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

Muhammad Momin Rahman

Development of a Signal Flow Graph-Based De-Embedding Framework for Broadband Passive Linear Multiport RF & Microwave Networks with Applications to Power Splitters

When & Where:


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

Committee Members:

Carl Leuschen, Chair
Kenneth Demarest
Fernando Rodriguez-Morales


Abstract

Discrete passive components, including resistors, capacitors, inductors, and transformers, are fundamental to broadband 75-Ω radio-frequency (RF) systems, particularly in cable television (CATV) and hybrid fiber-coaxial (HFC) networks. These components are incorporated into power splitters, couplers, power inserters, and amplifiers to achieve desired frequency responses, impedance matching, and signal transmission characteristics. However, their electrical behavior becomes increasingly complex at higher frequencies due to parasitic resistance, inductance, capacitance, electromagnetic coupling, and distributed transmission-line effects. Consequently, accurate broadband characterization is essential for predicting component behavior and developing representative RF circuit models for simulation purposes.

A significant challenge arises when characterizing passive components with limited or unavailable manufacturer specifications, particularly when existing component inventories must be evaluated for broadband applications. Conventional measurement instruments, including digital multimeters, LCR meters, oscilloscopes, and function generators, generally lack the frequency range required for comprehensive RF characterization from 5 MHz to 3 GHz. Vector network analyzers (VNAs) enable complex scattering-parameter measurements across this frequency range; however, the measured response includes contributions from test fixtures, interconnections, and parasitic discontinuities that must be distinguished from the intrinsic response of the device under test (DUT).

This project investigates the development of a signal flow graph (SFG)-based de-embedding framework for broadband passive linear multiport RF networks. The methodology incorporates series-through and shunt-through DUT (CPWG) fixture configurations, conventional Open-Short and Short-Open de-embedding techniques, followed by lumped-element and distributed transmission-line representations of fixture parasitics. Scattering-parameter and ABCD-parameter analysis are employed to characterize fixture responses, while signal flow graph derivations provide a mathematical basis for analyzing interconnected multiport networks. Particular attention is given to fixture asymmetry, impedance discontinuities, and multiple signal propagation paths that complicate the extension of conventional two-port characterization methods to higher-order networks.

Experimental validation is conducted through the characterization of passive components with published impedance and quality-factor characteristics, enabling comparisons between extracted measurements, equivalent-circuit models, and manufacturer reference data. The methodology is subsequently applied to broadband power splitter networks used in CATV/HFC systems to investigate the accuracy and applicability of the proposed multiport de-embedding framework. The project aims to establish a systematic approach to broadband passive component characterization that integrates fixture modeling, network theory, and experimental measurement while reducing dependence on proprietary characterization data.

Fairuz Shadmani Shishir

Efficient and Privacy-Preserving Biomedical AI: From Precision Therapeutics Design to Private Clinical Prediction

When & Where:


Eaton Hall, Room 2001B

Committee Members:

Sumaiya Shomaji, Chair
Tamzidul Hoque
Cuncong Zhong
Bishnu Sarker
Michael Hageman

Abstract

Accurate protein characterization, precision therapeutics design, and privacy-preserving clinical modeling are important challenges in biomedical artificial intelligence (AI). Addressing these challenges requires computational frameworks that are not only accurate and scalable but also capable of supporting reliable decision-making. This dissertation develops efficient, scalable, and reliable computational frameworks across three areas: Computational characterization of proteins, precision therapeutics design, and privacy-aware clinical representation learning. The first part of this dissertation focuses on efficient protein characterization at both the sequence and residue levels. GPCR-SLM, a knowledge-distilled small protein language model, classifies G-protein-coupled receptors (GPCRs), a family targeted by approximately 35% of FDA-approved drugs. Across 86 families, GPCR-SLM achieves 99% accuracy, outperforming BLAST (86.4%) and HMMER (91%), with a 33.5-fold speedup over large protein language models. Extending characterization to the residue level, a protein-metal-ion binding site prediction framework evaluates five protein language models and incorporates positional encoding to capture long-range dependencies. Under 10-fold cross-validation, it achieves a Matthews correlation coefficient of 0.89, with precision, recall, and F1 scores exceeding 95% across six metal ions.

