I2S Masters/ Doctoral Theses

All students and faculty are welcome to attend the final defense of I2S graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.

Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.

Upcoming Defense Notices

Sai Rithvik Gundla

Beyond Regression Accuracy: Evaluating Runtime Prediction for Scheduling Input Sensitive Workloads

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Hongyang Sun, Chair
Arvin Agah
David Johnson


Abstract

Runtime estimation plays a structural role in reservation-based scheduling for High Performance Computing (HPC) systems, where predicted walltimes directly influence reservation timing, backfilling feasibility, and overall queue dynamics. This raises a fundamental question of whether improved runtime prediction accuracy necessarily translates into improved scheduling performance. In this work, we conduct an empirical study of runtime estimation under EASY Backfilling using an application-driven workload consisting of MRI-based brain segmentation jobs. Despite identical configurations and uniform metadata, runtimes exhibit substantial variability driven by intrinsic input structure. To capture this variability, we develop a feature-driven machine learning (ML) framework that extracts region-wise features from MRI volumes to predict job runtimes without relying on historical execution traces or scheduling metadata. We integrate these ML-derived predictions into an EASY Backfilling scheduler implemented in the Batsim simulation framework. Our results show that regression accuracy alone does not determine scheduling performance. Instead, scheduling performance depends strongly on estimation bias and its effect on reservation timing and runtime exceedances. In particular, mild multiplicative calibration of ML-based runtime estimates stabilizes scheduler behavior and yields consistently competitive performance across workload and system configurations. Comparable performance can also be observed with certain levels of uniform overestimation; however, calibrated ML predictions provide a systematic mechanism to control estimation bias without relying on arbitrary static inflation. In contrast, underestimation consistently leads to severe performance degradation and cascading job terminations. These findings highlight runtime estimation as a structural control input in backfilling-based HPC scheduling and demonstrate the importance of evaluating prediction models jointly with scheduling dynamics rather than through regression metrics alone.

Devin Setiawan

Concept-Driven Interpretability in Graph Neural Networks: Applications in Neuroscientific Connectomics and Clinical Motor Analysis

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Sumaiya Shomaji, Chair
Sankha Guria
Han Wang


Abstract

Graph Neural Networks (GNNs) achieve state-of-the-art performance in modeling complex biological and behavioral systems, yet their "black-box" nature limits their utility for scientific discovery and clinical translation. Standard post-hoc explainability methods typically attribute importance to low-level features, such as individual nodes or edges, which often fail to map onto the high-level, domain-specific concepts utilized by experts. To address this gap, this thesis explores diverse methodological strategies for achieving Concept-Level Interpretability in GNNs, demonstrating how deep learning models can be structurally and analytically aligned with expert domain knowledge. This theme is explored through two distinct methodological paradigms applied to critical challenges in neuroscience and clinical psychology. First, we introduce an interpretable-by-design approach for modeling brain structure-function coupling. By employing an ensemble of GNNs conceptually biased via input graph filtering, the model enforces verifiably disentangled node embeddings. This allows for the quantitative testing of specific structural hypotheses, revealing that a minority of strong anatomical connections disproportionately drives functional connectivity predictions. Second, we present a post-hoc conceptual alignment paradigm for quantifying atypical motor signatures in Autism Spectrum Disorder (ASD). Utilizing a Spatio-Temporal Graph Autoencoder (STGCN-AE) trained on normative skeletal data, we establish an unsupervised anomaly detection system. To provide clinical interpretability, the model's reconstruction error is systematically aligned with a library of human-interpretable kinematic features, such as postural sway and limb jerk. Explanatory meta-modeling via XGBoost and SHAP analysis further translates this abstract loss into a multidimensional clinical signature. Together, these applications demonstrate that integrating concept-level interpretability through either architectural design or systematic post-hoc alignment enables GNNs to serve as robust tools for hypothesis testing and clinical assessment.

