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
Andrew Riachi
An Investigation Into The Memory Consumption of Web Browsers and A Memory Profiling Tool Using Linux SmapsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Degree Type:
MS Thesis DefenseCommittee Members:
Prasad Kulkami, ChairPerry Alexander
Drew Davidson
Heechul Yun
Abstract
Web browsers are notorious for consuming large amounts of memory. Yet, they have become the dominant framework for writing GUIs because the web languages are ergonomic for programmers and have a cross-platform reach. These benefits are so enticing that even a large portion of mobile apps, which have to run on resource-constrained devices, are running a web browser under the hood. Therefore, it is important to keep the memory consumption of web browsers as low as practicable.
In this thesis, we investigate the memory consumption of web browsers, in particular, compared to applications written in native GUI frameworks. We introduce smaps-profiler, a tool to profile the overall memory consumption of Linux applications that can report memory usage other profilers simply do not measure. Using this tool, we conduct experiments which suggest that most of the extra memory usage compared to native applications could be due the size of the web browser program itself. We discuss our experiments and findings, and conclude that even more rigorous studies are needed to profile GUI applications.
Elizabeth Wyss
A New Frontier for Software Security: Diving Deep into npmWhen & Where:
Eaton Hall, Room 2001B
Degree Type:
PhD Dissertation DefenseCommittee 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)
Degree Type:
MS Thesis DefenseCommittee 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.
Qua Nguyen
Hybrid Array and Privacy-Preserving Signaling Optimization for NextG Wireless CommunicationsWhen & Where:
Zoom (ID: 87142881713 Passcode: 135902)
Degree Type:
PhD Dissertation DefenseCommittee Members:
Erik Perrins, ChairMorteza Hashemi
Zijun Yao
Taejoon Kim
KC Long
Abstract
This PhD research tackles two critical challenges in NextG wireless networks: hybrid precoder design for wideband sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communications and privacy-preserving federated learning (FL) over wireless networks.
In the first part, we propose a novel hybrid precoding framework that integrates true-time delay (TTD) devices and phase shifters (PS) to counteract the beam squint effect - a significant challenge in the wideband sub-THz massive MIMO systems that leads to considerable loss in array gain. Unlike previous methods that only designed TTD values while fixed PS values and assuming unbounded time delay values, our approach jointly optimizes TTD and PS values under realistic time delays constraint. We determine the minimum number of TTD devices required to achieve a target array gain using our proposed approach. Then, we extend the framework to multi-user wideband systems and formulate a hybrid array optimization problem aiming to maximize the minimum data rate across users. This problem is decomposed into two sub-problems: fair subarray allocation, solved via continuous domain relaxation, and subarray gain maximization, addressed via a phase-domain transformation.
The second part focuses on preserving privacy in FL over wireless networks. First, we design a differentially-private FL algorithm that applies time-varying noise variance perturbation. Taking advantage of existing wireless channel noise, we jointly design differential privacy (DP) noise variances and users transmit power to resolve the tradeoffs between privacy and learning utility. Next, we tackle two critical challenges within FL networks: (i) privacy risks arising from model updates and (ii) reduced learning utility due to quantization heterogeneity. Prior work typically addresses only one of these challenges because maintaining learning utility under both privacy risks and quantization heterogeneity is a non-trivial task. We approach to improve the learning utility of a privacy-preserving FL that allows clusters of devices with different quantization resolutions to participate in each FL round. Specifically, we introduce a novel stochastic quantizer (SQ) that ensures a DP guarantee and minimal quantization distortion. To address quantization heterogeneity, we introduce a cluster size optimization technique combined with a linear fusion approach to enhance model aggregation accuracy. Lastly, inspired by the information-theoretic rate-distortion framework, a privacy-distortion tradeoff problem is formulated to minimize privacy loss under a given maximum allowable quantization distortion. The optimal solution to this problem is identified, revealing that the privacy loss decreases as the maximum allowable quantization distortion increases, and vice versa.
This research advances hybrid array optimization for wideband sub-THz massive MIMO and introduces novel algorithms for privacy-preserving quantized FL with diverse precision. These contributions enable high-throughput wideband MIMO communication systems and privacy-preserving AI-native designs, aligning with the performance and privacy protection demands of NextG networks.
Arin Dutta
Performance Analysis of Distributed Raman Amplification with Different Pumping ConfigurationsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Degree Type:
PhD Dissertation DefenseCommittee Members:
Rongqing Hui, ChairMorteza Hashemi
Rachel Jarvis
Alessandro Saladrino
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.
Rich Simeon
Delay-Doppler Channel Estimation for High-Speed Aeronautical Mobile Telemetry ApplicationsWhen & Where:
Eaton Hall, Room 2001B
Degree Type:
PhD Comprehensive DefenseCommittee Members:
Erik Perrins, ChairShannon Blunt
Morteza Hashemi
James 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
Degree Type:
MS Project DefenseCommittee 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.
