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 npmWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Drew Davidson, ChairAlex 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 ModulationsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Patrick McCormick, ChairShannon 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 ConfigurationsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Rongqing Hui, ChairMorteza 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 RecognitionWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Patrick McCormick, ChairShannon Blunt
Jon Owen
Abstract
Pending.
Rich Simeon
Delay-Doppler Channel Estimation for High-Speed Aeronautical Mobile Telemetry ApplicationsWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Erik Perrins, ChairShannon 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 AssessmentWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Jim Stiles, ChairRachel 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 ModerationWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
David Johnson, ChairPrasad 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.
Past Defense Notices
Tejarsha Arigila
Benchmarking Aggregation Free Federated Learning using Data Condensation: Comparison with Federated AveragingWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Fengjun Li, ChairBo Luo
Sumaiya Shomaji
Abstract
This project investigates the performance of Federated Learning Aggregation-Free (FedAF) compared to traditional federated learning methods under non-independent and identically distributed (non-IID) data conditions, characterized by Dirichlet distribution parameters (alpha = 0.02, 0.05, 0.1). Utilizing the MNIST and CIFAR-10 datasets, the study benchmarks FedAF against Federated Averaging (FedAVG) in terms of accuracy, convergence speed, communication efficiency, and robustness to label and feature skews.
Traditional federated learning approaches like FedAVG aggregate locally trained models at a central server to form a global model. However, these methods often encounter challenges such as client drift in heterogeneous data environments, which can adversely affect model accuracy and convergence rates. FedAF introduces an innovative aggregation-free strategy wherein clients collaboratively generate a compact set of condensed synthetic data. This data, augmented by soft labels from the clients, is transmitted to the server, which then uses it to train the global model. This approach effectively reduces client drift and enhances resilience to data heterogeneity. Additionally, by compressing the representation of real data into condensed synthetic data, FedAF improves privacy by minimizing the transfer of raw data.
The experimental results indicate that while FedAF converges faster, it struggles to stabilize under highly heterogenous environments due to limited real data representation capacity of condensed synthetic data.
Mohammed Misbah Zarrar
Efficient End-to-End Deep Learning for Autonomous Racing: TinyLidarNet and Low-Power Computing PlatformsWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Heechul Yun, ChairPrasad Kulkarni
Bo Luo
Abstract
End-to-end deep learning has proven effective for robotic navigation by deriving control signals directly from raw sensory data. However, the majority of existing end-to-end navigation solutions are predominantly camera-based.
We propose TinyLidarNet, a lightweight 2D LiDAR-based end-to-end deep learning model for autonomous racing. We systematically analyze its performance on untrained tracks and computing requirements for real-time processing. We find that TinyLidarNet's 1D Convolutional Neural Network (CNN) based architecture significantly outperforms widely used Multi-Layer Perceptron (MLP) based architecture. In addition, we show that it can be processed in real-time on low-end micro-controller units (MCUs).
We deployed TinyLidarNet on an MCU-based F1TENTH platform, which is comprised of an ESP32-S3 MCU and a RPLiDAR sensor and demonstrated the feasibility of using MCUs in F1TENTH autonomous racing.
Finally, we compare TinyLidarNet with ForzaETH, a state-of-the-art Model Predictive Controller (MPC) based F1TENTH racing stack. Our results show that TinyLidarNet is able to closely match the performance of ForzaETH by training the model using the data generated by ForzaETH
Ye Wang
Deceptive Signals: Unveiling and Countering Sensor Spoofing Attacks on Cyber SystemsWhen & Where:
Nichols Hall, Room 250 (Gemini Room)
Committee Members:
Fengjun Li, ChairDrew Davidson
Rongqing Hui
Bo Luo
Haiyang Chao
Abstract
In modern computer systems, sensors play a critical role in enabling a wide range of functionalities, from navigation in autonomous vehicles to environmental monitoring in smart homes. Acting as an interface between physical and digital worlds, sensors collect data to drive automated functionalities and decision-making. However, this reliance on sensor data introduces significant potential vulnerabilities, leading to various physical, sensor-enabled attacks such as spoofing, tampering, and signal injection. Sensor spoofing attacks, where adversaries manipulate sensor input or inject false data into target systems, pose serious risks to system security and privacy.