Moh Absar Rahman

Permissions vs Promises: Assessing Over-privileged Android Apps via Local LLM-based Description Validation

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Drew Davidson, Chair
Sankha Guria
David Johnson


Abstract

Android is the most widely adopted mobile operating system, supporting billions of devices and driven by a robust app ecosystem.  Its permission-based security model aims to enforce the Principle of Least Privilege (PoLP), restricting apps to only the permissions it needs.  However, many apps still request excessive permissions, increasing the risk of data leakage and malicious exploitation. Previous research on overprivileged permission has become ineffective due to outdated methods and increasing technical complexity.  The introduction of runtime permissions and scoped storage has made some of the traditional analysis techniques obsolete.  Additionally, developers often are not transparent in explaining the usage of app permissions on the Play Store, misleading users unknowingly and unwillingly granting unnecessary permissions. This combination of overprivilege and poor transparency poses significant security threats to Android users.  Recently, the rise of local large language models (LLMs) has shown promise in various security fields. The main focus of this study is to analyze whether an app is overpriviledged based on app description provided on the Play Store using Local LLM. Finally, we conduct a manual evaluation to validate the LLM’s findings, comparing its results against human-verified response.

Brinley Hull

An Interactive Virtual Pet for Autism Spectrum Disorder Stress Onset Detection & Mitigation

When & Where:


Nichols Hall, Room 317 (Richard K. Moore Conference Room)

Degree Type:

MS Thesis Defense

Committee Members:

Arvin Agah, Chair
Perry Alexander
David Johnson
Sumaiya Shomaji

Abstract

Individuals with Autism Spectrum Disorder (ASD) frequently experience elevated stress and are at higher risk for mood disorders such as anxiety and depression. Sensory over-responsivity, social challenges, and difficulties with emotional recognition and regulation contribute to such heightened stress. This study presents a proof-of-concept system that detects and mitigates stress through interactions with a virtual pet. Designed for young adults with high-functioning autism, and potentially useful for people beyond that group, the system monitors simulated heart rate, skin resistance, body temperature, and environmental sound and light levels. Upon detection of stress or potential triggers, the system alerts the user and offers stress-reduction activities via a virtual pet, including guided deep-breathing exercises and interactive engagement with the virtual companion. Through combining real-time stress detection with interactive interventions on a single platform, the system aims to help autistic individuals recognize and manage stress more effectively.

Harun Khan

Identifying Weight Surgery Attacks in Siamese Networks

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Degree Type:

MS Thesis Defense

Committee Members:

Prasad Kulkami, Chair
Alex Bardas
Bo Luo


Abstract

Facial recognition systems increasingly rely on machine learning services, yet they remain vulnerable to cyber-attacks. While traditional adversarial attacks target input data, an underexplored threat comes from weight manipulation attacks, which directly modify model parameters and can compromise deployed systems in cyber-physical settings. This paper investigates defenses against Weight Surgery, a weight manipulation attack that modifies the final linear layer of neural networks to merge or shatter classes without requiring access to training data. We propose a computationally lightweight defense capable of detecting sample pairs affected by Weight Surgery at low false-positive rates. The defense is designed to operate in realistic deployment scenarios, selecting its sensitivity parameter 𝛾 using only benign samples to meet a target false-positive rate. Evaluation on 1000 independently attacked models demonstrates that our method achieves over 95% recall at a target false-positive rate of 0.001. Performance remains strong even under stricter conditions: at FPR = 0.0001, recall is 92.5%, and at 𝛾=0.98, FPR drops to 0.00001 while maintaining 88.9% recall. These results highlight the robustness and practicality of the defense, offering an effective safeguard for neural networks against model-targeted attacks.

Tanvir Hossain

Security Solutions for Zero-Trust Microelectronics Supply Chains

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Degree Type:

PhD Comprehensive Defense

Committee Members:

Tamzidul Hoque, Chair
Drew Davidson
Prasad Kulkarni
Heechul Yun
Huijeong Kim

Abstract

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.

Krishna Chaitanya Reddy Chitta

A Dynamic Resource Management Framework and Reconfiguration Strategies for Cloud-native Bulk Synchronous Parallel Applications

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Hongyang Sun, Chair
David Johnson
Sumaiya Shomaji


Abstract

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.

Smriti Pranjal

NoBIAS: Non-coding RNA Base Interaction Annotation using Visual Snapshot

When & Where:


Slawson Hall, Rm 198

Degree Type:

PhD Comprehensive Defense

Committee Members:

Cuncong Zhong, Chair
Sumaiya Shomaji
Hongyang Sun
Zijun Yao
Xiaoqing Wu

Abstract

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.