Building on these protein characterization methods, the second part advances precision therapeutics design through uncertainty quantification and generative learning for therapeutic antibodies. NbBayesLM combines protein language model embeddings and physicochemical descriptors in a Bayesian neural network to predict nanobody thermostability. Evaluated on 10,630 VHH sequences, it achieves a mean absolute error of 1.89C and an R2 of 0.67 while estimating epistemic and aleatoric uncertainty to support candidate prioritization. CIgFlow employs classifier-free guidance to generate de novo, antigen-conditioned antibody sequences without fixed scaffolds or paired structural data. Generated sequences achieve a median OASis humanness score of 0.90 and mean predicted local distance difference test (pLDDT) score of 85.0, demonstrating promising novelty and diversity. The third part addresses privacy-aware clinical modeling using electrocardiograms (ECGs), which encode diagnostic information and sensitive demographic attributes. A variational autoencoder with dual discriminators suppresses demographic information while preserving clinically relevant representations.

Collectively, this dissertation advances efficient protein representation learning, computational antibody design, and privacy-preserving clinical AI, contributing methodological foundations for trustworthy biomedical applications.

Arturo Solorio

Performance Analysis of Mismatched Filtering for Stochastic Waveforms

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Committee Members:

Patrick McCormick, Chair
Shannon Blunt
Charles Mohr


Abstract

Mismatched filtering (MMF) is a widely used pulse compression technique that reduces cross-correlation sidelobe levels at the cost of some loss in signal-to-noise ratio (SNR). Applying MMF to stochastically generated radar waveforms makes the filter's performance inherently random, which can be problematic if the variability is excessive. As well as the computational cost being relatively high, as per pulse, a new MMF optimization is required. In this thesis, the feasibility of a fixed MMF strategy for stochastic radar waveforms to reduce computational cost is investigated. Two MMF formulations were evaluated, the optimal MMF (Diagonal Loaded Filter) and a projection-based MMF. Both filters were designed to minimize sidelobe energy within specified regions of the Cross-Ambiguity Function (CAF) for Stochastic Waveform Generation (StoWGe) waveforms, and their performance was assessed across multiple time-bandwidth products and sidelobe-regions for two center frequencies, Fc = 30 Ghz and Fc  = 3.5 Ghz. The MMF performance was characterized using SNR loss and sidelobe depth over hundreds of trails. It is observed that the variability in SNR and sidelobe depth decreases as the waveform time-bandwdith product and the sidelobe region targeted for minimization increase, although the Fc = 3.5 GHz cases exhibited a plateau in achievable sidelobe depth. Additional validation using 1000-trail histograms and loop-back testing confirmed the robustness for the fixed MMF approach for higher time-bandwidth product stochastic waveforms. Where, overall, the results demonstrate that a fixed MMF strategy is feasible for stochastic waveforms when the time-bandwdith product and sidelobe region are sufficiently large. Furthermore, the Projection-based MMF provides a suboptimal alternative to the optimal MMF as it achieves comparable MMF performance when constrained to similar SNR-loss and may be desirable when the size of the domination eigenvalues is small relative to the filter length and can be efficiency obtained.

Past Defense Notices

Dates

JAISNEET BHANDAL

Classification of Private Tweets using Tweets Content

When & Where:


2001B Eaton Hall

Committee Members:

