Automated Root Tracing Using Deep Learning

Roots play a crucial role in plant development by anchoring plants, absorbing nutrients, and maintaining soil structure. Understanding root structures and dynamics is vital for ecological research and assessing soil health. However, tracing roots from photos obtained with Minirhizotron is a time-consuming task, and applying deep learning techniques can facilitate this process. This thesis applies the DeepLabV3+ model with a confidence weighted approach to segment root structures in soil images. The methodology involves classifying images based on root visibility, cropping images to focus on root regions, and training the DeepLabV3+ model, which employs atrous convolutions and an Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual information. The confidence method modulates the loss function based on pixel confidence scores to handle ambiguous boundaries and low-resolution images. The confidence function decreases with distance from root boundaries and adapts to varying scales. This method was tested on multiple datasets from natural environments with varying soil types, including Mepibdeath, Ban Harol, Champenoux, and Hesse, which allowed for an assessment of the robustness and generalization ability of the tested models. Evaluated using metrics such as Cohen’s kappa and R2 for surface and length, the results show that the confidence-weighted approach improves segmentation quality by reducing false positives but may miss weakly expressed roots. Future work should focus on enhancing model robustness and improving training data quality to handle complex root structures and environmental noise better.M.S.Computer Scienc

An immune-competent microvascularized human lung-on-chip device for studying immunopathologies of the lung

Severe influenza affects 3-5 million people worldwide each year, resulting in >300,000 deaths. Standard-of-care antiviral therapeutics have limited effectiveness in these patients where infection severity is driven by an aberrant immune response. In severe influenza, the hyperactive immune system causes acute cytokine storm, cytopenia, and local tissue damage. Current preclinical models of severe influenza, in small animal models and in vitro, fail to recapitulate the human immune response to severe viral infection accurately. Here, we bioengineered a human lung tissue model that represents small airway structures with tissue-resident and circulatory immune cells. The immune-competent lung tissue model comprises of a 3D, perfusable microvascular network underneath a mature, differentiated epithelium at an air-liquid interface.       With this model, we demonstrate that a conventional lung-on-chip (LOC) that lacks immune cells induces limited cytokine response to severe influenza infection, and while a LOC with tissue-resident macrophages induces significant response in the airway, the presence of both tissue-resident and circulatory immune cells was necessary to elicit a significant airway and interstitial cytokine storm. We demonstrate through extensive microscopy, secretome, and single-cell RNA sequencing analyses that severe flu infection results in significant lymphopenia, extracellular matrix remodeling, and transcriptional shutdown in fully immune-competent lung tissues. Lastly, we highlight the prominent role of stromal-immune interactions in the response to severe influenza infection, with stromal cells participating in both cytokine signaling and ECM remodeling. The introduction of both tissue-resident and circulatory immune cells into this lung-on-chip model allows for investigation into the distinct role of each immune cell type in the initiation and progression of influenza and may shed light on potential therapeutic avenues targeting immune dysregulation.Ph.D.Bioengineerin

Scenario Modeling and Analysis for Strategic Decision-Making towards Net-Zero Aviation

Presented at AIAA Scitech 2025With the commercial aviation industry being a major contributor to global emissions, there is a growing sense of urgency among stakeholders to reduce carbon emissions generated by the industry. Industry stakeholders must collaborate and execute decisions at crucial moments in time and realize a scenario to achieve the goal of net-zero emissions by 2050. This work integrates a methodology for generating emissions-reducing scenarios for the aviation industry with an approach to simulate the fleet-level impacts for these scenarios and assess them in terms of pertinent metrics, such as CO2 emissions, airline operating cost ,and flight ticket prices. The simulation approach is integrated into an environment called Sustainable Aviation Visualization Environment (SAVE) to allow users to compare the generated scenarios, analyze them, and aid aviation’s stakeholders in making informed decisions for enabling net-zero by 2050

Neutronics Analyses of a NERVA-derived NTP Reactor Core using Nodal Diffusion Codes