Past Defense Notices

Dates

Mohammad Ful Hossain Seikh

AAFIYA: Antenna Analysis in Frequency-domain for Impedance and Yield Assessment

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

Jim Stiles, Chair
Rachel Jarvis
Alessandro Salandrino


Abstract

This project presents AAFIYA (Antenna Analysis in Frequency-domain for Impedance and Yield Assessment), a modular Python toolkit developed to automate and streamline the characterization and analysis of radiofrequency (RF) antennas using both measurement and simulation data. Motivated by the need for reproducible, flexible, and publication-ready workflows in modern antenna research, AAFIYA provides comprehensive support for all major antenna metrics, including S-parameters, impedance, gain and beam patterns, polarization purity, and calibration-based yield estimation. The toolkit features robust data ingestion from standard formats (such as Touchstone files and beam pattern text files), vectorized computation of RF metrics, and high-quality plotting utilities suitable for scientific publication.

Validation was carried out using measurements from industry-standard electromagnetic anechoic chamber setups involving both Log Periodic Dipole Array (LPDA) reference antennas and Askaryan Radio Array (ARA) Bottom Vertically Polarized (BVPol) antennas, covering a frequency range of 50–1500 MHz. Key performance metrics, such as broadband impedance matching, S11 and S21 related calculations, 3D realized gain patterns, vector effective lengths,  and cross-polarization ratio, were extracted and compared against full-wave electromagnetic simulations (using HFSS and WIPL-D). The results demonstrate close agreement between measurement and simulation, confirming the reliability of the workflow and calibration methodology.

AAFIYA’s open-source, extensible design enables rapid adaptation to new experiments and provides a foundation for future integration with machine learning and evolutionary optimization algorithms. This work not only delivers a validated toolkit for antenna research and pedagogy but also sets the stage for next-generation approaches in automated antenna design, optimization, and performance analysis.

Soumya Baddham

Battling Toxicity: A Comparative Analysis of Machine Learning Models for Content Moderation

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad Kulkarni
Hongyang Sun


Abstract

With the exponential growth of user-generated content, online platforms face unprecedented challenges in moderating toxic and harmful comments. Due to this, Automated content moderation has emerged as a critical application of machine learning, enabling platforms to ensure user safety and maintain community standards. Despite its importance, challenges such as severe class imbalance, contextual ambiguity, and the diverse nature of toxic language often compromise moderation accuracy, leading to biased classification performance.

This project presents a comparative analysis of machine learning approaches for a Multi-Label Toxic Comment Classification System using the Toxic Comment Classification dataset from Kaggle.  The study examines the performance of traditional algorithms, such as Logistic Regression, Random Forest, and XGBoost, alongside deep architectures, including Bi-LSTM, CNN-Bi-LSTM, and DistilBERT. The proposed approach utilizes word-level embeddings across all models and examines the effects of architectural enhancements, hyperparameter optimization, and advanced training strategies on model robustness and predictive accuracy.

The study emphasizes the significance of loss function optimization and threshold adjustment strategies in improving the detection of minority classes. The comparative results reveal distinct performance trade-offs across model architectures, with transformer models achieving superior contextual understanding at the cost of computational complexity. At the same time, deep learning approaches(LSTM models) offer efficiency advantages. These findings establish evidence-based guidelines for model selection in real-world content moderation systems, striking a balance between accuracy requirements and operational constraints.

Manu Chaudhary

Utilizing Quantum Computing for Solving Multidimensional Partial Differential Equations

When & Where:


Eaton Hall, Room 2001B

Degree Type:

PhD Dissertation Defense

Committee Members:

Esam El-Araby, Chair
Perry Alexander
Tamzidul Hoque
Prasad Kulkarni
Tyrone Duncan

Abstract

Quantum computing has the potential to revolutionize computational problem-solving by leveraging the quantum mechanical phenomena of superposition and entanglement, which allows for processing a large amount of information simultaneously. This capability is significant in the numerical solution of complex and/or multidimensional partial differential equations (PDEs), which are fundamental to modeling various physical phenomena. There are currently many quantum techniques available for solving partial differential equations (PDEs), which are mainly based on variational quantum circuits. However, the existing quantum PDE solvers, particularly those based on variational quantum eigensolver (VQE) techniques, suffer from several limitations. These include low accuracy, high execution times, and low scalability on quantum simulators as well as on noisy intermediate-scale quantum (NISQ) devices, especially for multidimensional PDEs.

