Defense Notices

Cold emailing remains an effective strategy for software service companies to improve organizational reach by acquiring clients. Generic emails often fail to get a response.
This project leverages Generative AI to automate the cold email generation. This project is built with the Llama-3.1 model and a Chroma vector database that supports the semantic search of keywords in the job description that matches the project portfolio links of software service companies. The application automatically extracts the technology related job openings for Fortune 500 companies. Users can either select from these extracted job postings or manually enter URL of a job posting, after which the system generates email and sends email upon approval. Advanced techniques like Chain-of-Thought Prompting and Few-Shot Learning were applied to improve the relevance making the email more responsive. This AI driven approach improves engagement and simplifies the business development process for software service companies.

Predictive maintenance plays a central role in reducing vehicle downtime and improving operational efficiency by using data-driven methods to classify the condition of automotive engines. Rather than relying on fixed service schedules or reacting to unexpected breakdowns, this approach leverages machine learning to distinguish between healthy and failed engines based on operational data.

In this project, engine telemetry data capturing key parameters such as engine speed, fuel pressure, and coolant temperature was used to train and evaluate several machine learning models, including logistic regression, random forest, k-nearest neighbors, and a neural network. To further enhance predictive performance, ensemble strategies such as soft voting and stacking were applied. The stacking ensemble, which combines the strengths of multiple classifiers through a meta-learning approach, demonstrated particularly effective results.

This classification-based framework demonstrates how data-driven fault detection can enhance automotive maintenance operations. By identifying engine failures more reliably, machine learning enables safer transportation, reduces maintenance costs, and enhances overall vehicle dependability. Beyond individual vehicles, such approaches have broader applications in fleet management, where proactive decision-making can improve service continuity, reduce operational risks, and increase customer satisfaction.

The Doppler tolerance of a waveform refers to its behavior when subjected to a fast-time Doppler shift imposed by scattering that involves nonnegligible radial velocity. While previous efforts have established decision-based criteria that lead to a binary judgment of Doppler tolerant or intolerant, it is also useful to establish a measure of the degree of Doppler tolerance. The purpose in doing so is to establish a consistent standard, thereby permitting assessment across different parameterizations, as well as introducing a Doppler “quasi-tolerant” trade-space that can ultimately inform automated/cognitive waveform design in increasingly complex and dynamic radio frequency (RF) environments. 

Separately, the application of slow-time coding (STC) to the Doppler-tolerant linear FM (LFM) waveform has been examined for disambiguation of multiple range ambiguities. However, using STC with non-adaptive Doppler processing often results in high Doppler “cross-ambiguity” side lobes that can hinder range disambiguation despite the degree of separability imparted by STC. To enhance this separability, a gradient-based optimization of STC sequences is developed, and a “multi-range” (MR) modification to the reiterative super-resolution (RISR) approach that accounts for the distinct range interval structures from STC is examined. The efficacy of these approaches is demonstrated using open-air measurements. 

The proposed work to appear in the final dissertation focuses on the connection between Doppler tolerance and STC. The first proposal includes the development of a gradient-based optimization procedure to generate Doppler quasi-tolerant random FM (RFM) waveforms. Other proposals consider limitations of STC, particularly when processed with MR-RISR. The final proposal introduces an “intrapulse” modification of the STC/LFM structure to achieve enhanced sup pression of range-folded scattering in certain delay/Doppler regions while retaining a degree of Doppler tolerance.

Scheduling a graph of moldable tasks, where each task can be executed by a varying number of

processors with execution time depending on the processor allocation, represents a fundamental

problem in high-performance computing (HPC). The online version of the scheduling problem

introduces an additional constraint: each task is only discovered when all its predecessors have

been completed. A key challenge for this online problem lies in making processor allocation

decisions without complete knowledge of the future tasks or dependencies. This uncertainty can

lead to inefficient resource utilization and increased overall completion time, or makespan. Recent

studies have provided theoretical analysis (i.e., derived competitive ratios) for certain processor

allocation algorithms. However, the algorithms’ practical performance remains under-explored,

and their reliance on fixed parameter settings may not consistently yield optimal performance

across varying workloads. In this thesis, we conduct a comprehensive evaluation of three processor

allocation strategies by empirically assessing their performance under widely used speedup models

and diverse graph structures. These algorithms are integrated into a List scheduling framework that

greedily schedules ready tasks based on the current processor availability. We perform systematic

tuning of the algorithms’ parameters and report the best observed makespan together with the

corresponding parameter settings. Our findings highlight the critical role of parameter tuning in

obtaining optimal makespan performance, regardless of the differences in allocation strategies.

