Defense Notices


All students and faculty are welcome to attend the final defense of EECS 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

Elizabeth Wyss

A New Frontier for Software Security: Diving Deep into npm

When & Where:


Eaton Hall, Room 2001B

Committee Members:

Drew Davidson, Chair
Alex Bardas
Fengjun Li
Bo Luo
J. Walker

Abstract

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. 


Alfred Fontes

Optimization and Trade-Space Analysis of Pulsed Radar-Communication Waveforms using Constant Envelope Modulations

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Patrick McCormick, Chair
Shannon Blunt
Jonathan Owen


Abstract

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.


Arin Dutta

Performance Analysis of Distributed Raman Amplification with Different Pumping Configurations

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Rongqing Hui, Chair
Morteza Hashemi
Rachel Jarvis
Alessandro Salandrino
Hui Zhao

Abstract

As internet services like high-definition videos, cloud computing, and artificial intelligence keep growing, optical networks need to keep up with the demand for more capacity. Optical amplifiers play a crucial role in offsetting fiber loss and enabling long-distance wavelength division multiplexing (WDM) transmission in high-capacity systems. Various methods have been proposed to enhance the capacity and reach of fiber communication systems, including advanced modulation formats, dense wavelength division multiplexing (DWDM) over ultra-wide bands, space-division multiplexing, and high-performance digital signal processing (DSP) technologies. To maintain higher data rates along with maximizing the spectral efficiency of multi-level modulated signals, a higher Optical Signal-to-Noise Ratio (OSNR) is necessary. Despite advancements in coherent optical communication systems, the spectral efficiency of multi-level modulated signals is ultimately constrained by fiber nonlinearity. Raman amplification is an attractive solution for wide-band amplification with low noise figures in multi-band systems.

Distributed Raman Amplification (DRA) have been deployed in recent high-capacity transmission experiments to achieve a relatively flat signal power distribution along the optical path and offers the unique advantage of using conventional low-loss silica fibers as the gain medium, effectively transforming passive optical fibers into active or amplifying waveguides. Also, DRA provides gain at any wavelength by selecting the appropriate pump wavelength, enabling operation in signal bands outside the Erbium doped fiber amplifier (EDFA) bands. Forward (FW) Raman pumping configuration in DRA can be adopted to further improve the DRA performance as it is more efficient in OSNR improvement because the optical noise is generated near the beginning of the fiber span and attenuated along the fiber. Dual-order FW pumping scheme helps to reduce the non-linear effect of the optical signal and improves OSNR by more uniformly distributing the Raman gain along the transmission span.

The major concern with Forward Distributed Raman Amplification (FW DRA) is the fluctuation in pump power, known as relative intensity noise (RIN), which transfers from the pump laser to both the intensity and phase of the transmitted optical signal as they propagate in the same direction. Additionally, another concern of FW DRA is the rise in signal optical power near the start of the fiber span, leading to an increase in the non-linear phase shift of the signal. These factors, including RIN transfer-induced noise and non-linear noise, contribute to the degradation of system performance in FW DRA systems at the receiver.

As the performance of DRA with backward pumping is well understood with relatively low impact of RIN transfer, our research  is focused on the FW pumping configuration, and is intended to provide a comprehensive analysis on the system performance impact of dual order FW Raman pumping, including signal intensity and phase noise induced by the RINs of both 1st and the 2nd order pump lasers, as well as the impacts of linear and nonlinear noise. The efficiencies of pump RIN to signal intensity and phase noise transfer are theoretically analyzed and experimentally verified by applying a shallow intensity modulation to the pump laser to mimic the RIN. The results indicate that the efficiency of the 2nd order pump RIN to signal phase noise transfer can be more than 2 orders of magnitude higher than that from the 1st order pump. Then the performance of the dual order FW Raman configurations is compared with that of single order Raman pumping to understand trade-offs of system parameters. The nonlinear interference (NLI) noise is analyzed to study the overall OSNR improvement when employing a 2nd order Raman pump. Finally, a DWDM system with 16-QAM modulation is used as an example to investigate the benefit of DRA with dual order Raman pumping and with different pump RIN levels. We also consider a DRA system using a 1st order incoherent pump together with a 2nd order coherent pump. Although dual order FW pumping corresponds to a slight increase of linear amplified spontaneous emission (ASE) compared to using only a 1st order pump, its major advantage comes from the reduction of nonlinear interference noise in a DWDM system. Because the RIN of the 2nd order pump has much higher impact than that of the 1st order pump, there should be more stringent requirement on the RIN of the 2nd order pump laser when dual order FW pumping scheme is used for DRA for efficient fiber-optic communication. Also, the result of system performance analysis reveals that higher baud rate systems, like those operating at 100Gbaud, are less affected by pump laser RIN due to the low-pass characteristics of the transfer of pump RIN to signal phase noise.