In this work, we propose an efficient and scalable algorithm for solving multidimensional PDEs. We present two variants of our algorithm: the first leverages finite-difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, while the second employs FDM, C2Q, and column-by-column decomposition (CCD). Both variants are designed to enhance accuracy and scalability while reducing execution times. We have validated and evaluated our proposed concepts using a number of case studies including multidimensional Poisson equation, multidimensional heat equation, Black Scholes equation, and Navier-Stokes equation for computational fluid dynamics (CFD) achieving promising results. Our results demonstrate higher accuracy, higher scalability, and faster execution times compared to VQE-based solvers on noise-free and noisy quantum simulators from IBM. Additionally, we validated our approach on hardware emulators and actual quantum hardware, employing noise mitigation techniques. This work establishes a practical and effective approach for solving PDEs using quantum computing for engineering and scientific applications.

Alex Manley

Taming Complexity in Computer Architecture through Modern AI-Assisted Design and Education

When & Where:


Nichols Hall, Room 250 (Gemini Room)

Degree Type:

MS Thesis Defense

Committee Members:

Heechul Yun, Chair
Tamzidul Hoque
Mohammad Alian
Prasad Kulkarni

Abstract

The escalating complexity inherent in modern computer architecture presents significant challenges for both professional hardware designers and students striving to gain foundational understanding. Historically, the steady improvement of computer systems was driven by transistor scaling, predictable performance increases, and relatively straightforward architectural paradigms. However, with the end of traditional scaling laws and the rise of heterogeneous and parallel architectures, designers now face unprecedented intricacies involving power management, thermal constraints, security considerations, and sophisticated software interactions. Prior tools and methodologies, often reliant on complex, command-line driven simulations, exacerbate these challenges by introducing steep learning curves, creating a critical need for more intuitive, accessible, and efficient solutions. To address these challenges, this thesis introduces two innovative, modern tools. 

The first tool, SimScholar, provides an intuitive graphical user interface (GUI) built upon the widely-used gem5 simulator. SimScholar significantly simplifies the simulation process, enabling students and educators to more effectively engage with architectural concepts through a visually guided environment, both reducing complexity and enhancing conceptual understanding. Supporting SimScholar, the gem5 Extended Modules API (gEMA) offers streamlined backend integration with gem5, ensuring efficient communication, modularity, and maintainability.

The second contribution, gem5 Co-Pilot, delivers an advanced framework for architectural design space exploration (DSE). Co-Pilot integrates cycle-accurate simulation via gem5, detailed power and area modeling through McPAT, and intelligent optimization assisted by a large language model (LLM). Central to Co-Pilot is the Design Space Declarative Language (DSDL), a Python-based domain-specific language that facilitates structured, clear specification of design parameters and constraints.

Collectively, these tools constitute a comprehensive approach to taming complexity in computer architecture, offering powerful, user-friendly solutions tailored to both educational and professional settings.

Prashanthi Mallojula

On the Security of Mobile and Auto Companion Apps

When & Where:


Eaton Hall, Room 2001B

Degree Type:

PhD Dissertation Defense

Committee Members:

Bo Luo, Chair
Alex Bardas
Fengjun Li
Hongyang Sun
Huazhen Fang

Abstract

The rapid development of mobile apps on modern smartphone platforms has raised critical concerns regarding user data privacy and the security of app-to-device communications, particularly with companion apps that interface with external IoT or cyber-physical systems (CPS). In this dissertation, we investigate two major aspects of mobile app security: the misuse of permission mechanisms and the security of app to device communication in automotive companion apps.

 

Mobile apps seek user consent for accessing sensitive information such as location and personal data. However, users often blindly accept these permission requests, allowing apps to abuse this mechanism. As long as a permission is requested, state-of-the-art security mechanisms typically treat it as legitimate. This raises a critical question: Are these permission requests always valid? To explore this, we validate permission requests using statistical analysis on permission sets extracted from groups of functionally similar apps. We identify mobile apps with abusive permission access and quantify the risk of information leakage posed by each app. Through a large-scale statistical analysis of permission sets from over 200,000 Android apps, our findings reveal that approximately 10% of the apps exhibit highly risky permission usage. 

 

Next, we present a comprehensive study of automotive companion apps, a rapidly growing yet underexplored category of mobile apps. These apps are used for vehicle diagnostics, telemetry, and remote control, and they often interface with in-vehicle networks via OBD-II dongles, exposing users to significant privacy and security risks. Using a hybrid methodology that combines static code analysis, dynamic runtime inspection, and network traffic monitoring, we analyze 154 publicly available Android automotive apps. Our findings uncover a broad range of critical vulnerabilities. Over 74% of the analyzed apps exhibit vulnerabilities that could lead to private information leakage, property theft, or even real-time safety risks while driving. Specifically, 18 apps were found to connect to open OBD-II dongles without requiring any authentication, accept arbitrary CAN bus commands from potentially malicious users, and transmit those commands to the vehicle without validation. 16 apps were found to store driving logs in external storage, enabling attackers to reconstruct trip histories and driving patterns. We demonstrate several real-world attack scenarios that illustrate how insecure data storage and communication practices can compromise user privacy and vehicular safety. Finally, we discuss mitigation strategies and detail the responsible disclosure process undertaken with the affected developers.