The insights gained in this study can guide the deployment of these algorithms in practical runtime

systems.

Genetic Algorithms (GAs), a subclass of evolutionary algorithms, seek to apply the concept of natural selection to promote the optimization and furtherance of “something” designated by the user. GAs generate a population of chromosomes represented as value strings, score each chromosome with a “fitness function” on a defined set of criteria, and mutate future generations depending on the scores ascribed to each chromosome. In this project, each chromosome is a bitstring representing one canvased color artwork. Artworks are scored with a variety of design fundamentals and user preference. The artworks are then evolved through thousands of generations and the final piece is computationally drawn for analysis. While the rise of gradient-based optimization has resulted in more limited use-cases of GAs, genetic algorithms still have applications in various settings such as hyperparameter tuning, mathematical optimization, reinforcement learning, and black box scenarios. Neural networks are favored presently in image generation due to their pattern recognition and ability to produce new content; however, in cases where a user is seeking to implement their own vision through careful algorithmic refinement, genetic algorithms still find a place in visual computing.

Wireless edge systems typically need to complete timely computation and inference tasks under strict power, bandwidth, latency, and processing constraints. As AI models and datasets grow in size and complexity, the traditional model of sending all data to a remote cloud or running full inference on edge device becomes impractical. This creates a need for communication-computation co-design to enable efficient AI task processing at the wireless edge. To address this problem, we investigate task-aware communication-computation optimization for two specific problem settings.

First, we explore semantic communication that transmits only the information essential for the receiver’s computation tasks. We propose a semantic-aware and goal-oriented communication method for object detection. Our proposed approach is built upon the auto-encoders, with the encoder and the decoder are respectively implemented at the transmitter and receiver to extract semantic information for the specific computation goal (e.g., object detection). Numerical results show that transmitting only the necessary semantic features significantly improves the overall system efficiency.

Second, we study collaborative inference in wireless edge networks, where energy-constrained devices aim to complete delay-sensitive inference tasks. The inference computation is split between the device and an edge server, thereby achieving collaborative inference. We formulate a utility maximization problem under energy and delay constraints and propose Bayes-Split-Edge, which uses Bayesian optimization to determine the optimal transmission power and neural network split point. The proposed framework introduces a hybrid acquisition function that balances inference task utility, sample efficiency, and constraint violation penalties. We evaluate our approach using the VGG19 model, the ImageNet-Mini dataset, and real-world mMobile wireless channel datasets.

Overall, this research is aimed at developing efficient edge AI systems by incorporating the underlying wireless communications limitations and challenges into AI tasks processing.

Web browsers are notorious for consuming large amounts of memory. Yet, they have become the dominant framework for writing GUIs because the web languages are ergonomic for programmers and have a cross-platform reach. These benefits are so enticing that even a large portion of mobile apps, which have to run on resource-constrained devices, are running a web browser under the hood. Therefore, it is important to keep the memory consumption of web browsers as low as practicable.

In this thesis, we investigate the memory consumption of web browsers, in particular, compared to applications written in native GUI frameworks. We introduce smaps-profiler, a tool to profile the overall memory consumption of Linux applications that can report memory usage other profilers simply do not measure. Using this tool, we conduct experiments which suggest that most of the extra memory usage compared to native applications could be due the size of the web browser program itself. We discuss our experiments and findings, and conclude that even more rigorous studies are needed to profile GUI applications.

Open-source package managers (e.g., npm for Node.js) have become an established component of modern software development. Rather than creating applications from scratch, developers may employ modular software dependencies and frameworks--called packages--to serve as building blocks for writing larger applications. Package managers make this process easy. With a simple command line directive, developers are able to quickly fetch and install packages across vast open-source repositories. npm--the largest of such repositories--alone hosts millions of unique packages and serves billions of package downloads each week. 

However, the widespread code sharing resulting from open-source package managers also presents novel security implications. Vulnerable or malicious code hiding deep within package dependency trees can be leveraged downstream to attack both software developers and the end-users of their applications. This downstream flow of software dependencies--dubbed the software supply chain--is critical to secure.

