I2S Masters/ Doctoral Theses

Microelectronics supply chains increasingly rely on globally distributed design, fabrication, integration, and deployment processes, making traditional assumptions of trusted hardware inadequate. Security in this setting can be understood through a zero-trust microelectronics supply-chain model, in which neither manufacturing partners nor procured hardware platforms are assumed trustworthy by default. Two complementary threat scenarios are considered in the proposed research. In the first scenario, custom Integrated Circuits (ICs) fabricated through potentially untrusted foundries are examined, where design-for-security protections intended to prevent piracy, overproduction, and intellectual-property theft can themselves become vulnerable to attacks. In this scenario, hardware Trojan-assisted meta-attacks are used to show that such protections can be systematically identified and subverted by fabrication-stage adversaries. In the second scenario, commercial off-the-shelf ICs are considered from the perspective of end users and procurers, where internal design visibility is unavailable and hardware trustworthiness cannot be directly verified. For this setting, runtime-oriented protection mechanisms are developed to safeguard sensitive computation against malicious hardware behavior and side-channel leakage. Building on these two scenarios, a future research direction is outlined for side-channel-driven vulnerability discovery in off-the-shelf devices, motivated by the need to evaluate and test such platforms prior to deployment when no design information is available. The proposed direction explores gray-box security evaluation using power and electromagnetic side-channel analysis to identify anomalous behaviors and potential vulnerabilities in opaque hardware platforms. Together, these directions establish a foundation for analyzing and mitigating security risks across zero-trust microelectronics supply chains.

Many High Performance Computing (HPC) applications following the Bulk Synchronous Parallel (BSP) model are increasingly deployed in cloud-native, multi-tenant container environments such as Kubernetes. Unlike dedicated HPC clusters, these shared platforms introduce resource virtualization and variability, making BSP applications more susceptible to performance fluctuations.

Workload imbalance across supersteps can trigger the straggler effect, where faster tasks wait at synchronization barriers for slower ones, increasing overall execution time. Existing BSP resource management approaches typically assume static workloads and reuse a single configuration throughout execution. However, real-world workloads vary due to dynamic data and system conditions, making static configurations suboptimal. This limitation underscores the need for adaptive resource management strategies that respond to workload changes while considering reconfiguration costs.

To address these limitations, we evaluate a dynamic, data-driven resource management framework tailored for cloud-native BSP applications. This approach integrates workload profiling, time-series forecasting, and predictive performance modeling to estimate task execution behavior under varying workload and resource conditions. The framework explicitly models the trade-off between performance gains achieved through reconfiguration and the associated checkpointing and migration costs incurred during container reallocation. Multiple reconfiguration strategies are evaluated, spanning simple window-based heuristics, dynamic programming methods, and reinforcement learning approaches. Through extensive experimental evaluation, this framework demonstrates up to 24.5% improvement in total execution time compared to a baseline static configuration. Furthermore, we systematically analyze the performance of each strategy under varying workload characteristics, simulation lengths, and checkpoint penalties, and provide guidance on selecting the most appropriate strategy for a given workload environment.

Non-coding RNAs fold into complex 3D structures that govern their biological functions, with RNA structural motifs (RSMs) serving as conserved building blocks of this architecture.
These motifs are defined by characteristic base-interaction patterns, making accurate identification and classification of RNA interactions essential for understanding RNA structure and function.

Despite their biological importance, accurately identifying and classifying these interactions remains challenging because the available data are highly variable in quality and scarce in quantity. This compromises annotation reliability, hinders the construction of trustworthy ground truth for systematic assessment, and restricts the supply of reliable training examples needed for supervised learning.

To address this, we introduce NoBIAS, the first resolution-aware, integrated machine learning-based suite for annotating base interactions from 3D RNA structures, inspired by human pattern recognition, augmented with structure prediction for data enrichment, and evaluated on a carefully curated, stratified benchmark.

NoBIAS is a hierarchical framework for RNA base-interaction annotation that integrates interaction-specific inductive biases with multimodal representation learning. By combining a convolution-augmented, rule-guided module for stacking interactions with complementary graph and image encoders for pairing interactions, NoBIAS captures both structural priors and local visual cues of RNA base doublets. A performance-calibrated logit fusion scheme then adaptively integrates modality-specific predictions based on local-structural resolution, enabling robust inference across heterogeneous 3D RNA structures.