Syed Abid Sahdman

Soliton Generation and Pulse Optimization using Nonlinear Transmission Lines

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Alessandro Salandrino, Chair
Shima Fardad
Morteza Hashemi


Abstract

Nonlinear Transmission Lines (NLTLs) have gained significant interest due to their ability to generate ultra-short, high-power RF pulses, which are valuable in applications such as ultrawideband radar, space vehicles, and battlefield communication disruption. The waveforms generated by NLTLs offer frequency diversity not typically observed in High-Power Microwave (HPM) sources based on electron beams. Nonlinearity in lumped element transmission lines is usually introduced using voltage-dependent capacitors due to their simplicity and widespread availability. The periodic structure of these lines introduces dispersion, which broadens pulses. In contrast, nonlinearity causes higher-amplitude regions to propagate faster. The interaction of these effects results in the formation of stable, self-localized waveforms known as solitons.

Soliton propagation in NLTLs can be described by the Korteweg-de Vries (KdV) equation. In this thesis, the Bäcklund Transformation (BT) method has been used to derive both single and two-soliton solutions of the KdV equation. This method links two different partial differential equations (PDEs) and their solutions to produce solutions for nonlinear PDEs. The two-soliton solution is obtained from the single soliton solution using a nonlinear superposition principle known as Bianchi’s Permutability Theorem (BPT). Although the KdV model is suitable for NLTLs where the capacitance-voltage relationship follows that of a reverse-biased p-n junction, it cannot generally represent arbitrary nonlinear capacitance characteristics.

To address this limitation, a Finite Difference Time Domain (FDTD) method has been developed to numerically solve the NLTL equation for soliton propagation. To demonstrate the pulse sharpening and RF generation capability of a varactor-loaded NLTL, a 12-section lumped element circuit has been designed and simulated using LTspice and verified with the calculated result. In airborne radar systems, operational constraints such as range, accuracy, data rate, environment, and target type require flexible waveform design, including variation in pulse widths and pulse repetition frequencies. A gradient descent optimization technique has been employed to generate pulses with varying amplitudes and frequencies by optimizing the NLTL parameters. This work provides a theoretical analysis and numerical simulation to study soliton propagation in NLTLs and demonstrates the generation of tunable RF pulses through optimized circuit design.

Vinay Kumar Reddy Budideti

NutriBot: An AI-Powered Personalized Nutrition Recommendation Chatbot Using Rasa

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Victor Frost
Prasad Kulkarni


Abstract

In recent years, the intersection of Artificial Intelligence and healthcare has paved the way for intelligent dietary assistance. NutriBot is an AI-powered chatbot developed using the Rasa framework to deliver personalized nutrition recommendations based on user preferences, diet types, and nutritional goals. This full-stack system integrates Rasa NLU, a Flask backend, the Nutritionix API for real-time food data, and a React.js + Tailwind CSS frontend for seamless interaction. The system is containerized using Docker and deployable on cloud platforms like GCP. 

The chatbot supports multi-turn conversations, slot-filling, and remembers user preferences such as dietary restrictions or nutrient focus (e.g., high protein). Evaluation of the system showed perfect intent and entity recognition accuracy, fast API response times, and user-friendly fallback handling. While NutriBot currently lacks persistent user profiles and multilingual support, it offers a highly accurate, scalable framework for future extensions such as fitness tracker integration, multilingual capabilities, and smart assistant deployment.

Arun Kumar Punjala

Deep Learning-Based MRI Brain Tumor Classification: Evaluating Sequential Architectures for Diagnostic Accuracy

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad Kulkarni
Dongjie Wang


Abstract

Accurate classification of brain tumors from MRI scans plays a vital role in assisting clinical diagnosis and treatment planning. This project investigates and compares three deep learning-based classification approaches designed to evaluate the effectiveness of integrating recurrent layers into conventional convolutional architectures. Specifically, a CNN-LSTM model, a CNN-RNN model with GRU units, and a baseline CNN classifier using EfficientNetB0 are developed and assessed on a curated MRI dataset.