In this work, we have developed two novel sensor spoofing attack methods that significantly enhance both efficacy and practicality. The first method employs physical signals that are imperceptible to humans but detectable by sensors. Specifically, we target deep learning based facial recognition systems using infrared lasers. By leveraging advanced laser modeling, simulation-guided targeting, and real-time physical adjustments, our infrared laser-based physical adversarial attack achieves high success rates with practical real-time guarantees, surpassing the limitations of prior physical perturbation attacks. The second method embeds physical signals, which are inherently present in the system, into legitimate patterns. In particular, we integrate trigger signals into standard operational patterns of actuators on mobile devices to construct remote logic bombs, which are shown to be able to evade all existing detection mechanisms. Achieving a zero false-trigger rate with high success rates, this novel sensor bomb is highly effective and stealthy.
Our study on emerging sensor-based threats highlights the urgent need for comprehensive defenses against sensor spoofing. Along this direction, we design and investigate two defense strategies to mitigate these threats. The first strategy involves filtering out physical signals identified as potential attack vectors. The second strategy is to leverage beneficial physical signals to obfuscate malicious patterns and reinforce data integrity. For example, side channels targeting the same sensor can be used to introduce cover signals that prevent information leakage, while environment-based physical signals serve as signatures to authenticate data. Together, these strategies form a comprehensive defense framework that filters harmful sensor signals and utilizes beneficial ones, significantly enhancing the overall security of cyber systems.
SM Ishraq-Ul Islam
Quantum Circuit Synthesis using Genetic Algorithms Combined with Fuzzy LogicWhen & Where:
LEEP2, Room 1420
Committee Members:
Esam El-Araby, ChairTamzidul Hoque
Prasad Kulkarni
Abstract
Quantum computing emerges as a promising direction for high-performance computing in the post-Moore era. Leveraging quantum mechanical properties, quantum devices can theoretically provide significant speedup over classical computers in certain problem domains. Quantum algorithms are typically expressed as quantum circuits composed of quantum gates, or as unitary matrices. Execution of quantum algorithms on physical devices requires translation to machine-compatible circuits -- a process referred to as quantum compilation or synthesis.
Quantum synthesis is a challenging problem. Physical quantum devices support a limited number of native basis gates, requiring synthesized circuits to be composed of only these gates. Moreover, quantum devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits on the device.
Current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints, through their gate errors and high susceptibility to noise. NISQ devices are vulnerable to errors during gate application and their short decoherence times leads to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to have a low number of gates to reduce gate errors, and short execution times to avoid qubit decoherence.
The problem of synthesizing device-compatible quantum circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based compilation techniques, which are able to produce approximations of the desired algorithm to a required degree of precision. In this work, we investigate using Genetic Algorithms (GAs) -- a proven gradient-free optimization technique based on natural selection -- for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality of our proposed quantum synthesis method.
Sravan Reddy Chintareddy
Combating Spectrum Crunch with Efficient Machine-Learning Based Spectrum Access and Harnessing High-frequency Bands for Next-G Wireless NetworksWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Morteza Hashemi, ChairVictor Frost
Erik Perrins
Dongjie Wang
Shawn Keshmiri
Abstract
There is an increasing trend in the number of wireless devices that is now already over 14 billion and is expected to grow to 40 billion devices by 2030. In addition, we are witnessing an unprecedented proliferation of applications and technologies with wireless connectivity requirements such as unmanned aerial vehicles, connected health, and radars for autonomous vehicles. The advent of new wireless technologies and devices will only worsen the current spectrum crunch that service providers and wireless operators are already experiencing. In this PhD study, we address these challenges through the following research thrusts, in which we consider two emerging applications aimed at advancing spectrum efficiency and high-frequency connectivity solutions.
First, we focus on effectively utilizing the existing spectrum resources for emerging applications such as networked UAVs operating within the Unmanned Traffic Management (UTM) system. In this thrust, we develop a coexistence framework for UAVs to share spectrum with traditional cellular networks by using machine learning (ML) techniques so that networked UAVs act as secondary users without interfering with primary users. We propose federated learning (FL) and reinforcement learning (RL) solutions to establish a collaborative spectrum sensing and dynamic spectrum allocation framework for networked UAVs. In the second part, we explore the potential of millimeter-wave (mmWave) and terahertz (THz) frequency bands for high-speed data transmission in urban settings. Specifically, we investigate THz-based midhaul links for 5G networks, where a network's central units (CUs) connect to distributed units (DUs). Through numerical analysis, we assess the feasibility of using 140 GHz links and demonstrate the merits of high-frequency bands to support high data rates in midhaul networks for future urban communications infrastructure. Overall, this research is aimed at establishing frameworks and methodologies that contribute toward the sustainable growth and evolution of wireless connectivity.