Evaluation across multiple benchmark tiers: spanning consensus, homolog-supported, and manually verified cases, shows that NoBIAS consistently outperforms existing methods under increasingly challenging conditions. Together, the NoBIAS design and its evaluation framework provide a systematic foundation for robust RNA base-interaction annotation, enabling more reliable analysis of RNA structure under realistic uncertainty.

Secure and scalable authentication has become a primary requirement in modern digital ecosystems, where both human biometrics and electronic identities must be verified under noise, large population growth and resource constraints. Existing approaches often struggle to simultaneously provide storage efficiency, dynamic updates and strong authentication reliability. The proposed work advances a unified probabilistic framework based on Hierarchical Bloom Filter (HBF) architectures to address these limitations across biometric and hardware domains. The first contribution establishes the Dynamic Hierarchical Bloom Filter (DHBF) as a noise-tolerant and dynamically updatable authentication structure for large-scale biometrics. Unlike static Bloom-based systems that require reconstruction upon updates, DHBF supports enrollment, querying, insertion and deletion without structural rebuild. Experimental evaluation on 30,000 facial biometric templates demonstrates 100% enrollment and query accuracy, including robust acceptance of noisy biometric inputs while maintaining correct rejection of non-enrolled identities. These results validate that hierarchical probabilistic encoding can preserve both scalability and authentication reliability in practical deployments. Building on this foundation, Bio-BloomChain integrates DHBF into a blockchain-based smart contract framework to provide tamper-evident, privacy-preserving biometric lifecycle management. The system stores only hashed and non-invertible commitments on-chain while maintaining probabilistic verification logic within the contract layer. Large-scale evaluation again reports 100% enrollment, insertion, query and deletion accuracy across 30,000 templates, therefore, solving the existing problem of blockchains being able to authenticate noisy data. Moreover, the deployment analysis shows that execution on Polygon zkEVM reduces operational costs by several orders of magnitude compared to Ethereum, therefore, bringing enrollment and deletion costs below $0.001 per operation which demonstrate the feasibility of scalable blockchain biometric authentication in practice. Finally, the hierarchical probabilistic paradigm is extended to electronic hardware authentication through the Persistent Hierarchical Bloom Filter (PHBF). Applied to electronic fingerprints derived from physical unclonable functions (PUFs), PHBF demonstrates robust authentication under environmental variations such as temperature-induced noise. Experimental results show zero-error operation at the selected decision threshold and substantial system-level improvements as well as over 10^5 faster query processing and significantly reduced storage requirements compared to large scale tracking.

Recent advancements in optical technology are invaluable in a variety of fields, extending far beyond high-speed communications. These innovations enable optical 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 transceivers to develop novel measurement techniques to extract detailed information about optical fiber characteristics, as well as target information. Through this approach, we aim to enable 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) coherent Light Detection and Ranging (LiDAR) system for target range and velocity detection through different waveform design, including experimental validation of frequency modulation continuous wave (FMCW) implementations and theoretical analysis of orthogonal frequency division multiplexing (OFDM) based approaches and (3) birefringence measurements using a coherent Polarization-sensitive Optical Frequency Domain Reflectometer (P-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. Simultaneous multi-target of range and velocity detection is demonstrated experimentally. Furthermore, we explore the use of commercially available coherent transceivers for joint communication and sensing using OFDM waveforms.

In addition, we demonstrate a P-OFDR system utilizing a 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 5 mm, a 26 GHz chirping bandwidth, and a 200 us measurement time, this system enables precise birefringence measurements. By employing three mutually orthogonal SOPs of the launched optical signal, we measure relative birefringence vectors along the fiber.

Accurate biological sequence annotation and privacy-aware clinical modeling are central challenges in modern computational biology and biomedical AI. This dissertation presents scalable and interpretable deep learning frameworks spanning protein family classification, metal-ion binding prediction, and privacy-preserving electrocardiogram (ECG) representation learning. First, we introduce GPCR-SLM, a lightweight transformer-based framework for high-resolution classification of G-protein coupled receptors (GPCRs), one of the largest and most pharmacologically important protein families, targeted by approximately 35% of FDA-approved drugs. Unlike traditional homology-based tools such as BLAST and HMMER, which struggle to distinguish closely related families with low sequence similarity, our knowledge-distilled small language model achieves 99% accuracy across 86 GPCR families. The framework significantly outperforms BLAST (86.4%) and HMMER (91%) while delivering a 33.5× computational speedup compared to large protein language models, enabling scalable functional annotation as protein databases continue to expand. 

