I2S Masters/ Doctoral Theses

Just as the internet-of-things leverages machine learning over a vast amount of data produced by an innumerable number of sensors, the Internet of Catalysis program uses similar strategies with catalysis research. One application of the Internet of Catalysis strategy is treating research papers as datapoints, rich with text, figures, and tables. Prior research within the program focused on machine learning models applied strictly over text.This project is the first step of the program in creating a machine learning model from the images of catalysis research papers. Specifically, this project creates a dataset of faradaic efficiency graphs using transfer learning from pretrained models. The project utilizes FasterRCNN_ResNet50_FPN, LayoutLMv3SequenceClassification, and computer vision techniques to recognize figures, extract all graphs, then classify the faradaic efficiency graphs.

Downstream of this project, researchers will create a graph reading model to integrate with large language models. This could potentially lead to a multimodal model capable of fully learning from images, tables, and texts of catalysis research papers. Such a model could then guide experimentation on reaction conditions, catalysts, and production.

Significant advances in information and networking technologies have transformed Cyber-Physical Systems (CPS) into networked cyber-physical systems (NCPS). A noteworthy example of such systems is smart grid networks, which include distributed energy resources (DERs), renewable generation, and the widespread adoption of Electric Vehicles (EVs). Such complex NCPS require intelligent and autonomous control solutions. For example, the increasing number of EVs introduces significant sources of demand and user behavior uncertainty that can jeopardize grid stability during peak hours. Traditional model-based demand-supply controls fail to accurately model and capture the complex nature of smart grid systems in the presence of different uncertainties and as the system size grows. To address these challenges, data-driven approaches have emerged as an effective solution for informed decision-making, predictive modeling, and adaptive control to enhance the resiliency of NCPS in uncertain environments.

As a powerful data-driven approach, Multi-Agent Reinforcement Learning (MARL) enables agents to learn and adapt in dynamic and uncertain environments. However, MARL techniques introduce complexities related to communication, coordination, and synchronization among agents. In this PhD research, we investigate autonomous control for smart grid decision networks using MARL. First, we examine the issue of imperfect state information, which frequently arises due to the inherent uncertainties and limitations in observing the system state.

Second, we focus on the cooperative behavior of agents in distributed MARL frameworks, particularly under the central training with decentralized execution (CTDE) paradigm. We provide theoretical results and variance analysis for stochastic and deterministic cooperative MARL algorithms, including Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Multi-Agent Proximal Policy Optimization (MAPPO), and Dueling MAPPO. These analyses highlight how coordinated learning can improve system-wide decision-making in uncertain and dynamic environments like EV networks.

Third, we address the scalability challenge in large-scale NCPS by introducing a hierarchical MARL framework based on a cluster-based architecture. This framework organizes agents into coordinated subgroups, improving scalability while preserving local coordination. We conduct a detailed variance analysis of this approach to demonstrate its effectiveness in reducing communication overhead and learning complexity. This analysis establishes a theoretical foundation for scalable and efficient control in large-scale smart grid applications.

This project presents the development of a custom Convolutional Neural Network (CNN) designed to classify object states—such as sliced, diced, or peeled—in robotic cooking environments. Recognizing fine-grained object states is critical for context-aware manipulation yet remains a challenging task due to the visual similarity between states and the limited availability of cooking-specific datasets. To address these challenges, we built a lightweight, non-pretrained CNN trained on a curated dataset of 11 object states. Starting with a baseline architecture, we progressively enhanced the model using data augmentation, optimized dropout, batch normalization, Inception modules, and residual connections. These improvements led to a performance increase from ~45% to ~52% test accuracy. The final model demonstrates improved generalization and training stability, showcasing the effectiveness of combining classical and advanced deep learning techniques. This work contributes toward real-time state recognition for autonomous robotic cooking systems, with implications for assistive technologies in domestic and elder care settings.