Nuclear Thermal Propulsion (NTP) systems offer twice the specific impulse over modern chemical systems, resulting in a significant travel time reduction necessary for enabling long duration space missions. NTP core neutronics analyses typically rely on Monte Carlo (MC) codes, which are computationally expensive and can be prohibitive to use in transient simulations. To reduce the computational burden of advanced analysis, Light Water Reactors (LWRs) are traditionally modeled using reduced order methods that follow a standard two-step approach in which cross sections are pre-generated for each unique assembly and fed into the deterministic core simulator. Previous to the work performed in this thesis, the extent of reduced order modeling for NTP reactors has been through the use of SPH-corrected deterministic transport. As such, the primary goal of this thesis is to demonstrate a framework for NTP analysis that is better aligned with the conventional methods widely used in the commercial power industry. In the approach investigated in this work, few-group macroscopic cross sections are generated using a single, full core MC simulation and input into a nodal diffusion code, DYN3D, to obtain the multiplication factor and spatial power distribution. Through several 1D and 2D sensitivity analyses, various homogenization techniques are investigated and complied into a single successful sequence that is applied to a realistic NERVA-derived NTP core model. To ensure that the MC solution is reproduced by the diffusion solver, discontinuity factors (DFs) are generated using a Jacobian-Free Newton Krylov (JFNK) iterative scheme and applied as equivalence correction parameters to the full core homogenous solution. The development of the JFNK method is not an originality to this thesis; however, the verification of the DFs it produces is an aspect of this work. Verification of the JFNKgenerated DFs is performed using the semi-analytical NEM-based solution to the intranodal homogeneous flux, yielding NEM-derived DFs that are guaranteed to preserve the heterogenous reaction rates, average flux and net currents. Using multiple SMR test cases, comparisons between the two methods of DF generation show excellent agreement in both the corrected homogenous flux and eigenvalue, as well as the actual values of the surface DF ratios. The last section of results depicts the adapted homogenization sequence applied to a 2D radial slice of the NTP core demonstrating that the homogenous power profile from DYN3D yields near perfect agreement (<0.25% maximum relative error) with the reference power profile only when complemented by the JFNK-generated DFs. Further reduction of the residual error can be achieved when the reflector nodes are homogenized in lumped configuration. A final subsection of results presents a practical approach to modeling NTP cores using a hybrid 2D fuel + 3D reflector homogenization scheme, resulting in a corrected power profile with an average absolute error of less than 0.2%.M.S.Nuclear Engineerin

Resonance-Based Series Compensating Fault Current Limiters

This proposed research explores the functionality of resonant fault current limiting and series compensators. Being a variable impedance from both amplitude and phase point of view enables a series or parallel type resonate circuit to be suitable for various applications from power quality to power system protection. Accordingly, this thesis can be divided into two distinctly different parts. The first deals with an abrupt impedance change used for fault current limiting purposes. The other offers a continuous and moderate change of impedance that can eliminate voltage sag/swell. As a result, the final device will be called a Resonance-Based Series Compensating Fault Current Limiter (resonant series compensated FCL). The unprecedented cost reduction of rooftop PV systems has motivated many homeowners to install rooftop PV systems. However, their intermittent generation can cause problems in the distribution network’s ability to accommodate a high penetration. This legitimate concern inspired this research to develop a controllable resonant device for both series compensation and fault current limiting. The resonant series compensated FCL proposed in this work can increase the hosting capacity, i.e., the accommodation capacity of PV units in each distribution area, and, at the same time, improve the reliability of a distribution system by preventing power interruptions during momentary or transient faults. An efficient resonant series compensated FCL must fulfill the requirements of both series compensator and fault current limiter (FCL), which are somewhat mutually exclusive. As such, this research study starts with the design of a resonant FCL (RFCLs), and then is modified to include series compensation operation during normal conditions.Ph.D.Electrical and Computer Engineerin