Past Defense Notices
Oluwanisola Ibikunle
DEEP LEARNING ALGORITHMS FOR RADAR ECHOGRAM LAYER TRACKINGWhen & Where:
Richard K. Moore Conference Room
Degree Type:
PhD Comprehensive DefenseCommittee Members:
Shannon Blunt, ChairCarl Leuschen
Jilu Li
James Stiles
Chris Depcik
Abstract
The accelerated melting of ice sheets in the polar regions of the world, specifically in Greenland and Antarctica, due to contemporary climate warming is contributing to global sea level rise. To understand and quantify this phenomenon, airborne radars have been deployed to create echogram images that map snow accumulation patterns in these regions. Using advanced radar systems developed by the Center for Remote Sensing and Integrated Systems (CReSIS), a significant amount (1.5 petabytes) of climate data has been collected. However, the process of extracting ice phenomenology information, such as accumulation rate, from the data is limited. This is because the radar echograms require tracking of the internal layers, a task that is still largely manual and time-consuming. Therefore, there is a need for automated tracking.
Machine learning and deep learning algorithms are well-suited for this problem given their near-human performance on optical images. Moreover, the significant overlap between classical radar signal processing and machine learning techniques suggests that fusion of concepts from both fields can lead to optimized solutions for the problem. However, supervised deep learning algorithms suffer the circular problem of first requiring large amounts of labeled data to train the models which do not exist currently.
In this work, we propose custom algorithms, including supervised, semi-supervised, and self-supervised approaches, to deal with the limited annotated data problem to achieve accurate tracking of radiostratigraphic layers in echograms. Firstly, we propose an iterative multi-class classification algorithm, called “Row Block,” which sequentially tracks internal layers from the top to the bottom of an echogram given the surface location. We aim to use the trained iterative model in an active learning paradigm to progressively increase the labeled dataset. We also investigate various deep learning semantic segmentation algorithms by casting the echogram layer tracking problem as a binary and multiclass classification problem. These require post-processing to create the desired vector-layer annotations, hence, we propose a custom connected-component algorithm as a post-processing routine. Additionally, we propose end-to-end algorithms that avoid the post-processing to directly create annotations as vectors. Furthermore, we propose semi-supervised algorithms using weakly-labeled annotations and unsupervised algorithms that can learn the latent distribution of echogram snow layers while reconstructing echogram images from a sparse embedding representation.
A concurrent objective of this work is to provide the deep learning and science community with a large fully-annotated dataset. To achieve this, we propose synchronizing radar data with outputs from a regional climate model to provide a dataset with overlapping measurements that can enhance the performance of the trained models.
Prashanthi Mallojula
On the Security of Mobile and Auto Companion AppsWhen & Where:
Nichols Hall 246 | Executive Conference Room
Degree Type:
PhD Comprehensive DefenseCommittee Members:
Bo Luo, ChairAlex Bardas
Fengjun Li
Hongyang Sun
Huazhen Fang
Abstract
Today’s smartphone platforms have millions of applications, which not only access users’ private data but also information from the connected external services and IoT/CPS devices. Mobile application security involves protecting sensitive information and securing communication between the application and external services or devices. We focus on these two key aspects of mobile application security.
In the first part of this dissertation, we aim to ensure the security of user information collected by mobile apps. Mobile apps seek consent from users to approve various permissions to access sensitive information such as location and personal information. However, users often blindly accept permission requests and apps start to abuse this mechanism. As long as a permission is requested, the state-of-the-art security mechanisms will treat it as legitimate. We ask the question whether the permission requests are valid? We attempt to validate permission requests using statistical analysis on permission sets extracted from groups of functionally similar apps. We detected mobile applications with abusive permission access and measure the risk of information leaks through each mobile application.
Second, we propose to investigate the security of auto companion apps. Auto companion apps are mobile apps designed to remotely connect with cars to provide features such as diagnostics, navigation, entertainment, and safety alerts. However, this can lead to several security threats, for instance, onboard information of vehicles can be tracked or altered through a malicious app. We design a comprehensive security analysis framework on automotive companion apps all stages of communication and collaboration between vehicles and companion apps such as connection establishment, authentication, encryption, information storage, and Vehicle diagnostic and control command access. By conducting static and network traffic analysis of Android OBD apps, we identify a series of vulnerability scenarios. We further evaluate these vulnerabilities with vehicle-based testing and identify potential security threats associated with auto companion apps.