Although the Center for Remote Sensing of Ice Sheets (CReSIS) performs several radar calibration steps to produce Operation IceBridge (OIB) radar depth sounder data products, these datasets are not radiometrically calibrated and the swath array processing uses ideal (rather than measured [calibrated]) steering vectors. Any errors in the steering vectors, which describe the response of the radar as a function of arrival angle, will lead to errors in positioning and backscatter that subsequently affect estimates of basal conditions, ice thickness, and radar attenuation. Scientific applications that estimate physical characteristics of surface and subsurface targets from the backscatter are limited with the current data because it is not absolutely calibrated. Moreover, changes in instrument hardware and processing methods for OIB over the last decade affect the quality of inter-seasonal comparisons. Recent methods which interpret basal conditions and calculate radar attenuation using CReSIS OIB 2D radar depth sounder echograms are forced to use relative scattering power, rather than absolute methods.

As an active target calibration is not possible for past field seasons, a method that uses natural targets will be developed. Unsaturated natural target returns from smooth sea-ice leads or lakes are imaged in many datasets and have known scattering responses. The proposed method forms a system of linear equations with the recorded scattering signatures from these known targets, scattering signatures from crossing flight paths, and the radiometric correction terms. A least squares solution to optimize the radiometric correction terms is calculated, which minimizes the error function representing the mismatch in expected and measured scattering. The new correction terms will be used to correct the remaining mission data. The radar depth sounder data from all OIB campaigns can be reprocessed to produce absolutely calibrated echograms for the Arctic and Antarctic. A software simulator will be developed to study calibration errors and verify the calibration software. The software for processing natural targets and crossovers will be made available in CReSIS’s open-source polar radar software toolbox. The OIB data will be reprocessed with new calibration terms, providing to the data user community a complete set of radiometrically calibrated radar echograms for the CReSIS OIB radar depth sounder for the first time.

With the increasing congestion of the usable RF spectrum, it is increasingly necessary for communication and radar systems to share the same frequencies without disturbing one another. To accomplish this, research has focused on designing a class of non-repeating radar waveforms that appear as noise at the receiver of uncooperative systems, but the peak power from high-power pulsed systems can still overwhelm nearby in-band systems. Therefore, to minimize peak power while maximizing the total energy on target, radar systems must transition to operating at a 100% duty cycle, which inherently requires Simultaneous Transmit and Receive (STAR) operation.

One inherent difficulty when operating monostatic STAR systems is the direct path coupling interference that can saturate a number of components in the radar’s receive chain, which makes digital processing methods that remove this interference ineffective. This thesis proposes a method to reduce the self-interference between the radar’s transmitter in receiver prior to the receiver’s sensitive components to increase the power that the radar can transmit at. By using a combination of tests that manipulate the timing, phase, and magnitude of a secondary waveform that is injected into the radar just before the receiver, upwards of 35.0 dB of self-interference cancellation is achieved for radar waveforms with bandwidths of up to 100 MHz at both S-band and X-band in both simulation and open-air testing.

Recent advancements in optical fiber technology have proven to be invaluable in a variety of fields, extending far beyond high-speed communications. These innovations enable optical fiber sensing, which plays a critical role across diverse applications, from medical diagnostics to infrastructure monitoring and automotive systems. This research focuses on leveraging commercially available coherent optical transceiver systems to develop novel measurement techniques for characterizing optical fiber properties. Specifically, our goal is to leverage a digitally chirped frequency-modulated continuous wave (FMCW) to extract detailed information about optical fiber characteristics, as well as target range. Through this approach, we aim to enable more accurate and fast assessments of fiber performance and integrity, while exploring the potential for utilizing existing optical communication networks to enhance fiber characterization capabilities. This goal is investigated through three distinct projects: (1) fiber type characterization based on intensity-modulated electrostriction response, (2) self-homodyne coherent Light Detection and Ranging (LiDAR) system for target range and velocity detection, and (3) birefringence measurements using a coherent Polarization-sensitive Optical Frequency Domain Reflectometer (OFDR) system.