Bo Luo, Chair
Jerzy Grzymala-Busse
Prasad Kulkarni


Abstract

Online social networks (OSNs) like Twitter provide an open platform for users to easily convey their thoughts and ideas from personal experiences to breaking news. With the increasing popularity of Twitter and the explosion of tweets, we have observed large amounts of potentially sensitive/private messages being published to OSNs inadvertently or voluntarily. The owners of these messages may become vulnerable to online stalkers or adversaries, and they often regret posting such messages. Therefore, identifying tweets that reveal private/sensitive information is critical for both the users and the service providers. However, the definition of sensitive information is subjective and different from person to person. To develop a privacy protection mechanism that is customizable to fit the needs of diverse audiences, it is essential to accurately and automatically identify and classify potentially sensitive tweets. 
In this project, we adopted a two-step approach - private tweet identification, and private tweet classification. We make the first attempt to classify private tweets into two main categories, sensitive and nonsensitive - private tweet identification, followed by private tweet classification where we categorize the sensitive tweets into 13 pre-defined topics. We consider private tweet identification and private tweet classification as dual-problems. Progress towards one of them will eventually benefit the other. We used a 2-layer classification approach, where we explore different combinations of classifiers, and analyze the performance of each combination. 

JONATHAN LYLE

A Digital Approach to Bistatic Radar Synchronization via GPS PPS

When & Where:


246 Nichols Hall

Committee Members:

Carl Leuschen, Chair
Chris Allen
Jilu Li


Abstract

Bistatic Radar systems utilize physically separate transmit and receive systems to collect information that monostatic systems cannot. One issue with developing bisatic systems is guaranteeing synchronization between the transmitters and receivers. This project presents a purely digital method for improving synchronization of a bistatic system based on the GPS PPS signal, and using step-time for both transmitter and receiver timing. The issue of bistatic synchronization is simulated in Matlab and then modified to utilize the proposed step-time adjustment to show that the method works in theory. This method is then implemented in hardware on the digital system of CReSIS’s ‘HF Sounder’ radar system, and then tested to verify that the proposed method can be implemented in hardware and that it improves performance.

TYLER WADE

AOT Vs. JIT: Impact of Profile Data on Code Quality

When & Where:


246 Nichols Hall

Committee Members:

Prasad Kulkarni, Chair
Perry Alexander
Heechul Yun


Abstract

Just-in-time (JIT) compilation during program execution and 
ahead-of-time (AOT) compilation during software installation are 
alternate techniques used by managed language virtual machines 
(VM) to generate optimized native code while simultaneously 
achieving binary code portability and high execution performance. 
JIT compilers typically collect profile information at run-time to 
enable profile-guided optimizations (PGO) to customize the gener- 
ated native code to different program inputs/behaviors. AOT com- 
pilation removes the speed and energy overhead of online profile 
collection and dynamic compilation, but may not be able to achieve 
the quality and performance of customized native code. The goal 
of this work is to investigate and quantify the implications of the 
AOT compilation model on the quality of the generated native code 
for current VMs. 
First, we quantify the quality of native code generated by the 
two compilation models for a state-of-the-art (HotSpot) Java VM. 
Second, we determine how the amount of profile data collected af- 
fects the quality of generated code. Third, we develop a mechanism 
to determine the accuracy or similarity of different profile data for a 
given program run, and investigate how the accuracy of profile data 
affects its ability to effectively guide PGOs. Finally, we categorize 
the profile data types in our VM and explore the contribution of 
each such category to performance. 

LOHITH NANUVALA

An Implementation of the MLEM2 Algorithm

When & Where:


1 Eaton Hall

Committee Members:

Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Richard Wang


Abstract

Data mining is the process of finding meaningful information from data. Data mining can be used in several areas like business, medicine, education etc. It allows us to find patterns in the data and make predictions for the future. One form of data mining is to extract rules from data sets. In this project we discuss an implementation of one of the data mining algorithms called MLEM2 (Modified Learning from Examples Module, version 2). This algorithm uses the concept of blocks of attribute-value pairs. It is also robust and generates rules for both complete and incomplete data sets with numeric and symbolic attributes. A rule checker has been developed which is used to evaluate the rule sets produced by MLEM2. The accuracy of the rules is measured by computing the error rate which is the ratio of the number of incorrectly classified cases to the total number of all cases. Experiments are conducted on different kinds of data sets (complete, incomplete, numeric and symbolic) using 10-fold cross validation method.