Michael Nieses
Trustworthy Measurements of a Linux Kernel and Layered Attestation via a Verified MicrokernelWhen & Where:
Nichols Hall, Room 246
Degree Type:
PhD Comprehensive DefenseCommittee Members:
Perry Alexander, ChairDrew Davidson
Matthew Moore
Cuncong Zhong
Corey Maley
Abstract
Layered attestation is a process by which one can establish trust in a remote party. It is a special case of attestation in which different layers of the attesting system are handled distinctly. This type of trust is desirable because a vast and growing number of people depend on networked devices to go about their daily lives. Current architectures for remote attestation are lacking in process isolation, which is evidenced by the existence of virtual machine escape exploits. This implies a deficiency of trustworthy ways to determine whether a networked Linux system has been exploited. The seL4 microkernel, uniquely in the world, has machine-checked proofs concerning process confidentiality and integrity. The seL4 microkernel is leveraged here to provide a verified level of software-based process isolation. When complemented with a comprehensive collection of measurements, this architecture can be trusted to report its own corruption. The architecture is described, implemented, and tested against a variety of exploits, which are detected using introspective measurement techniques.
Blake Douglas Bryant
Building Better with Blocks – A Novel Secure Multi-Channel Internet Memory Information Control (S-MIMIC) Protocol for Complex Latency Sensitive ApplicationsWhen & Where:
Eaton Hall, Room 2001B
Degree Type:
PhD Dissertation DefenseCommittee Members:
Hossein Saiedian, ChairArvin Agah
Perry Alexander
Bo Luo
Reza Barati
Abstract
Multimedia networking is the area of study associated with the delivery of heterogeneous data including, but not limited to, imagery, video, audio, and interactive content. Multimedia and communication network researchers have continually struggled to devise solutions for addressing the three core challenges in multimedia delivery: security, reliability, and performance. Solutions to these challenges typically exist in a spectrum of compromises achieving gains in one aspect at the cost of one or more of the others. Networked videogames represent the pinnacle of multimedia presented in a real-time interactive format. Continual improvements to multimedia delivery have led to tools such as buffering, redundant coupling of low-resolution alternative data streams, congestion avoidance, and forced in-order delivery of best-effort service; however, videogames cannot afford to pay the latency tax of these solutions in their current state.
I developed the Secure Multi-Channel Internet Memory Information Control (S-MIMIC) protocol as a novel solution to address these challenges. The S-MIMIC protocol leverages recent developments in blockchain and distributed ledger technology, coupled with creative enhancements to data representation and a novel data model. The S-MIMIC protocol also implements various novel algorithms for create, read, update, and delete (CRUD) interactions with distributed ledger and blockchain technologies. For validation, the S-MIMIC protocol was integrated with an open source open source First-Person Shooter (FPS) videogame to demonstrate its ability to transfer complex data structures under extreme network latency demands. The S-MIMIC protocol demonstrated improvements in confidentiality, integrity, availability and data read operations under all test conditions. Data write performance of S-MIMIC is slightly below traditional TCP-based networking in unconstrained networks, but matches performance in networks exhibiting 150 milliseconds of delay or more.
Though the S-MIMIC protocol was evaluated for use in networked videogames, its potential uses are far reaching with promising applicability to medical information, legal documents, financial transactions, information security threat feeds and many other use cases that require security, reliability and performance guarantees.
Archana Chalicheemala
A Machine Learning Study using Gene Expression Profiles to Distinguish Patients with Non-Small Cell Lung CancerWhen & Where:
Eaton Hall, Room 2001B
Degree Type:
MS Project DefenseCommittee Members:
Zijun Yao, ChairPrasad Kulkarni
Hongyang Sun
Abstract
Early diagnosis can effectively treat non-small cell lung cancer (NSCLC). Lung cancer cells usually have altered gene expression patterns compared to normal cells, which can be utilized to predict cancer through gene expression tests. This study analyzed gene expression values measured from 15227-probe microarray, and 290 patients consisting of cancer and control groups, to find relations between the gene expression features and lung cancer. The study explored k-means, statistical tests, and deep neural networks to obtain optimal feature representations and achieved the highest accuracy of 82%. Furthermore, a bipartite graph was built using the Bio Grid database and gene expression values, where the probe-to-probe relationship based on gene relevance was leveraged to enhance the prediction performance.