Safe Bipedal Locomotion and Navigation in Uncertain Environments

This dissertation addresses the challenge of enabling bipedal robots to navigate and operate autonomously in dynamic and uncertain environments. While industrial robots have been highly successful in structured settings such as factories and warehouses---where robots perform predefined tasks in controlled environments with minimal uncertainty---replicating this success in real-world, dynamic scenarios remains a significant hurdle. Despite recent advancements in humanoid robotics, integrating these robots into unstructured environments is difficult due to the complexity of bipedal locomotion and the need to prioritize safety for both the robot and the surrounding agents. This research focuses on overcoming these obstacles by developing a hierarchical approach that includes high-level task planning, mid-level motion planning, and low-level full-body control, with the overarching goal of ensuring safe navigation in uncertain environments. The two primary sources of uncertainty explored in this thesis are obstacle uncertainty, which involves dynamic obstacles such as autonomous grounded mobile robots and human pedestrians, and terrain uncertainty, which includes partial observability and unknown terrain elevation. In this dissertation, we address the uncertainties in the following context. First, it addresses formal task and motion planning in partially observable environments with dynamic obstacles. In this work, the environment is partially observable when static obstacles occlude the robot’s view of certain regions in the environment; thus, guaranteeing collision avoidance with dynamic obstacles out of the robot's view becomes a challenging problem. To solve this, we develop a formal task planning framework based on Linear Temporal Logic (LTL), which provides guarantees for safe navigation and task completion. It employs a belief abstraction method to handle out-of-view dynamic obstacles. A key feature of this work is the abstraction of reduced-order model safety theorems into symbolic specifications to guarantee that the high-level task planner can be successfully executed by the underlying motion planner. The second work addresses obstacle uncertainty in the context of social navigation. In this task, the bipedal robot is tasked to navigate and reach a specific goal in an open environment containing pedestrians. The bipedal robot is required to avoid collision with pedestrians and navigate in a socially acceptable manner. The pedestrians’ dynamics are not known, thus we introduce the Social Zonotope Network (SZN), a Conditional Variational Auto-encoder (CVAE) architecture for coupled pedestrian future trajectory prediction and ego-agent social path planning both parameterized as zonotopes. We integrate the SZN with a model predictive controller (MPC), where the zonotopes outputted by SZN are encoded as constraints for reachability-based motion planning and collision checking. Our results demonstrate the framework’s effectiveness in producing a socially acceptable path with consistent locomotion velocity and optimality. Finally, we address terrain uncertainty in the context of search and rescue tasks, where we coordinate a heterogeneous team of bipedal and aerial robots. In this project, the terrain elevation is unknown. As the robots navigate the environment, they collect elevation data and update a terrain Gaussian process (GP) model. We present a terrain-aware MPC that solves the optimal paths for the bipedal while maximizing the traversability. We integrate lateral slopes derived from the terrain GP into the cost function of our proposed MPC framework. This method allows for a safer traversal of rough terrains by planning paths with minimum lateral slopes. This dissertation contributes to advancing the field of bipedal robot navigation by providing a comprehensive set of tools and methods to address the challenges posed by dynamic and uncertain environments, offering solutions that ensure safety, optimality, and efficiency for real-world deployment.Ph.D.Mechanical Engineerin

Utilizing Combinatory Adjuvant-Loaded Chitosan-Derived Nanoparticles for a Joint SARS-CoV-2/Influenza Vaccine

As new pathogens arise and spread, potentially on the level of a global pandemic, the need for vaccines that target these pathogens continues to grow. Subunit vaccines are a standard method of protecting the body from these pathogens by administering a relevant protein from the said pathogen. These vaccines require the assistance of adjuvants to produce a sufficiently strong immune response, which can be fine-tuned to further increase efficacy by more accurately representing the pathogen of interest. To ensure that antigen and adjuvant do not simply disperse away from the vaccination site, nano- and microparticles are typically used as carriers. These PLPs are made of less reactive materials, leaving room for immunostimulatory functionalization. Our primary hypothesis is that “chitosan-derived nanoparticles can serve as a flexible and functional adjuvant-carrying system as part of a vaccine.” In this work, we fabricated chitosan nanoparticles and presented them alongside combinatory nucleic acid adjuvants to create a proof-of-concept vaccine for SARS-CoV-2 and H5N1 influenza. First, we assessed various combinations of nucleic acid-derived adjuvants in vitro, monitoring the response in cell cultures containing either GM-CSF or FLT3L to arrive at a formulation we believe would be ideal for this purpose. We have also chosen to characterize cytokine-secreting cells using flow cytometry to assess potential sources of the immunological differences we observed between cultures. Lastly, we administered subunit vaccines in vivo utilizing our combinatory adjuvant-loaded chitosan NP systems to determine efficacy.Ph.D.Bioengineerin

Accelerated Tensor Robust Algorithms for Hyperspectral Imaging and Video Processing

