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

David Felton

Optimization and Evaluation of Physical Complementary Radar Waveforms

When & Where:

Tuesday, May 26, 2026 - 1:00 PM
Nichols Hall, Room 129 (Apollo Auditorium)

Committee Members:

Shannon Blunt, Chair
Rachel Jarvis
Patrick McCormick
James Stiles
Zsolt Talata

Abstract

The RF spectrum is a precious, finite resource with ever-increasing demand. Consequently, the mandate to be a "good spectral neighbor" is in direct conflict with the requirements for high-performance sensing where correlation error is fundamentally limited. As such, matched-filter radar performance is often sidelobe-limited with estimation error being constrained by the time-bandwidth (TB) of the collective emission. The methods developed here seek to bridge this gap between idealized radar performance and practical utility via waveform design.

Estimation error becomes more complex when employing pulse-agility. In doing so, range-sidelobe modulation (RSM) spreads energy across Doppler, rendering traditional methods ineffective. To address this, the gradient-based complementary-FM framework was developed to produce complementary sidelobe cancellation (CSC) after coherently combining subsets within a pulse-agile emission. In contrast to the majority of complementary signals, explored via phase-coding, these Comp-FM waveform subsets achieve CSC while preserving hardware-compatibility since they are FM (though design distortion is never completely avoided). Although Comp-FM addressed practicality via hardware amenability, CSC was localized to zero-Doppler. This work expands the Comp-FM notion to a Doppler-generalized (DG) framework, extending the cancellation condition to an arbitrary span. The same framework can likewise be employed to jointly optimize an entire coherent processing interval (CPI) to minimize RSM within the radar point-spread-function (PSF), thereby generalizing the notion of complementarity and introducing the potential for cognitive operation if sufficient scattering knowledge is available a-priori.

Sensing with a single emitter is limited by self-inflicted error alone (e.g., clutter, sidelobes), while MIMO systems must additionally contend with the cross-responses from emitters operating concurrently (e.g., simultaneously, spatially proximate, in a shared spectrum), further degrading radar sensitivity. Now, total correlation error is dictated by the overlapping TB (i.e., how coincident are the signals) and number of operating emitters, compounding difficulty to estimate if left unaddressed. As such, the determination of "orthogonal waveforms" comprises a large portion of MIMO literature, though remains a phenomenological misnomer for pulsed emissions. Here, the notion of complementary-FM is applied to a multi-emitter context in which transmitter-amenable quasi-orthogonal subsets, occupying the same spectral band, are produced via a similar gradient-based approach. To further practicalize these MIMO-Comp-FM waveform subsets, the same "DG" approach described above, addressing the otherwise-default Doppler-induced degradation of complementary signals, is applied. In doing so, Doppler-independent separability and complementarity greatly improves estimation sensitivity for multi-emitter systems.

This MIMO-Comp-FM framework is developed for standard matched filter processing. Coupling this framework with a "DG" form of the previously explored MIMO-MiCRFt is also investigated, illustrating the added benefit of pairing optimized subsets with similarly calibrated processing.

Each of these methods is developed to address unique and increasingly complex sources of estimation error. All approaches are initially developed and evaluated via simulated analysis where ground-truth is known. Then, despite hardware-induced distortion being unavoidable, the MIMO-Comp-FM framework is confirmed via loopback measurements to preserve the majority of CSC that was observed in simulation. Finally, open-air demonstration of each approach validates practical utility on a radar system.

Hao Xuan

Toward an Integrated Computational Framework for Metagenomics: From Sequence Alignment to Automated Knowledge Discovery

When & Where:

Tuesday, May 19, 2026 - 8:00 AM
Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Cuncong Zhong, Chair
Fengjun Li
Suzanne Shontz
Hongyang Sun
Liang Xu

Abstract

Metagenomic sequencing has become a central paradigm for studying complex microbial communities and their interactions with the host, with emerging applications in clinical prediction and disease modeling. In this work, we first investigate two representative application scenarios: predicting immune checkpoint inhibitor response in non-small cell lung cancer using gut microbial signatures, and characterizing host–microbiome interactions in neonatal systems. The proposed reference-free neural network captures both compositional and functional signals without reliance on reference genomes, while the neonatal study demonstrates how environmental and genetic factors reshape microbial communities and how probiotic intervention can mitigate pathogen-induced immune activation.