Electrostriction in an optical fiber is introduced by interaction between the forward propagated optical signal and the acoustic standing waves in the radial direction resonating between the center of the core and the cladding circumference of the fiber. The response of electrostriction is dependent on fiber parameters, especially the mode field radius. We demonstrated a novel technique of identifying fiber types through the measurement of intensity modulation induced electrostriction response. As the spectral envelope of electrostriction induced propagation loss is anti-symmetrical, the signal to noise ratio can be significantly increased by subtracting the measured spectrum from its complex conjugate. We show that if the field distribution of the fiber propagation mode is Gaussian, the envelope of the electrostriction-induced loss spectrum closely follows a Maxwellian distribution whose shape can be specified by a single parameter determined by the mode field radius.         
We also present a self-homodyne FMCW LiDAR system based on a coherent receiver. By using the same linearly chirped waveform for both the LiDAR signal and the local oscillator, the self-homodyne coherent receiver performs frequency de-chirping directly in the photodiodes, significantly simplifying signal processing. As a result, the required receiver bandwidth is much lower than the chirping bandwidth of the signal. Multi-target detection is demonstrated experimentally, and while only amplitude modulation is required in the LiDAR transmitter, the phase-diversity coherent receiver enables simultaneous detection of both range and velocity for each target, along with the sign Image of the target’s velocity.

In addition, we demonstrate a polarization-sensitive OFDR system utilizing a commercially available digital coherent optical transceiver to generate a linear frequency chirp via carrier-suppressed single-sideband modulation. This method ensures linearity in chirping and phase continuity of the optical carrier. The coherent homodyne receiver, incorporating both polarization and phase diversity, recovers the state of polarization (SOP) of the backscattered optical signal along the fiber, mixing with an identically chirped local oscillator. With a spatial resolution of approximately Image, a Image chirping bandwidth, and a Image measurement time, this system enables precise birefringence measurements. By employing three mutually orthogonal SOPs of the launched optical signal, we can measure birefringence vectors Image along the fiber, providing not only the magnitude of birefringence but also the direction of any external pressure applied to the fiber.

Binary similarity is a fundamental technique that enables software analysis practitioners to compare machine-level code at scale and with fine granularity. With application in software reverse engineering, vulnerability research, malware attribution and more, state-of-the-art binary similarity tools have undergone thorough research and development to account for variations in compilers, optimizations, machine architectures, and even obfuscations. And, although these tools aim to compare and detect binary-level code segments generated from similar or identical source code, no preexisting work has investigated the effects of source languages other than C and C++. This thesis addresses this research gap by presenting a thorough investigation of SOTA binary similarity tools when applied to modern compiled languages, Rust and Golang.

To adequately evaluate the capabilities of the available binary similarity approaches, this work includes three distinct tools - BSim, a new component of the Ghidra Software Reverse Engineering Framework, which utilizes a clustering based similarity mechanism; BinDiff, an industry-recognized tool using graph-based comparisons; and jTrans, a BERT-based model fine-tuned to the binary similarity task. First, to enable this work, we introduce a new dataset of Rust and Golang binaries compiled from leading open-source projects in the Homebrew and Arch Linux repositories. Comprised of 800 binaries and over 1 million functions, this dataset was built to represent a broad range of implementation styles, application diversity, and source language features. Next, the main investigation of this thesis is presented wherein we asses each approach's ability to accurately report semantically equivalent functions compiled from the same source code. Results across the three tools reveal a systematic degradation of precision when comparing binaries produced by Rust and Go rather than those produced by C and C++. Finally, we provide a technical demonstration which highlights the implications of these results and discuss near- and long-term solutions to more adequately equip binary analysis practitioners.  

Deep learning is widely used in healthcare, finance, and other critical domains, raising concerns about system trustworthiness. However, deep learning models and data still face three types of critical attacks: model theft, identity impersonation, and abuse of AI-generated content (AIGC). To address model theft, homomorphic encryption has been explored for privacy-preserving inference, but it remains highly inefficient. To counter identity impersonation, prior work focuses on detection, disruption, and tracing—yet fails to protect source and target images simultaneously. To prevent AIGC abuse, methods like evaluation, watermarking, and machine unlearning exist, but text-driven image editing remains largely unprotected.

