct health profiles.

3. A few-shot meta-learning framework that can address the patient cold-start issue through a self- and peer-adaptive model enhanced by uncertainty-based filtering.

Together, these contributions advance a data-efficient, generalizable, and interpretable foundation for large-scale clinical EHR mining toward truly personalized medical outcome prediction.

In the light of rapid advancement of global connectivity and the increasing reliance on multilingual communication, speech-based Machine Translation (MT) systems have emerged as essential technologies for facilitating seamless cross-lingual interaction. These systems enable individuals and organizations to overcome linguistic boundaries by automatically translating spoken language in real time. However, despite their growing ubiquity in various applications such as virtual assistants, international conferencing, and accessibility services, the security and robustness of speech-based MT systems remain underexplored. In particular, limited attention has been given to understanding their vulnerabilities under adversarial conditions, where malicious actors intentionally craft or manipulate speech inputs to mislead or degrade translation performance.

This thesis presents a comprehensive investigation into the security landscape of speech-based machine translation systems from an adversarial perspective. We systematically categorize and analyze potential attack vectors, evaluate their success rates across diverse system architectures and environmental settings, and explore the practical implications of such attacks. Furthermore, through a series of controlled experiments and human-subject evaluations, we demonstrate that adversarial manipulations can significantly distort translation outputs in realistic use cases, thereby posing tangible risks to communication reliability and user trust.

Our findings reveal critical weaknesses in current MT models and underscore the urgent need for developing more resilient defense strategies. We also discuss open research challenges and propose directions for building secure, trustworthy, and ethically responsible speech translation technologies. Ultimately, this work contributes to a deeper understanding of adversarial robustness in multimodal language systems and provides a foundation for advancing the security of next-generation machine translation frameworks.

As high-performance computing (HPC) systems achieve exascale capabilities, traditional single-objective schedulers that optimize solely for performance prove inadequate for environments requiring simultaneous optimization of energy efficiency and system resilience. Current scheduling approaches result in suboptimal resource utilization, excessive energy consumption, and reduced fault tolerance in the demanding requirements of large-scale scientific applications. This dissertation proposes a novel multi-objective optimization framework that integrates graph neural networks (GNNs) with reinforcement learning (RL) to jointly optimize performance, energy efficiency, and system resilience in HPC workload scheduling. The central hypothesis posits that graph-structured representations of workloads and system states, combined with adaptive learning policies, can significantly outperform traditional scheduling methods in complex, dynamic HPC environments. The proposed framework comprises three integrated components: (1) GNN-RL, which combines graph neural networks with reinforcement learning for adaptive policy development; (2) EA-GATSched, an energy-aware scheduler leveraging Graph Attention Networks; and (3) HARMONIC (Holistic Adaptive Resource Management for Optimized Next-generation Interconnected Computing), a probabilistic model for workload uncertainty quantification. The proposed methodology encompasses novel uncertainty modeling techniques, scalable GNN-based scheduling algorithms, and comprehensive empirical evaluation using production supercomputing workload traces. Preliminary results demonstrate 10-19% improvements in energy efficiency while maintaining comparable performance metrics. The framework will be evaluated across makespan reduction, energy consumption, resource utilization efficiency, and fault tolerance in various operational scenarios. This research advances sustainable and resilient HPC resource management, providing critical infrastructure support for next-generation scientific computing applications.

Remote attestation is a process of obtaining verifiable evidence from a remote party to establish trust. A relying party makes a request of a remote target that responds by executing an attestation protocol producing evidence reflecting the target's system state and meta-evidence reflecting the evidence’s integrity and provenance. This process occurs in the presence of adversaries intent on misleading the relying party to trust a target they should not. This research introduces a robust approach for evaluating and comparing attestation protocols based on their relative resilience against such adversaries. I develop a Rocq-based, formally-verified mathematical model aimed at describing the difficulty for an active adversary to successfully compromise the attestation. The model supports systematically ranking attestation protocols by the level of adversary effort required to produce evidence that does not accurately reflect the target’s state. My work aims to facilitate the selection of a protocol resilient to adversarial attack.

Ice-penetrating radar systems are critical tools in glaciology and climate research, supporting scientific missions such as that of the Center for Oldest Ice Exploration (COLDEX). A primary challenge for these radars is achieving sufficient dynamic range to capture both strong, shallow reflections from the ice surface without saturating the radar's analog to digital converter (ADC), and extremely weak signals from the deep bedrock. This thesis presents a non-conventional analog receiver architecture and signal processing methodology designed to enhance the dynamic range of a radar system by utilizing characterized signal compression. The core of this approach relies on the non-linear properties of a set of RF power limiters to compress high-power received signals.

A complete receiver module was designed, simulated, implemented on a 4-layer printed circuit board for operation in the 600-900 MHz band, with the design being adaptable to other frequency ranges (e.g. 140-215 MHz). Multiple modules based on this design were manufactured for three different multichannel radar systems. Characterization of the manufactured receiver blocks demonstrates reproducible performance, confirming the well-defined non-linear input and output power relationship, which is essential for this technique.

To recover the original signal from the compressed data, this work approaches the inversion problem using a machine learning technique. A 3-layer neural network was trained on a test data set generated from an exponentially-varying, single-tone waveform, mapping the compressed receiver output back to the original input envelope. The trained model was then validated using a distinct, triangular-amplitude-modulated test signal. The results show that the neural network can accurately predict and reconstruct the original, uncompressed waveform envelope from the compressed receiver output for discrete frequencies within the band of operation. This work serves as a successful proof-of-concept for a decompression-based analog receiver, offering an alternate and effective pathway to enhancing the dynamic range of ice-sounding radar systems.