These studies highlight both the promise and the inherent difficulty of metagenomic analysis: transforming raw sequencing data into clinically actionable insights remains an algorithmically fragmented and computationally intensive process. This challenge arises from two key limitations: the lack of a unified algorithmic foundation for sequence alignment and the absence of systematic approaches for selecting and organizing analytical tools. Motivated by these challenges, we present a unified computational framework for metagenomic analysis that integrates complementary algorithmic and systems-level solutions.

First, to resolve fragmentation at the alignment level, we develop the Versatile Alignment Toolkit (VAT), a unified algorithmic system for biological sequence alignment across diverse applications. VAT introduces an asymmetric multi-view k-mer indexing scheme that integrates multiple seeding strategies within a single architecture and enables dynamic seed-length adjustment via longest common prefix (LCP)–based inference without re-indexing. A flexible seed-chaining mechanism further supports diverse alignment scenarios, including collinear, rearranged, and split alignments. Combined with a hardware-efficient in-register bitonic sorting algorithm and dynamic index-loading strategy, VAT achieves high efficiency and broad applicability across read mapping, homology search, and whole-genome alignment. Second, to address the challenge of tool selection and pipeline construction, we develop SNAIL, a natural language processing system for automated recognition of bioinformatics tools from large-scale and rapidly growing scientific literature. By integrating XGBoost and Transformer-based models such as SciBERT, SNAIL enables structured extraction of analytical tools and supports automated, reproducible pipeline construction.

Together, this work establishes a unified framework that is grounded in real-world applications and addresses key bottlenecks in metagenomic analysis, enabling more efficient, scalable, and clinically actionable workflows.

Pramil Paudel

Learning Without Seeing: Privacy-Preserving and Adversarial Perspectives in Lensless Imaging

When & Where:

Monday, May 18, 2026 - 8:30 AM
Eaton Hall, Room 2001B

Committee Members:

Fengjun Li, Chair
Alex Bardas
Bo Luo
Cuncong Zhong
Haiyang Chao

Abstract

Conventional computer vision relies on spatially resolved, human-interpretable images, which inherently expose sensitive information and raise privacy concerns. In this study, we explore an alternative paradigm based on lensless imaging, where scenes are captured as diffraction patterns governed by the point spread function (PSF). Although unintelligible to humans, these measurements encode structured, distributed information that remains useful for computational inference.

We propose a unified framework for privacy-preserving vision that operates directly on lensless sensor measurements by leveraging their frequency-domain and phase-encoded properties. The framework is developed along two complementary directions. First, we enable reconstruction-free inference by exploiting the intrinsic obfuscation of lensless data. We show that semantic tasks such as classification can be performed directly on diffraction patterns using models tailored to non-local, phase-scrambled representations. We further design lensless-aware architectures and integrate them into practical pipelines, including a Swin Transformer-based steganographic framework (DiffHide) for secure and imperceptible information embedding. To assess robustness, we formalize adversarial threat models and develop defenses against learning-based reconstruction attacks, particularly GAN-driven inversion. Second, we investigate the limits of privacy by studying the reconstructability of lensless measurements without explicit knowledge of the forward model. We develop learning-based reconstruction methods that approximate the inverse mapping and analyze conditions under which sensitive information can be recovered. Our results demonstrate that lensless measurements enable effective vision tasks without reconstruction, while providing a principled framework to evaluate and mitigate privacy risks.

Sharmila Raisa

Digital Coherent Optical System: Investigation and Monitoring

When & Where:

Friday, May 15, 2026 - 11:00 AM
Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Rongqing Hui, Chair
Morteza Hashemi
Erik Perrins
Alessandro Salandrino
Jie Han

Abstract

Coherent wavelength-division multiplexed (WDM) optical fiber systems have become the primary transmission technology for high-capacity data networks, driven by the explosive bandwidth demand of cloud computing, streaming services, and large-scale artificial intelligence training infrastructure. This dissertation investigates two fundamental aspects of digital coherent fiber optic systems under the unifying theme of source and monitoring: the design of multi-wavelength optical sources compatible with high-order coherent detection, and the leveraging of fiber Kerr-effect nonlinearity at the coherent receiver to perform physical-layer link health monitoring and to assess inherent security vulnerabilities — both achieved through digital signal processing of the received complex optical field without dedicated hardware.