Yoganand Pitta
Insightful Visualization: An Interactive Dashboard Uncovering Disease Patterns in Patient Healthcare DataWhen & Where:
Eaton Hall, Room 2001B
Degree Type:
MS Project DefenseCommittee Members:
Zijun Yao, ChairPrasad Kulkarni
Hongyang Sun
Abstract
As Electronic Health Records (EHRs) become more available, there is increasing interest in discovering hidden disease patterns by leveraging cutting-edge data visualization techniques, such as graph-based knowledge representation and interactive graphical user interfaces (GUIs). In this project, we have developed a web-based interactive EHR analytics and visualization tool to provide healthcare professionals with valuable insights that can ultimately improve the quality and cost-efficiency of patient care. Specifically, we have developed two visualization panels: one for the intelligence of individual patients and the other for the relevance among diseases. For individual patients, we capture the similarity between them by linking them based on their relatedness in diagnosis. By constructing a graph representation of patients based on this similarity, we can identify patterns and trends in patient data that may not be apparent through traditional methods. For disease relationships, we provide an ontology graph for the specific diagnosis (ICD10 code), which helps to identify ancestors and predecessors of a particular diagnosis. Through the demonstration of this dashboard, we show that this approach can provide valuable insights to better understand patient outcomes with an informative and user-friendly web interface.
Michael Cooley
Machine Learning for Navel Discharge ReviewWhen & Where:
Eaton Hall, Room 1
Degree Type:
MS Project DefenseCommittee Members:
Prasad Kulkarni, ChairDavid Johnson
Jerzy Grzymala-Busse
Abstract
This research project aims to predict the outcome of the Naval Discharge Review Board decision for an applicant based on factors in the application, using Machine Learning techniques. The study explores three popular machine learning algorithms: MLP, Adaboost, and KNN, with KNN providing the best results. The training is verified through hyperparameter optimization and cross fold validation.
Additionally, the study investigates the ability of ChatGPT's API to classify the data that couldn't be classified manually. A total of over 8000 samples were classified by ChatGPT's API, and an MLP model was trained using the same hyperparameters that were found to be optimal for the 3000 size manual sample.The model was then tested on the manual sample. The results show that the model trained on data labeled by ChatGPT performed equivalently, suggesting that ChatGPT's API is a promising tool for labeling in this domain.
Sarah Johnson
Formal Analysis of TPM Key Certification ProtocolsWhen & Where:
Nichols Hall, Room 246
Degree Type:
MS Thesis DefenseCommittee Members:
Perry Alexander, ChairMichael Branicky
Emily Witt
Abstract
Development and deployment of trusted systems often require definitive identification of devices. A remote entity should have confidence that a device is as it claims to be. An ideal method for fulfulling this need is through the use of secure device identitifiers. A secure device identifier (DevID) is defined as an identifier that is cryptographically bound to a device. A DevID must not be transferable from one device to another as that would allow distinct devices to be identified as the same. Since the Trusted Platform Module (TPM) is a secure Root of Trust for Storage, it provides the necessary protections for storing these identifiers. Consequently, the Trusted Computing Group (TCG) recommends the use of TPM keys for DevIDs. The TCG's specification TPM 2.0 Keys for Device Identity and Attestation describes several methods for remotely proving a key to be resident in a specific device's TPM. These methods are carefully constructed protocols which are intended to be performed by a trusted Certificate Authority (CA) in communication with a certificate-requesting device. DevID certificates produced by an OEM's CA at device manufacturing time may be used to provide definitive evidence to a remote entity that a key belongs to a specific device. Whereas DevID certificates produced by an Owner/Administrator's CA require a chain of certificates in order to verify a chain of trust to an OEM-provided root certificate. This distinction is due to the differences in the respective protocols prescribed by the TCG's specification. We aim to abstractly model these protocols and formally verify that their resulting assurances on TPM-residency do in fact hold. We choose this goal since the TCG themselves do not provide any proofs or clear justifications for how the protocols might provide these assurances. The resulting TPM-command library and execution relation modeled in Coq may easily be expanded upon to become useful in verifying a wide range of properties regarding DevIDs and TPMs.
Anna Fritz
Negotiating Remote Attestation ProtocolsWhen & Where:
Nichols Hall, Room 246
Degree Type:
PhD Comprehensive DefenseCommittee Members:
Perry Alexander, ChairAlex Bardas
Drew Davidson
Fengjun Li
Emily Witt
Abstract
During remote attestation, a relying party prompts a target to perform some stateful measurement which can be appraised to determine trust in the target's system. In this current framework, requested measurement operations must be provisioned by a knowledgeable system user who may fail to consider situational demands which potentially impact the desired measurement. To solve this problem, we introduce negotiation: a framework that allows the target and relying party to mutually determine an attestation protocol that satisfies both the target's need to protect sensitive information and the relying party's desire for a comprehensive measurement. We designed and verified this negotiation procedure such that for all negotiations, we can provably produce an executable protocol that satisfies the targets privacy standards. With the remainder of this work, we aim to realize and instantiate protocol orderings ensuring negotiation produces a protocol sufficient for the relying party. All progress is towards our ultimate goal of producing a working, fully verified negotiation scheme which will be integrated into our current attestation framework for flexible, end-to-end attestations.