The CNN-LSTM model uses ResNet50 as a feature extractor, with spatial features reshaped and passed through stacked LSTM layers to explore sequential learning on static medical images. The CNN-RNN model implements TimeDistributed convolutional layers followed by GRUs, examining the potential benefits of GRU-based modeling. The EfficientNetB0-based CNN model, trained end-to-end without recurrent components, serves as the performance baseline.

All three models are evaluated using training accuracy, validation loss, confusion matrices, and class-wise performance metrics. Results show that the CNN-LSTM architecture provides the most balanced performance across tumor types, while the CNN-RNN model suffers from mild overfitting. The EfficientNetB0 baseline offers stable and efficient classification for general benchmarking.

Ganesh Nurukurti

Customer Behavior Analytics and Recommendation System for E-Commerce

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad Kulkarni
Han Wang


Abstract

In the era of digital commerce, personalized recommendations are pivotal for enhancing user experience and boosting engagement. This project presents a comprehensive recommendation system integrated into an e-commerce web application, designed using Flask and powered by collaborative filtering via Singular Value Decomposition (SVD). The system intelligently predicts and personalizes product suggestions for users based on implicit feedback such as purchases, cart additions, and search behavior.

 

The foundation of the recommendation engine is built on user-item interaction data, derived from the Brazilian e-commerce Olist dataset. Ratings are simulated using weighted scores for purchases and cart additions, reflecting varying degrees of user intent. These interactions are transformed into a user-product matrix and decomposed using SVD, yielding latent user and product features. The model leverages these latent factors to predict user interest in unseen products, enabling precise and scalable recommendation generation.

 

To further enhance personalization, the system incorporates real-time user activity. Recent search history is stored in an SQLite database and used to prioritize recommendations that align with the user’s current interests. A diversity constraint is also applied to avoid redundancy, limiting the number of recommended products per category.

 

The web application supports robust user authentication, product exploration by category, cart management, and checkout simulations. It features a visually driven interface with dynamic visualizations for product insights and user interactions. The home page adapts to individual preferences, showing tailored product recommendations and enabling users to explore categories and details.

 

In summary, this project demonstrates the practical implementation of a hybrid recommendation strategy combining matrix factorization with contextual user behavior. It showcases the importance of latent factor modeling, data preprocessing, and user-centric design in delivering an intelligent retail experience.

Masoud Ghazikor

Distributed Optimization and Control Algorithms for UAV Networks in Unlicensed Spectrum Bands

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Degree Type:

MS Thesis Defense

Committee Members:

Morteza Hashemi, Chair
Victor Frost
Prasad Kulkarni


Abstract

UAVs have emerged as a transformative technology for various applications, including emergency services, delivery, and video streaming. Among these, video streaming services in areas with limited physical infrastructure, such as disaster-affected areas, play a crucial role in public safety. UAVs can be rapidly deployed in search and rescue operations to efficiently cover large areas and provide live video feeds, enabling quick decision-making and resource allocation strategies. However, ensuring reliable and robust UAV communication in such scenarios is challenging, particularly in unlicensed spectrum bands, where interference from other nodes is a significant concern. To address this issue, developing a distributed transmission control and video streaming is essential to maintaining a high quality of service, especially for UAV networks that rely on delay-sensitive data. 

In this MSc thesis, we study the problem of distributed transmission control and video streaming optimization for UAVs operating in unlicensed spectrum bands. We develop a cross-layer framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground-aerial nodes at the physical layer, (ii) limited-size queues with delay-constrained packet arrival at the MAC layer, and (iii) video encoding rate at the application layer. This framework is designed to optimize the average throughput and PSNR by adjusting fading thresholds and video encoding rates for an integrated aerial-ground network in unlicensed spectrum bands. Using consensus-based distributed algorithm and coordinate descent optimization, we develop two algorithms: (i) Distributed Transmission Control (DTC) that dynamically adjusts fading thresholds to maximize the average throughput by mitigating trade-offs between low-SINR transmission errors and queue packet losses, and (ii) Joint Distributed Video Transmission and Encoder Control (JDVT-EC) that optimally balances packet loss probabilities and video distortions by jointly adjusting fading thresholds and video encoding rates. Through extensive numerical analysis, we demonstrate the efficacy of the proposed algorithms under various scenarios.