In recent years, the application of tensor-based methods to high-dimensional data has gained considerable attention, particularly for tasks involving denoising, classification, and compression of complex data structures such as hyperspectral images. This thesis presents novel approaches to enhance Tensor Robust Principal Component Analysis (TRPCA), addressing challenges such as computational efficiency, noise removal, and real-time processing. Firstly, the thesis presents a new online robust principal component analysis (RPCA) algorithm that recursively decomposes incoming data into low-rank and sparse components. Unlike traditional approaches that operate on data vectors, this method preserves the multi-dimensional structure of data, such as video frames. It is based on the recently proposed tensor singular value decomposition (T-SVD) and incorporates a convex optimization-based approach for recovering the sparse component and updating the low-rank component using incremental T-SVD. An efficient tensor convolutional extension to the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) is also proposed, significantly speeding up the optimization process. The effectiveness of this online tensor-RPCA is demonstrated through its application in background-foreground separation in video streams, where the foreground is modeled as a sparse signal and the background as a gradually changing low-rank subspace. Extensive experiments on real-world videos showcase the robustness and effectiveness of the proposed algorithm. Secondly, the thesis proposes a randomized blocked algorithm for tensor singular-value thresholding (T-SVT), aimed at reducing the computational demands of TRPCA when applied to noisy hyperspectral images. TRPCA has been successfully employed to reduce noise by employing a minimization involving a tensor nuclear norm and a $\ell_1$-norm to separate the low-rank hyperspectral image from the sparse noise. However, the high computational complexity of TRPCA is primarily due to the implementation of the T-SVT operator, which typically involves performing full tensor singular value decomposition (T-SVD) followed by shrinking the singular values of the frontal slices in the frequency domain. The proposed randomized blocked algorithm incrementally finds the singular values until they fall below the threshold, leveraging compression achieved by the fast Fourier transform (FFT) to accelerate TRPCA significantly. Numerical experiments indicate that this method is much faster than traditional TRPCA approaches while maintaining classification accuracy. Finally, the tensor-robust CUR (TRCUR) method is introduced for hyperspectral data compression and denoising. This method heavily downsamples the input hyperspectral image to form small subtensors and performs TRPCA on these subtensors. The desired hyperspectral image is recovered by combining the low-rank solution of the subtensors using tensor CUR reconstruction. We provide theoretical guarantees showing that the desired low-rank tensor can be exactly recovered using our proposed TRCUR method. Numerical experiments demonstrate that our method is up to 14 times faster than performing TRPCA on the original input data, while maintaining the classification accuracy.Ph.D.Electrical and Computer Engineerin

A Lived Experience: A Journey Through Alcohol Use Disorder

This short documentary was created as a course requirement in HTS 3086 – Sociology of Medicine and Health under the supervision of Dr. Jennifer Singh.Runtime: 05:58 minutesThis documentary explores the illness experience of recovering from Alcohol Use Disorder through Alcoholics Anonymous

Insights into Ozone and PM2.5 Pollution: A Case Study in Spring China and Trend Analysis across the Continental United States

Ground-level Ozone (O3) and fine particulate matters (PM2.5) are two major pollutants, produced through complex photochemical processes involving nitrogen oxides (NOx=NO+NO2), volatile organic compounds (VOCs), and various radicals. Understanding this chemical system is crucial for effective mitigation strategies. This thesis leverages model simulations, and comprehensive ground- and satellite-based observations to gain insights into the underlying photochemistry of O3 and PM2.5 formation. This dissertation begins by exploring three observation-based pathways associated with nitrous acid (HONO) production, highlighting intrinsic relationships between NO2, particulate nitrate (pNO3) and nitric acid (HNO3). Our results reveal varying implications for O3 production. The conversion of HONO from pNO3 enhances regional O3 production, while the conversion of HONO from NO2 can reduce O3 sensitivity to NOx changes in polluted eastern China. Secondly, the comparison between satellite and ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements validates the use of satellite in assessing air pollution patterns, with better agreement observed for NO2 compared to formaldehyde (HCHO). Nonetheless, the TROPOspheric Monitoring Instrument (TROPOMI) HCHO still shows promising improvements compared to two Ozone Monitoring Instrument (OMI) products. Meanwhile, a bias ~30% between two OMI NO2 products are linked to the discrepancies between scattering weight profiles in two retrieval algorithms. In addition, regional effects, seasonal and historical trends of secondary organic carbon (SOC) across the continental United States (CONUS) through 2005-2020 are investigated using organic carbon (OC) and elemental carbon (EC) data from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network. We divided CONUS into six regions according to the correlations of OC concentrations among different sites. The regional mean secondary fractions vary from 22% to 40% and are consistent with co-located values reported by previous studies. With a consistent peak-in-summer seasonal pattern across all six regions, controlling factors for summertime SOC production for each region are investigated through a stepwise multiple linear regression. Furthermore, despite decreasing trends of anthropogenic emissions, as well as that of primary OC, significant decreasing trends in SOC are found only in eastern US in winter, and the southeast (SE) in summer. Accordingly, annual mean SOC fractions have been found to be significantly increasing except for SE. As anthropogenic emissions continue to decrease, SOC will most likely account for increasingly larger fractions of OC and PM2.5.Ph.D.Earth and Atmospheric Science