Audrey Mockenhaupt

Using Dual Function Radar Communication Waveforms for Synthetic Aperture Radar Automatic Target Recognition

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Patrick McCormick, Chair
Shannon Blunt
Jon Owen


Abstract

Pending.


Rich Simeon

Delay-Doppler Channel Estimation for High-Speed Aeronautical Mobile Telemetry Applications

When & Where:


Eaton Hall, Room 2001B

Committee Members:

Erik Perrins, Chair
Shannon Blunt
Morteza Hashemi
Jim Stiles
Craig McLaughlin

Abstract

The next generation of digital communications systems aims to operate in high-Doppler environments such as high-speed trains and non-terrestrial networks that utilize satellites in low-Earth orbit. Current generation systems use Orthogonal Frequency Division Multiplexing modulation which is known to suffer from inter-carrier interference (ICI) when different channel paths have dissimilar Doppler shifts.

A new Orthogonal Time Frequency Space (OTFS) modulation (also known as Delay-Doppler modulation) is proposed as a candidate modulation for 6G networks that is resilient to ICI. To date, OTFS demodulation designs have focused on the use cases of popular urban terrestrial channel models where path delay spread is a fraction of the OTFS symbol duration. However, wireless wide-area networks that operate in the aeronautical mobile telemetry (AMT) space can have large path delay spreads due to reflections from distant geographic features. This presents problems for existing channel estimation techniques which assume a small maximum expected channel delay, since data transmission is paused to sound the channel by an amount equal to twice the maximum channel delay. The dropout in data contributes to a reduction in spectral efficiency.

Our research addresses OTFS limitations in the AMT use case. We start with an exemplary OTFS framework with parameters optimized for AMT. Following system design, we focus on two distinct areas to improve OTFS performance in the AMT environment. First we propose a new channel estimation technique using a pilot signal superimposed over data that can measure large delay spread channels with no penalty in spectral efficiency. A successive interference cancellation algorithm is used to iteratively improve channel estimates and jointly decode data. A second aspect of our research aims to equalize in delay-Doppler space. In the delay-Doppler paradigm, the rapid channel variations seen in the time-frequency domain is transformed into a sparse quasi-stationary channel in the delay-Doppler domain. We propose to use machine learning using Gaussian Process Regression to take advantage of the sparse and stationary channel and learn the channel parameters to compensate for the effects of fractional Doppler in which simpler channel estimation techniques cannot mitigate. Both areas of research can advance the robustness of OTFS across all communications systems.


Mohammad Ful Hossain Seikh

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

When & Where:


Eaton Hall, Room 2001B

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

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

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.


Past Defense Notices

Dates

ANDREW FARMER

Mechanizing Worker/Wrapper for Domain Specific Optimizations

When & Where:


250 Nichols Hall

Committee Members:

Andy Gill, Chair
Perry Alexander
Prasad Kulkarni
James Miller
Chris Depcik

Abstract

A hallmark of functional programming languages is their support for the algebraic manipulation of programs. While such techniques lend themselves to mechanization, they are usually employed by hand, requiring considerable expertise, but offering a high assurance of the correctness of the result. Library writers are especially likely to use these techniques in order to implement optimizations which are either deemed too narrowly applicable to be worth implementing in a general language compiler, or may only represent an optimization in certain specific contexts. Such domain-specific optimizations can be incredibly effective however, offering complexity-changing improvement. In many instances, they are accomplished by changing the data structures over which the computation operates. 

We propose a framework for mechanizing the worker/wrapper transformation, which can express a large number of these domain-specific optimizations that depend on changing data structures. As a significant case study, we propose to recast the Stream Fusion transformation as an instance of worker/wrapper, using it to motivate the capabilities of our framework. We also propose extensions to Stream Fusion to allow it to optimize higher-order stream combinators. The resulting framework should lower the effort required to mechanize such transformations, leading to increased correctness and reuse. Additionally, the extended Stream Fusion transformation will offer practical performance improvements to a wide range of programs.


BRIEN SMITH-MARTINEZ

A Genetic Algorithm for Generating Radar Transmit Codes

When & Where:


2001B Eaton Hall

Committee Members:

Arvin Agah, Chair
Swapan Chakrabarti
James Stiles


Abstract

This work presents the design and development of a genetic algorithm to generate long range transmit codes with low autocorrelation side lobes for radar. The genetic algorithm described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a target profile.