Second, we present an end-to-end deep learning pipeline for protein–metal-ion binding prediction. Binding site annotation is traditionally labor-intensive and limited by handcrafted features or predefined residue sets. We systematically evaluate five state-of-the-art protein language models and incorporate positional encoding to capture long-range residue dependencies. Our approach achieves a Matthews Correlation Coefficient (MCC) of 0.89 with precision, recall, and F1 scores exceeding 95% for six major metal ions under 10-fold cross-validation, demonstrating robust predictive performance and improved biological interpretability. Finally, we address fairness and privacy in clinical AI through a variational autoencoder (VAE) framework for ECG representation learning. Because ECGs inherently encode sensitive soft biometrics such as sex, age, and race, we design a dual-discriminator architecture that suppresses demographic information while preserving clinically relevant signals. The reconstructed ECGs substantially reduce demographic identifiability while maintaining strong predictive performance for reduced left ventricular ejection fraction, left ventricular hypertrophy, and 5-year mortality. 

Collectively, this work advances parameter-efficient, scalable, and privacy-conscious deep learning methodologies for both molecular and clinical domains, bridging computational protein science and trustworthy biomedical AI. 

Motor difficulties show up in as many as 90% of people with autism, but surprisingly few, somewhere between 13% and 32%, ever get motor-focused help. A big part of the problem is that the tools we have for measuring motor skills either rely on a clinician's subjective judgment or require expensive lab equipment that most families will never have access to. This dissertation tries to close that gap with three projects, all built around the idea that a regular webcam and some well-designed deep learning models can do much of what costly motion-capture labs do today.

The first project asks a straightforward question: can a computer tell the difference between how someone with autism moves and how a typically developing person moves, just by watching a short video? The answer, it turns out, is yes. We built an ensemble of three neural networks, each one tuned to notice something different. One focuses on how joints coordinate with each other spatially, other zeroes in on the timing of movements, and the third learns which body-part relationships matter most for a given clip. We tested the system on 582 videos from 118 people (69 with ASD and 49 without) performing simple everyday actions like stirring or hammering. The ensemble correctly classifies 95.65% of cases. The timing-focused model on its own hits 92%, which is nearly 10 points better than a standard recurrent network baseline. And when all three models agree, accuracy climbs above 98%.

The second project deals with stimming, the repetitive behaviors like arm flapping, head banging, and spinning that are common in autism. Working with 302 publicly available videos, we trained a skeleton-based model that reaches 91% accuracy using body pose alone. That is more than double the 47% that previous work managed on the same benchmark. When we combine the pose information with what the raw video shows through a late fusion approach, accuracy jumps to 99.9%. Across the entire test set, only a single video was misclassified.

The third project is E-MotionSpec, a web platform designed for clinicians and researchers who want to track motor development over time. It runs in any browser, uses MediaPipe to estimate body pose in real time, and extracts 44 movement features grouped into seven domains covering things like how smoothly someone moves, how quickly they initiate actions, and how coordinated their limbs are. We validated the platform on the same 118-participant dataset and found 36 features with statistically significant differences between the ASD and typically developing groups. Smoothness and initiation timing stood out as the strongest discriminators. The platform also includes tools for comparing sessions over time using frequency analysis and dynamic time warping, so a clinician can actually see whether someone's motor patterns are changing across weeks or months.

Taken together, these three projects offer a practical path toward earlier identification and better ongoing monitoring of motor difficulties in autism. Everything runs on a webcam and a web browser. No motion-capture suits, no force plates, no specialized labs. That matters most for the families, schools, and clinics that need these tools the most and can least afford the alternatives.

Guava leaf diseases significantly affect crop yield and quality, making timely detection essential for effective disease management. This project presents an end-to-end software system for automated guava leaf disease detection using deep learning and transfer learning techniques. Multiple pretrained convolutional neural network (CNN) architectures, including ResNet, AlexNet, VGG, SqueezeNet, DenseNet, Inception-v3, and EfficientNet, were adapted through feature extraction and trained on a guava leaf image dataset.