Arnab Mukherjee
Attention-Based Solutions for Occlusion Challenges in Person TrackingWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Prasad Kulkarni, ChairSumaiya Shomaji
Hongyang Sun
Jian Li
Abstract
Person tracking and association is a complex task in computer vision applications. Even with a powerful detector, a highly accurate association algorithm is necessary to match and track the correct person across all frames. This method has numerous applications in surveillance, and its complexity increases with the number of detected objects and their movements across frames. A significant challenge in person tracking is occlusion, which occurs when an individual being tracked is partially or fully blocked by another object or person. This can make it difficult for the tracking system to maintain the identity of the individual and track them effectively.
In this research, we propose a solution to the occlusion problem by utilizing an occlusion-aware spatial attention transformer. We have divided the entire tracking association process into two scenarios: occlusion and no-occlusion. When a detected person with a specific ID suddenly disappears from a frame for a certain period, we employ advanced methods such as Detector Integration and Pose Estimation to ensure the correct association. Additionally, we implement a spatial attention transformer to differentiate these occluded detections, transform them, and then match them with the correct individual using the Cosine Similarity Metric.
The features extracted from the attention transformer provide a robust baseline for detecting people, enhancing the algorithms adaptability and addressing key challenges associated with existing approaches. This improved method reduces the number of misidentifications and instances of ID switching while also enhancing tracking accuracy and precision.
Agraj Magotra
Data-Driven Insights into Sustainability: An Artificial Intelligence (AI) Powered Analysis of ESG Practices in the Textile and Apparel IndustryWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Sumaiya Shomaji, ChairPrasad Kulkarni
Zijun Yao
Abstract
The global textile and apparel (T&A) industry is under growing scrutiny for its substantial environmental and social impact, producing 92 million tons of waste annually and contributing to 20% of global water pollution. In Bangladesh, one of the world's largest apparel exporters, the integration of Environmental, Social, and Governance (ESG) practices is critical to meet international sustainability standards and maintain global competitiveness. This master's study leverages Artificial Intelligence (AI) and Machine Learning (ML) methodologies to comprehensively analyze unstructured corporate data related to ESG practices among LEED-certified Bangladeshi T&A factories.
Our study employs advanced techniques, including Web Scraping, Natural Language Processing (NLP), and Topic Modeling, to extract and analyze sustainability-related information from factory websites. We develop a robust ML framework that utilizes Non-Negative Matrix Factorization (NMF) for topic extraction and a Random Forest classifier for ESG category prediction, achieving an 86% classification accuracy. The study uncovers four key ESG themes: Environmental Sustainability, Social : Workplace Safety and Compliance, Social: Education and Community Programs, and Governance. The analysis reveals that 46% of factories prioritize environmental initiatives, such as energy conservation and waste management, while 44% emphasize social aspects, including workplace safety and education. Governance practices are significantly underrepresented, with only 10% of companies addressing ethical governance, healthcare provisions and employee welfare.
To deepen our understanding of the ESG themes, we conducted a Centrality Analysis to identify the most influential keywords within each category, using measures such as degree, closeness, and eigenvector centrality. Furthermore, our analysis reveals that higher certification levels, like Platinum, are associated with a more balanced emphasis on environmental, social, and governance practices, while lower levels focus primarily on environmental efforts. These insights highlight key areas where the industry can improve and inform targeted strategies for enhancing ESG practices. Overall, this ML framework provides a data-driven, scalable approach for analyzing unstructured corporate data and promoting sustainability in Bangladesh’s T&A sector, offering actionable recommendations for industry stakeholders, policymakers, and global brands committed to responsible sourcing.