PHILIP MEIN

A Latency-Determining/User Directed Firefox Browser Extension

When & Where:


246 Nichols Hall

Committee Members:

James Sterbenz, Chair
Bo Luo
Gary Minden


Abstract

As the World Wide Web continues to evolve as the preferred choice for information access it is critical that its utility to the user remains. Latency as a result of network congestion, bandwidth availability, server processing delays, embedded objects, and transmission delays and errors can impact the utility of the web browser application. To improve the overall user experience the application needs to not only provide feedback to the end user about the latency of links that are available but to also provide them decision controls in the retrieval of the web content. This thesis presents a background and related work relating to latency and web optimization techniques to reduce this latency and then introduce an improvement to the "latency aware" Mozilla Firefox extension which was originally developed by Sterbenz et. al., in 2002. This these describes the architecture and prototype implementation, followed with an analysis of its effectiveness to predict latency and future work.


BRIAN CORDILL

Radar System Enhancement through High Fidelity Electromagnetic Modeling

When & Where:


317 Nichols Hall

Committee Members:

Sarah Seguin, Chair
Shannon Blunt
Christopher Allen
James Stiles
Mark Ewing

Abstract

Radar systems can fulfill a wide range of remote sensing missions, from aircraft tracking, to rainfall rate measurements, to automobile cruise control assist. The history of radar is full of innovation and a drive to "get the most" out of a system. History, however, is also full of simplifying assumptions made along the way to make certain problems tractable. In some sense, engineers have historically traded performance for tractability. Antenna arrays and their accompanying electromagnetics is one such area that is ripe to have its simplifying assumptions pealed back in an effort to extract even more performance. This can be demonstrated by developing high fidelity electromagnetic (EM) models of a radar array and showing that existing array processing algorithms based on simplified models are operating at a degraded performance level. These high fidelity EM models can then serve as a basis to enhance the performance of these algorithms. This work will specifically examine integrating a blind calibration process into the Re-Iterative Super Resolution (RISR) Direction of Arrival (DoA) algorithm to improve RISRs performance, show that the misapplication of the reciprocity theorem is holding back radar array processing algorithms, and investigate the possibility of leveraging recent results showing non-linearities in off-boresight emissions to improve clutter mitigation.


BALASUBRAMANIAM SRIDHAR

Precise Computation Control using Discovered Computation Structure and Behaviour

When & Where:


250 Nichols Hall

Committee Members:

Prasad Kulkarni, Chair
Arvin Agah
Perry Alexander


Abstract

Many applications use a variety of components both at the middleware and OS as part of their execution and systems are designed to provide these applications with high availability and specific qualities of service intended for specific application semantics. To provide these features, systems must be developed that exercise precise control over computation 
behaviour. This thesis proposes a mechanism where the structure of the computation and its behaviour can be discovered by tracing the system calls that an application makes during the period of its execution in real time using Data Streams and Computation Component Set Manager frameworks. This thesis also demonstrates the integration of computation behaviour information with Hierarchical Group Scheduling framework to achieve precise control over the execution of the application through arbitrary scheduling semantics.


MARYAM MAHANI

Strategic Structural Reorganization in Multi-agent Systems Inspired by Social Organization Theory

When & Where:


129 Nichols

Committee Members:

Arvin Agah, Chair
Swapan Chakrabarti
Man Kong
Brian Potetz
Prajna Dhar

Abstract


HONGLIANG FEI

Learning from Structured Data

When & Where:


246 Nichols Hall

Committee Members:

Luke Huan, Chair
Arvin Agah
Bo Luo
Brian Potetz
Hongguo Xu

Abstract


MEGAN PECK

Composition Semantics of the Rosetta Specification Language

When & Where:


250 Nichols Hall

Committee Members:

Perry Alexander, Chair
Andy Gill
Prasad Kulkarni


Abstract


KRITI CHAKDAR

Cancer Detection for Low Grade Squamous Intraepithelial Lesion

When & Where:


2001B Eaton Hall

Committee Members:

Brian Potetz, Chair
Arvin Agah
Luke Huan


Abstract


NICOLAS FRISBY

Reducing the Cost of Precise Types

When & Where:


250 Nichols Hall

Committee Members:

Perry Alexander, Chair
Andy Gill
Prasad Kulkarni
Bo Luo
Sara Wilson

Abstract