We begin by addressing the multi-wavelength transmitter challenge in WDM coherent systems. Existing quantum-dot, quantum-dash, and quantum-well based optical frequency comb (OFC) sources share a common limitation: individual comb line linewidths in the tens of MHz range caused by low output power levels of 1–20 mW, making them incompatible with high-order coherent detection. We demonstrate coherent system application of a single-section InGaAsP QW Fabry-Perot laser diode with greater than 120 mW optical power at the fiber pigtail and 36.14 GHz mode spacing. The high optical power per mode produces Lorentzian equivalent linewidths below 100 kHz — compatible with 16-QAM carrier phase recovery without optical phase locking. Experimental results obtained using a commercial Ciena WaveLogic-Ai coherent transceiver demonstrate 20-channel WDM transmission over 78.3 km of standard single-mode fiber with all channels below the HD-FEC threshold of 3.8 × 10⁻³ at 30 GBaud differential-coded 16-QAM, corresponding to an aggregate capacity of 2.15 Tb/s from a single laser device.

After investigating the QW Fabry-Perot laser as a multi-wavelength source for coherent WDM transmission, we leverage the coherent receiver DSP to exploit fiber Kerr-effect nonlinearity for longitudinal power profile estimation, enabling reconstruction of the signal power distribution P(z) along the full multi-span link without dedicated hardware or traffic interruption. We propose a modified enhanced regular perturbation (ERP) method that corrects two independent physical error sources of the standard RP1 least-squares baseline: the accumulated nonlinear phase rotation, and the dispersion-mediated phase-to-intensity conversion — a second bias source not addressed by prior methods. The RP1 method produces mean absolute error (MAE) that scales quadratically with span count, growing to 1.656 dB at 10 spans and 3 dBm. The modified ERP reduces this to 0.608 dB — an improvement that grows consistently with link length, confirming increasing advantage in the long-haul regime. Extension to WDM through an XPM-aware per-channel formulation achieves MAE of 0.113–0.419 dB across 150–500 km link lengths.

In addition to its role in enabling DSP-based longitudinal power profile estimation, the fiber Kerr-effect nonlinearity is shown to give rise to an inherent physical-layer security vulnerability in coherent WDM systems. We show that an eavesdropper co-tenanting a shared fiber — transmitting a continuous-wave probe at a wavelength adjacent to the legitimate signal — can capture the XPM-induced waveform at the fiber output and apply a bidirectional gated recurrent unit neural network, trained on split-step Fourier method simulation data, to reconstruct the transmitted symbol sequence without physical fiber access and without perturbing the legitimate signal. This eavesdropping mechanism is experimentally validated using a commercial Ciena WaveLogic-Ai coherent transceiver for ASK, BPSK, QPSK, and 16-QAM modulation formats at 4.26 GBaud and 8.56 GBaud over one- and two-span 75 km fiber systems, achieving zero symbol errors under high-OSNR conditions. Noise-aware training over OSNR from 20 to 60 dB maintains symbol error rate below 10⁻² for OSNR above 25–30 dB.

Together, these three contributions demonstrate that the coherent fiber optic system is a versatile physical instrument extending well beyond its role as a data transmission medium. The coherent receiver infrastructure — deployed for high-order modulation and data recovery — simultaneously enables the high-power OFC laser to serve as a practical multi-wavelength transmitter source, and provides the complex field measurement capability through which fiber Kerr-effect nonlinearity can be exploited constructively for distributed link monitoring and, as a direct consequence, reveals an inherent physical-layer security exposure in shared fiber infrastructure. This unified perspective on the coherent system as both a transmission platform and a general-purpose measurement instrument has direct relevance to the design of spectrally efficient, self-monitoring, and physically secure optical interconnects for next-generation AI computing networks.