Samyoga Bhattarai
‘Pro-ID: A Secure Face Recognition System using Locality Sensitive Hashing to Protect Human ID’When & Where:
Eaton Hall, Room 2001B
Committee Members:
Sumaiya Shomaji, ChairTamzidul Hoque
Hongyang Sun
Abstract
Face recognition systems are widely used in various applications, from mobile banking apps to personal smartphones. However, these systems often store biometric templates in raw form, posing significant security and privacy risks. Pro-ID addresses this vulnerability by incorporating SimHash, an algorithm of Locality Sensitive Hashing (LSH), to create secure and irreversible hash codes of facial feature vectors. Unlike traditional methods that leave raw data exposed to potential breaches, SimHash transforms the feature space into high-dimensional hash codes, safeguarding user identity while preserving system functionality.
The proposed system creates a balance between two aspects: security and the system’s performance. Additionally, the system is designed to resist common attacks, including brute force and template inversion, ensuring that even if the hashed templates are exposed, the original biometric data cannot be reconstructed.
A key challenge addressed in this project is minimizing the trade-off between security and performance. Extensive evaluations demonstrate that the proposed method maintains competitive accuracy rates comparable to traditional face recognition systems while significantly enhancing security metrics such as irreversibility, unlinkability, and revocability. This innovative approach contributes to advancing the reliability and trustworthiness of biometric systems, providing a secure framework for applications in face recognition systems.
Shalmoli Ghosh
High-Power Fabry-Perot Quantum-Well Laser Diodes for Application in Multi-Channel Coherent Optical Communication SystemsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Rongqing Hui , ChairShannon Blunt
Jim Stiles
Abstract
Wavelength Division Multiplexing (WDM) is essential for managing rapid network traffic growth in fiber optic systems. Each WDM channel demands a narrow-linewidth, frequency-stabilized laser diode, leading to complexity and increased energy consumption. Multi-wavelength laser sources, generating optical frequency combs (OFC), offer an attractive solution, enabling a single laser diode to provide numerous equally spaced spectral lines for enhanced bandwidth efficiency.
Quantum-dot and quantum-dash OFCs provide phase-synchronized lines with low relative intensity noise (RIN), while Quantum Well (QW) OFCs offer higher power efficiency, but they have higher RIN in the low frequency region of up to 2 GHz. However, both quantum-dot/dash and QW based OFCs, individual spectral lines exhibit high phase noise, limiting coherent detection. Output power levels of these OFCs range between 1-20 mW where the power of each spectral line is typically less than -5 dBm. Due to this requirement, these OFCs require excessive optical amplification, also they possess relatively broad spectral linewidths of each spectral line, due to the inverse relationship between optical power and linewidth as per the Schawlow-Townes formula. This constraint hampers their applicability in coherent detection systems, highlighting a challenge for achieving high-performance optical communication.
In this work, coherent system application of a single-section Quantum-Well Fabry-Perot (FP) laser diode is demonstrated. This laser delivers over 120 mW optical power at the fiber pigtail with a mode spacing of 36.14 GHz. In an experimental setup, 20 spectral lines from a single laser transmitter carry 30 GBaud 16-QAM signals over 78.3 km single-mode fiber, achieving significant data transmission rates. With the potential to support a transmission capacity of 2.15 Tb/s (4.3 Tb/s for dual polarization) per transmitter, including Forward Error Correction (FEC) and maintenance overhead, it offers a promising solution for meeting the escalating demands of modern network traffic efficiently.
TJ Barclay
Proof-Producing Translation from Gallina to CakeMLWhen & Where:
Nichols Hall, Room 250 (Gemini Room)
Committee Members:
Perry Alexander, ChairAlex Bardas
Drew Davidson
Sankha Guria
Eileen Nutting
Abstract
Users of theorem provers often desire to to extract their verified code to a
more efficient, compiled language. Coq's current extraction mechanism provides
this facility but does not provide a formal guarantee that the extracted code
has the same semantics as the logic it is extracted from. Providing such a
guarantee requires a formal semantics for the target code. The CakeML
project, implemented in HOL4, provides a formally defined syntax and semantics
for a subset of SML and includes a proof-producing translator from
higher-order logic to CakeML. We use the CakeML definition to develop a
certifying extractor to CakeML from Gallina using the translation and proof techniques
of the HOL4 CakeML translator. We also address how differences
between HOL4 (higher-order logic) and Coq (calculus of constructions) effect
the implementation details of the Coq translator.