This research provides a deep dive into the npm-centric software supply chain, exploring distinctive phenomena that impact its overall security and usability. Such factors include (i) hidden code clones--which may stealthily propagate known vulnerabilities, (ii) install-time attacks enabled by unmediated installation scripts, (iii) hard-coded URLs residing in package code, (iv) the impacts of open-source development practices, (v) package compromise via malicious updates, (vi) spammers disseminating phishing links within package metadata, and (vii) abuse of cryptocurrency protocols designed to reward the creators of high-impact packages. For each facet, tooling is presented to identify and/or mitigate potential security impacts. Ultimately, it is our hope that this research fosters greater awareness, deeper understanding, and further efforts to forge a new frontier for the security of modern software supply chains. 

Dual function radar communications (DFRC) is a method of co-designing a single radio frequency system to perform simultaneous radar and communications service. DFRC is ultimately a compromise between radar sensing performance and communications data throughput due to the conflicting requirements between the sensing and information-bearing signals.

A novel waveform-based DFRC approach is phase attached radar communications (PARC), where a communications signal is embedded onto a radar pulse via the phase modulation between the two signals. The PARC framework is used here in a new waveform design technique that designs the radar component of a PARC signal to match the PARC DFRC waveform expected power spectral density (PSD) to a desired spectral template. This provides better control over the PARC signal spectrum, which mitigates the issue of PARC radar performance degradation from spectral growth due to the communications signal. 

The characteristics of optimized PARC waveforms are then analyzed to establish a trade-space between radar and communications performance within a PARC DFRC scenario. This is done by sampling the DFRC trade-space continuum with waveforms that contain a varying degree of communications bandwidth, from a pure radar waveform (no embedded communications) to a pure communications waveform (no radar component). Radar performance, which is degraded by range sidelobe modulation (RSM) from the communications signal randomness, is measured from the PARC signal variance across pulses; data throughput is established as the communications performance metric. Comparing the values of these two measures as a function of communications symbol rate explores the trade-offs in performance between radar and communications with optimized PARC waveforms.

This PhD research tackles two critical challenges in NextG wireless networks: hybrid precoder design for wideband sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communications and privacy-preserving federated learning (FL) over wireless networks.

In the first part, we propose a novel hybrid precoding framework that integrates true-time delay (TTD) devices and phase shifters (PS) to counteract the beam squint effect - a significant challenge in the wideband sub-THz massive MIMO systems that leads to considerable loss in array gain. Unlike previous methods that only designed TTD values while fixed PS values and assuming unbounded time delay values, our approach jointly optimizes TTD and PS values under realistic time delays constraint. We determine the minimum number of TTD devices required to achieve a target array gain using our proposed approach. Then, we extend the framework to multi-user wideband systems and formulate a hybrid array optimization problem aiming to maximize the minimum data rate across users. This problem is decomposed into two sub-problems: fair subarray allocation, solved via continuous domain relaxation, and subarray gain maximization, addressed via a phase-domain transformation.

The second part focuses on preserving privacy in FL over wireless networks. First, we design a differentially-private FL algorithm that applies time-varying noise variance perturbation. Taking advantage of existing wireless channel noise, we jointly design differential privacy (DP) noise variances and users transmit power to resolve the tradeoffs between privacy and learning utility. Next, we tackle two critical challenges within FL networks: (i) privacy risks arising from model updates and (ii) reduced learning utility due to quantization heterogeneity. Prior work typically addresses only one of these challenges because maintaining learning utility under both privacy risks and quantization heterogeneity is a non-trivial task. We approach to improve the learning utility of a privacy-preserving FL that allows clusters of devices with different quantization resolutions to participate in each FL round. Specifically, we introduce a novel stochastic quantizer (SQ) that ensures a DP guarantee and minimal quantization distortion. To address quantization heterogeneity, we introduce a cluster size optimization technique combined with a linear fusion approach to enhance model aggregation accuracy. Lastly, inspired by the information-theoretic rate-distortion framework, a privacy-distortion tradeoff problem is formulated to minimize privacy loss under a given maximum allowable quantization distortion. The optimal solution to this problem is identified, revealing that the privacy loss decreases as the maximum allowable quantization distortion increases, and vice versa.

This research advances hybrid array optimization for wideband sub-THz massive MIMO and introduces novel algorithms for privacy-preserving quantized FL with diverse precision. These contributions enable high-throughput wideband MIMO communication systems and privacy-preserving AI-native designs, aligning with the performance and privacy protection demands of NextG networks.