Even though fiber optics communication is providing a high bandwidth channel to achieve high speed data transmission, there is still a need for higher spectral efficiency, faster data processing speeds while reduced resource requirements due to ever increasing data and media traffic. Various multilevel modulation and demodulation techniques are used to improve spectral efficiency. Although, spectral efficiency is improved, there are other challenges that arise while doing so such as requirement for high speed electronics, receiver sensitivity, chromatic dispersion, operational flexibility etc. Here, we investigate multilevel modulation techniques to improve spectral efficiency while reducing the resource requirements.

We demonstrated a digital-analog hybrid subcarrier multiplexing (SCM) technique which can reduce the requirement of high speed electronics such as ADC and DAC, while providing wideband capability, high spectral efficiency, operational flexibility and controllable data-rate granularity.

With conventional Quadrature Phase Shift Keying (QPSK), to achieve maximum spectral efficiency, we need high spectral efficient Nyquist filters which takes high FPGA resources for digital signal processing (DSP). Hence, we investigated Quadrature Duobinary (QDB) modulation as a solution to reduce the FPGA resources required for DSP while achieving spectral efficiency of 2bits/s/Hz. Currently we are investigating all analog single sideband (SSB) complex field modulated direct detection system. Here, we are trying to achieve higher spectral efficiency by using QDB modulation scheme in comparison to QPSK while avoiding signal-signal beat interference (SSBI) by providing a guard-band based approach.

In coherent detection systems, the MLSE receiver could be implemented using Viterbi algorithm. However, in case of direct detection systems due to square law detection the noise in the received signal is not Gaussian anymore. This leads to requirement of channel behavior estimation for the implementation of MLSE receiver in direct detection systems. Recently, Kramers-Kronig receiver has attracted great deal of attention. We are working on utilizing Kramers-Kronig receiver to implement MLSE receiver for direct detection system without the need for channel estimation.

MAHDI JAFARISHIADEH

New Topology and Improved Control of Modular Multilevel Converter (MMC)-Based Converters

When & Where:

Monday, December 10, 2018 - 3:00 PM
1 Eaton Hall

Committee Members:

Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
James Stiles
Xiaoli (Laura) Li

Abstract

Trends toward large-scale integration and the high-power application of green energy resources necessitate the advent of efficient power converter topologies, multilevel converters. Multilevel inverters are effective solutions for high power and medium voltage DC-to-AC conversion due to their higher efficiency, provision of system redundancy, and generation of near-sinusoidal output voltage waveform. Among many proposed multilevel topologies, the neutral-point-clamped (NPC), flying capacitor (FC), and cascaded H-bridge (CHB) converters are the most well-known classical multilevel topologies. For generation of output voltages with more than five levels, the number of required diodes and capacitors in NPC and FC increases rapidly. Also, these two topologies suffer from a significant capacitor voltage balancing problem. CHBs also require bulky multi-winding transformers to realize several isolated dc sources. Recently, modular multilevel converter (MMC) has become increasingly attractive due to its modularity, high efficiency, excellent output voltage waveform, and no need for separate dc sources. To improve the harmonic profile of the output voltage, there is the need to increase the number of output voltage levels. However, this would require increasing the number of submodules (SMs) and power semi-conductor devices and their associated gate driver and protection circuitry, resulting in the overall multilevel converter to be complex and expensive. Fewer efforts have been devoted to proposing MMC-based multilevel topologies focusing on reduced part count. This work will investigate new medium-voltage high-power MMC-based multilevel inverter with reduced component numbers while using conventional half-bridge SM structure.