This report addresses the above challenges through three key designs. First, to enable privacy-preserving inference while accelerating homomorphic encryption, we propose PrivDNN, which selectively encrypts the most critical model parameters, significantly reducing encrypted operations. We design a selection score to evaluate neuron importance and use a greedy algorithm to iteratively secure the most impactful neurons. Across four models and datasets, PrivDNN reduces encrypted operations by 85%–98%, and cuts inference time and memory usage by over 97% while preserving accuracy and privacy. Second, to counter identity impersonation in deepfake face-swapping, where both the source and target can be exploited, we introduce PhantomSeal, which embeds invisible perturbations to encode a hidden “cloak” identity. When used as a target, the resulting content displays visible artifacts; when used as a source, the generated deepfake is altered to resemble the cloak identity. Evaluations across two generations of deepfake face-swapping show that PhantomSeal reduces attack success from 97% to 0.8%, with 95% of outputs recognized as the cloak identity, providing robust protection against manipulation. Third, to prevent AIGC abuse, we construct a comprehensive dataset, perform large-scale human evaluation, and establish a benchmark for detecting AI-generated artwork to better understand abuse risks in AI-generated content. Building on this direction, we propose Protecting Copyright against Image Editing (PCIE) to address copyright infringement in text-driven image editing. PCIE embeds an invisible copyright mark into the original image, which transforms into a visible watermark after text-driven editing to automatically reveal ownership upon unauthorized modification.

Millimeter Wave (mmWave) communication technologies have the potential to establish high data rates for next-generation wireless networks, as well as enable novel applications that were previously untenable due to high throughput requirements.  Yet reliable and efficient mmWave communication remains challenged by intermittent link quality due to user mobility and frequent line-of-sight (LoS) blockage, thereby making the links unavailable or more costly to use.  These factors are further exacerbated in multi-user settings where beam alignment overhead, limited RF chains, and heterogeneous user requirements must be balanced.  In this work, we present a hybrid multi-user scheduling solution that jointly accounts for mobility-and blockage-induced unavailability to enhance user experience in mmWave video streaming applications.  Our approach integrates two key components: (i) a blockage-aware scheduling strategy modeled via a Restless Multi-Armed Bandit (RMAB) formulation and prioritized using Whittle Indexing, and (ii) a mobility-aware geometric model that estimates beam alignment overhead cost as a function of receiver motion.  We develop a comprehensive and efficient index-based scheduler that fuses these models and leverages contextual information, such as receiver distance, mobility history, and queue state, to schedule multiple users in order to maximize throughput. Simulation results demonstrate that our approach reduces system queue backlog and improves fairness compared to round-robin and traditional index-based baselines.

With the development of Convolutional Neural Networks (CNNs), computer vision has entered a new era, significantly enhancing the performance of tasks such as image classification, object detection, segmentation, and recognition. Furthermore, the introduction of Transformer architectures has brought the attention mechanism and a global perspective to computer vision, advancing the field to a new level. The inductive bias inherent in CNNs makes convolutional models particularly well-suited for processing images and videos. On the other hand, the attention mechanism in Transformer models allows them to capture global relationships between tokens. While Transformers often require more data and longer training periods compared to their convolutional counterparts, they have the potential to achieve comparable or even superior performance when the constraints of data availability and training time are mitigated.

In this work, we propose more efficient and effective CNNs and Transformers to increase the performance of object detection and recognition. (1) A novel approach is proposed for real-time detection and tracking of small golf balls by combining object detection with the Kalman filter. Several classical object detection models were implemented and compared in terms of detection precision and speed. (2) To address the domain shift problem in object detection, we employ generative adversarial networks (GANs) to generate images from different domains. The original RGB images are concatenated with the corresponding GAN-generated images to form a 6-channel representation, improving model performance across domains. (3) A dynamic strategy for improving label assignment in modern object detection models is proposed. Rather than relying on fixed or statistics-based adaptive thresholds, a dynamic paradigm is introduced to define positive and negative samples. This allows more high-quality samples to be selected as positives, reducing the gap between classification and IoU scores and producing more accurate bounding boxes. (4) An efficient hybrid architecture combining Vision Transformers and convolutional layers is introduced for object recognition, particularly for small datasets. Lightweight depth-wise convolution modules bypass the entire Transformer block to capture local details that the Transformer backbone might overlook. The majority of the computations and parameters remain within the Transformer architecture, resulting in significantly improved performance with minimal overhead. (5) An innovative Multi-Overlapped-Head Self-Attention mechanism is introduced to enhance information exchange between heads in the Multi-Head Self-Attention mechanism of Vision Transformers. By overlapping adjacent heads during self-attention computation, information can flow between heads, leading to further improvements in vision recognition.