The system allows users to either capture an image using a camera or upload an existing leaf image through a software interface. The input image is processed and classified by the trained deep learning model, and the predicted disease class is displayed to the user. The dataset was divided into training, validation, and test sets to ensure robust performance evaluation, and final test accuracy was used to measure generalization on unseen data.

Experimental results demonstrate that transfer learning enables accurate and efficient guava leaf disease classification. Among the evaluated models, the best-performing architecture achieved an accuracy between 97% to 99%. Overall, the developed software provides a practical and user-friendly solution for real-world agricultural disease diagnosis.

The rapid growth of Internet of Things (IoT) technology has brought unprecedented convenience to everyday life, enabling users to deploy automation rules and develop IoT apps tailored to their specific needs. However, modern IoT ecosystems consist of numerous devices, applications, and platforms that interact continuously. As a result, users are increasingly exposed to complex and subtle security and privacy risks that are difficult to fully comprehend. Even interactions among seemingly harmless apps can introduce unforeseen security and privacy threats. In addition, violations of memory integrity can undermine the security guarantees on which IoT apps rely.

The first approach investigates hidden cross-app privacy leakage risks in IoT apps. These risks arise from cross-app interaction chains formed among multiple seemingly benign IoT apps. Our analysis reveals that interactions between apps can expose sensitive information such as user identity, location, tracking data, and activity patterns. We quantify these privacy leaks by assigning probability scores to evaluate risk levels based on inferences. In addition, we provide a fine-grained categorization of privacy threats to generate detailed alerts, enabling users to better understand and address specific privacy risks.

The second approach addresses cross-app interaction threats in IoT automation systems by leveraging a logic-based analysis model grounded in event relations. We formalize event relationships, detect event interferences, and classify rule conflicts, then generate risk scores and conflict rankings to enable comprehensive conflict detection and risk assessment. To mitigate the identified interaction threats, an optimization-based approach is employed to reduce risks while preserving system functionality. This approach ensures comprehensive coverage of cross-app interaction threats and provides a robust solution for detecting and resolving rule conflicts in IoT environments.

To support the development and rigorous evaluation of these security analyses, we further developed a large-scale, manually verified, and comprehensive dataset of real-world IoT apps. This clean and diverse benchmark dataset supports the development and validation of IoT security and privacy solutions. All proposed approaches are evaluated using this dataset of real-world apps, collectively offering valuable insights and practical tools for enhancing IoT security and privacy against cross-app threats. Furthermore, we examine the integrity of the execution environment that supports IoT apps. We show that, even under non-privileged execution, carefully crafted memory access patterns can induce bit flips in physical memory, allowing attackers to corrupt data and compromise system integrity without requiring elevated privileges.

In resource‑constrained contested environments (RCCEs), communications are routinely censored, surveilled, or disrupted by nation‑state adversaries, leaving at‑risk populations—including protesters, dissidents, disaster‑affected communities, and military units—without secure connectivity. This dissertation introduces MeshBLanket, a Bluetooth Mesh‑based framework designed for low‑power, low‑throughput messaging with minimal electromagnetic spectrum exposure. Built on commercial off‑the‑shelf hardware, MeshBLanket extends the Bluetooth Mesh specification with automated provisioning and network‑wide key refresh to enhance scalability and resilience.

We evaluated MeshBLanket through field experimentation (range, throughput, battery life, and security enhancements) and qualitative interviews with ten senior U.S. Army communications experts. Thematic analysis revealed priorities of availability, EMS footprint reduction, and simplicity of use, alongside adoption challenges and institutional skepticism. Results demonstrate that MeshBLanket maintains secure messaging under load, supports autonomous key refresh, and offers operational relevance at the forward edge of battlefields.

Beyond military contexts, parallels with protest environments highlight MeshBLanket’s broader applicability for civilian populations facing censorship and surveillance. By unifying technical experimentation with expert perspectives, this work contributes a proof‑of‑concept communications architecture that advances secure, resilient, and user‑centric connectivity in environments where traditional infrastructure is compromised or weaponized.