ASHWINI BALACHANDRA

Implementation of Truncated Lévy Walk Mobility Model in ns-3

When & Where:


246 Nichols Hall

Committee Members:

James Sterbenz, Chair
Victor Frost
Fengjun Li


Abstract

Mobility models generate the mobility patterns of the nodes in a given system. Mobility models help us to analyze and study the characteristic of new and existing systems. Various mobility models implemented in network simulation tools like ns-3 does not model the patterns of human mobility. The main idea of this project is to implement the truncated Lévy walk mobility model in ns-3. The model has two variations, in the first variation, the flight length and pause time of the nodes are determined from the truncated Pareto distribution and in the second variation, Lévy distribution models the flight length and pause time distributions and the values are obtained by Lévy α-stable random number generator. The mobility patterns of the nodes are generated and analyzed for the model by changing various model attributes. Further studies can be done to understand the behavior of these models for different ad hoc networking protocols.

PAVAN KUMAR MOTURU

Image Processing Techniques in Matlab GUI

When & Where:


246 Nichols Hall

Committee Members:

Carl Leuschen, Chair
Chris Allen
Fernando Rodriguez-Morales


Abstract

Identifying missing bed in radar data is very important in sea level changes. Increase in sea level is a problem of global importance because of its impact on infrastructure. Ice sheets in the Greenland and Antarctic are melting and increasing their contribution to sea level change over the last decade. Measuring ice sheets thickness is required to estimate sea level rise. We need to use several algorithms, pre-defined functions to extract the weak bed echoes, but we don’t have a tool in Matlab which contains some important algorithms like ImageJ. We can’t process all the data in ImageJ as Matlab produces better results compared to ImageJ as some of the functions like window and symmetric selection around center in FFT domain are not implemented in ImageJ. 
In this project, we will investigate the application of some image processing techniques using a GUI developed for analyzing ice sounding radargrams. One key advantage of the tool is that the image processing techniques are applied in a single GUI instead of doing separately. We apply these techniques on the data which came after applying extensive signal processing techniques. After performing these techniques, we compare the processed data with the original data. 

ASHWINI BALACHANDRA

Implementation of Truncated Lévy Walk Mobility Model in ns-3

When & Where:


246 Nichols Hall

Committee Members:

James Sterbenz, Chair
Victor Frost
Fengjun Li


Abstract

Mobility models generate the mobility patterns of the nodes in a given system. Mobility models help us to analyze and study the characteristic of new and existing systems. Various mobility models implemented in network simulation tools like ns-3 does not model the patterns of human mobility. The main idea of this project is to implement the truncated Lévy walk mobility model in ns-3. The model has two variations, in the first variation, the flight length and pause time of the nodes are determined from the truncated Pareto distribution and in the second variation, Lévy distribution models the flight length and pause time distributions and the values are obtained by Lévy α-stable random number generator. The mobility patterns of the nodes are generated and analyzed for the model by changing various model attributes. Further studies can be done to understand the behavior of these models for different ad hoc networking protocols. 

 

 

MOHSEN ALEENEJAD

New Modulation Methods and Control Strategies for Power Converters

When & Where:


1 Eaton Hall

Committee Members:

Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang

Abstract

The DC to AC power Inverters (so-called Inverters) are widely used in industrial applications. The multilevel Inverters are becoming increasingly popular in industrial apparatus aimed at medium to high power conversion applications. In comparison to the conventional inverters, they feature superior characteristics such as lower total harmonic distortion (THD), higher efficiency, and lower switching voltage stress{Malinowski, 2010 #9}{Malinowski, 2010 #9}. Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. As a general rule in a Inverter topology, as the number of power electronic switches increases, the chances of fault occurrence on of the switches increases, and thus the Inverter’s reliability decreases. Due to the extreme monetary ramifications of the interruption of operation in commercial and industrial applications, high reliability for power Inverters utilized in these sectors is critical. As a result, developing fault-tolerant operation schemes for multilevel Inverters has always been an interesting topic for researchers in related areas. The purpose of this proposal is to develop new control and fault-tolerant strategies for the multilevel power Inverter. In the event of a fault, the line voltages of the faulty Inverters are unbalanced and cannot be applied to the three phase loads. This fault-tolerant strategy generates balanced line voltages without bypassing any healthy and operative Inverter element, makes better use of the Inverter capacity and generates higher output voltage. This strategy exploits the advantages of the Selective Harmonic Elimination (SHE) method in conjunction with a slightly modified Fundamental Phase Shift Compensation technique to generate balanced voltages and manipulate voltage harmonics at the same time. However, due to the distinctive requirement of the strategy to manipulate both amplitude and angle of the harmonics, the conventional SHE technique is not the suitable basis for the proposed strategy. Therefore, in this project a modified Unbalanced SHE technique which can be used as the basis for the fault-tolerant strategy is developed. The proposed strategy is applicable to several classes of multilevel Inverters with three or more voltage levels. 

MOHSEN ALEENEJAD

New Modulation Methods and Control Strategies for Power Converters

When & Where:


1 Eaton Hall

Committee Members:

Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang

Abstract

The DC to AC power Inverters (so-called Inverters) are widely used in industrial applications. The multilevel Inverters are becoming increasingly popular in industrial apparatus aimed at medium to high power conversion applications. In comparison to the conventional inverters, they feature superior characteristics such as lower total harmonic distortion (THD), higher efficiency, and lower switching voltage stress{Malinowski, 2010 #9}{Malinowski, 2010 #9}. Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. As a general rule in a Inverter topology, as the number of power electronic switches increases, the chances of fault occurrence on of the switches increases, and thus the Inverter’s reliability decreases. Due to the extreme monetary ramifications of the interruption of operation in commercial and industrial applications, high reliability for power Inverters utilized in these sectors is critical. As a result, developing fault-tolerant operation schemes for multilevel Inverters has always been an interesting topic for researchers in related areas. The purpose of this proposal is to develop new control and fault-tolerant strategies for the multilevel power Inverter. In the event of a fault, the line voltages of the faulty Inverters are unbalanced and cannot be applied to the three phase loads. This fault-tolerant strategy generates balanced line voltages without bypassing any healthy and operative Inverter element, makes better use of the Inverter capacity and generates higher output voltage. This strategy exploits the advantages of the Selective Harmonic Elimination (SHE) method in conjunction with a slightly modified Fundamental Phase Shift Compensation technique to generate balanced voltages and manipulate voltage harmonics at the same time. However, due to the distinctive requirement of the strategy to manipulate both amplitude and angle of the harmonics, the conventional SHE technique is not the suitable basis for the proposed strategy. Therefore, in this project a modified Unbalanced SHE technique which can be used as the basis for the fault-tolerant strategy is developed. The proposed strategy is applicable to several classes of multilevel Inverters with three or more voltage levels.

SIVA RAM DATTA BOBBA

Rule Induction For Numerical Data using PRISM

When & Where:


2001B Eaton Hall

Committee Members:

Jerzy Grzymala-Busse, Chair
Bo Luo
James Miller


Abstract

Rule induction is one of the basic and important techniques of data mining. Inducing a rule set for symbolic data is simple and straightforward, but it becomes complex when the attributes are numerical. There are several algorithms available that do the task of rule induction for symbolic data. One such algorithm is PRISM which uses conditional probability for attribute-value selection to induce a rule. 
In the real world scenario, data may comprise of either symbolic or numerical attributes. It becomes difficult to induce a discriminant ruleset on the data with numerical attributes. This project provides an implementation of PRISM to handle numerical data. First, it takes as input, a dataset with numerical attributes and converts them into discrete values using the multiple scanning approach which identifies the cut-points for intervals using minimum conditional entropy. Once discretization completes, PRISM uses these discrete values to induce ruleset for each decision. Thus, this project helps to induce modular rulesets over a numerical dataset.