The second part of this work is on improving control of MMC-based high-power DC-DC converters. Medium-voltage DC (MVDC) grids have been the focus of numerous research studies in recent years due to their increasing applications in rapidly growing grid-connected renewable energy systems, such as wind, solar and wave farms. MMC-based DC-DC converters are employed for collecting power from offshore wind and wave farms. Among various developed high-power DC-DC converter topologies, MMC-based DC-DC converter with medium-frequency (MF) transformer is a valuable topology due to its numerous advantages. Specifically, they offer a significant reduction in the size of the MMC arm capacitors along with the ac-link transformer and arm inductors due to the ac-link transformer operating at medium frequencies. As such, this work focuses on improving the control of isolated MMMC-based DC-DC converters. Conventionally, the active power is controlled by phase shifts between the primary side and secondary side of transformers. Through this work, adding degree of freedom is investigated by considering the amplitude ratio index of MMC leg as a single control parameter. From the derived analytical formulas, this will lead to operating points where the same active power is transferrable but current stress is reduced. Subsequently, longer lifetimes of the high-frequency transformer and power switches are expected.

With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. As the first component of this dissertation, existing research on CPS security is studied and systematized under a unified framework. Smart cars, as a CPS application, was further explored under the proposed framework and new attacks are identified and addressed.

The Control Area Network (CAN bus) is a prevalent serial communication protocol adopted in industrial CPS, especially in small and large vehicles, ships, planes, and even in drones, radar systems, and submarines. Unfortunately, the CAN bus was designed without any security considerations. We then propose and demonstrate a stealthy targeted Denial of Service (DoS) attack against CAN. Experimentations show that the attack is effective and superior to attacks of the same category due to its stealthiness and ability to avoid detection from current countermeasures.

Two controls are proposed to defend against various spoofing and DoS attacks on CAN. The first one aims to minimize the attack using ID-Hopping mechanism such that CAN arbitration IDs are randomized so an attacker would not be able to target them. ID-Hopping raises the bar for attackers by randomizing the expected patterns in CAN network. Such randomization hinders the attacker's ability to launch targeted DoS attacks. Based on the evaluation on the testbed, the randomization mechanism, ID-Hopping, holds a promising solution for targeted DoS, and reverse engineering CAN IDs, which CAN networks are most vulnerable to. The second countermeasure is a novel CAN firewall that aims to prevent an attacker from launching a plethora of untraditional attacks on CAN that existing solutions do not adequately address. The firewall is placed between a potential attacker’s node and the rest of the CAN bus. Traffic is controlled bidirectionally between the main bus and the attacker’s side so that only benign traffic can pass to the main bus. This ensures that an attacker cannot arbitrarily inject malicious traffic into the main bus. Demonstration and evaluation of the attack and firewall were conducted by a bit-level analysis, i.e., “Bit banging”, of CAN’s traffic. Results show that the firewall successfully prevents the stealthy targeted DoS attack, as well as, other recent attacks. To evaluate the proposed attack and firewall, a testbed was built that consists of BeagleBone Black and STM32 Nucleo-144 microcontrollers to simulate real CAN traffic.

Finally, a design of an Intrusion Detection System (IDS) is proposed to complement the firewall. It utilizes the proposed firewall to add situational awareness capabilities to the bus’s security posture and detect and react to attacks that might bypass the firewall based on certain rules.

SAIKAT SENGUPTA

Understanding Memory Access Behavior for Heterogeneous Memory Systems

When & Where:

Monday, December 3, 2018 - 9:30 AM
2001 B Eaton Hall

Committee Members:

Prasad Kulkarni, Chair
Perry Alexander
Jerzy W. Grzymala-Busse

Abstract

Present day manufacturers have invented different memory technologies with distinct bandwidth, energy and cost tradeoffs. Systems with such heterogeneous memory technologies can only achieve the best performance and power characteristics by appropriately partitioning process data on OS pages and placing OS pages in the right memory areas. To achieve effective data partitioning and placement we need to first understand how programs access memory and how those patterns change at various stages (phases) of program execution. The goal of this work is to build a framework, design experiments and conduct analysis to understand overall memory usage patterns across many programs.

We use Intel’s Pin dynamic binary translation and instrumentation system for this work. Our Pin based framework instruments programs at run-time to collect data regarding memory allocations, de-allocations, reads and writes, which we then analyze using our specialized scripts. We collect and analyze information including page access counts, hot page ratio, memory read and write access patterns and how that varies in different program phases. We also analyze the similarities regarding memory behavior between distinct phases during program execution. We also study memory behavior both with cache and without cache to understand how caches affect the memory access behavior.