University of Illinois at Chicago
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Dynamic Analysis of Cable-Stayed Bridges Considering Soil-Structure Interaction under Near-Field Motions
No full textThis research investigates the dynamic response of cable-stayed bridges to near-field ground motions, incorporating the effects of soil-structure interaction (SSI), ground motion directionality, and ground motion characteristics. A 1/60-scale experimental model of a typical cable-stayed bridge, based on the "Twin Bridge" in China, was designed and fabricated for laboratory testing. The experimental program employed seismic records from far-field, non-pulse near-field, and pulse-type near-field events, applied in longitudinal, transverse, and diagonal directions to the bridge model. A key innovation of the experimental setup was a novel box-spring system that simulated various foundation soil stiffness conditions, enabling a detailed investigation of SSI effects. The study was conducted in two phases: a single-axis phase focusing on transverse near-field ground motions and a multi-axis phase examining pulse-type near-field ground motions applied at various incidence angles. A three-dimensional numerical model of the bridge was developed and validated against experimental results, and it was further used to analyze bridge response for additional incidence angles not tested in the laboratory. The results revealed that pulse-type near-field motions significantly amplify bridge demands, particularly when the foundation soil is more flexible. Directionality effects were also prominent, with a significant coupling between longitudinal and transverse displacements observed at angles of 45° and 135°. Additionally, the correlation between Peak Ground Velocity (PGV) and Peak Ground Acceleration (PGA) highlighted the reliability of velocity-based intensity measures as indicators of seismic performance. Predictive equations were developed to estimate dynamic displacements of the bridge deck and towers, incorporating the influence of ground motion characteristics, angle of incidence, soil flexibility, and the bridge’s dynamic properties. This comprehensive study underscores the importance of considering SSI, pulse-type ground motions, and directionality for the seismic design of cable-stayed bridges. The findings provide valuable insights for improving the safety and resilience of such structures in earthquake-prone regions
CPIExtract: A Framework for Collecting and Harmonizing Small Molecule-Protein Interaction Data
No full textThe binding interactions between small molecules (compounds) and proteins are fundamental to cellular functions and essential for understanding biological mechanisms. However, data on compound-protein interactions (CPI) are dispersed across multiple databases, each with unique formats and curation standards, creating significant challenges for researchers seeking to utilize this information. This work presents CPIExtract, a framework designed to systematically extract, filter, and harmonize CPI data from nine major databases into a single, unified format. By overcoming data heterogeneity, CPIExtract greatly expands the accessible collection of CPI data, providing over ten times the annotations available in a single database like DrugBank. The standardized datasets generated by CPIExtract enable researchers to streamline analysis and readily apply the information in disparate biomedical research applications.
Namely, CPIExtract’s data aids the improvement of machine learning models, such as AI-Bind, for drug discovery. Integrating harmonized CPI data into their training improves these models' generalizability and performance, especially in predicting interactions for understudied compounds and proteins. This work highlights CPIExtract’s potential to accelerate the discovery and design of therapeutic agents by supplying robust, comprehensive datasets that bridge the gaps in current CPI databases
Patient Cohort Visual Analytics for Post-Treatment Care
No full textPost-treatment decision-making is a process that depends on longitudinal studies of patient cohorts, in which identifying the risk of adverse treatment outcomes is critical to improving personalized care. This process benefits from visual analytics because it can overcome many of the challenges that arise with complex patient cohort data. Visual analytics can help to interpret treatment progressions and outcomes, and stratify cohorts by risk categories, which are crucial for improving treatment decision-making. However, post-treatment cohort analysis uses large-scale, heterogeneous, temporal, multivariate datasets with associated attributes and missing values. Visualization needs to provide scalable, effective methods for cohort analysis at different levels of detail that can uncover patterns and associations among patient attributes that correspond to negative treatment outcomes. Moreover, post-treatment care planning relies on computational cohort data modeling and, as a result, uses both objective and subjective evidence, namely, the clinician's interpretation of the modeling results. Consequently, cohort modeling and analysis depend on collaborations between clinicians and data modelers. Therefore, visual analytics solutions need to facilitate these collaborations and the interpretation and evaluation of modeling results in a clinical context.
This dissertation explores visual analytics techniques for cohort modeling and analysis and applies these techniques to post-treatment decision-making. This work addresses the challenges identified above by designing, developing, and evaluating four application-specific visualization systems in collaboration with clinical researchers and data modelers. I first identified the design requirements for a family of cohort modeling problems in cancer symptom and digital biomarker research. Next, I design several systems that integrate unsupervised modeling for the computational back-end and data visualization for the front-end. I propose novel, custom visual encodings for multivariate temporal cohorts that enable iterative risk assessment across cohort stratifications. A first system, OpenDBM, uses visual analytics for behavioral risk assessment in digital biomarker research, using cohorts with hundreds of modeled attributes, and it was designed for the open-source community. This work proposes a novel encoding that aggregates multivariate, spatial, and non-spatial temporal attributes on anatomical locations to explain behavioral biomarkers. A second system, THALIS, shifts the focus to clinician-modeler collaborations in head and neck cancer cohort modeling, and to a multi-stage patient monitoring process, namely, during and post-treatment. This system uses scalable visual encodings to interpret attribute associations and introduces a new encoding for evaluating patient outcomes in multivariate, multi-stage time series. A third system, Roses, builds on the previous work, using custom visualizations for the interpretation and evaluation of outcome risk predictions, this time accommodating configurable analytical workflows for clinicians and modelers. The system introduces a visual encoding to summarize multi-stage networks, with temporal nodes, which helps to evaluate patterns and associations in modeled outcome risk components. A fourth system, L-VISP, explores visual analytics for understanding and assessing black-box models in cohort risk prediction, with an emphasis on the design requirements for data modeler activities. To support model evaluation, the system visualizes results for machine-derived (cluster) or user-specified cohort stratifications and introduces custom encodings for weighted associations in multivariate attributes. Together, these systems contribute to data visualization and modeling solutions for the challenges that data modelers and clinicians face during collaborations
Impact of School Disciplinary Policies on Students with Emotional and Behavioral Disabilities
No full textThis study explores the beliefs and practices of Illinois middle school administrators regarding the discipline of students identified with Emotional and Behavioral Disorders (EBD). To gain insight into current disciplinary trends and their potential implications, a revised version of the Disciplinary Practices Survey (Green, 2016) was administered to middle school principals (grades 6 to 8) across the state. The survey examined administrators’ attitudes toward discipline, the frequency of specific strategies used, and how these beliefs and practices may vary based on demographic and school characteristics. Participation in the survey was limited to administrators in schools that had at least one student with EBD enrolled. Data from 68 surveys were analyzed using descriptive statistics and chi-square tests of independence. Additional statistical tests, such as Fisher’s Exact Test and Kendall’s tau-b, were used to assess reliability between principal demographics and their responses. Several themes emerged from this study. The findings show a shift in beliefs regarding discipline, changing perceptions about diverse student variables, the necessity for individualized support for students with EBD and other diverse learners, the role of administrators in behavioral education, and the importance of prioritizing unbiased, proactive discipline practices. However, restrictions in data, such as a small sample size and limited diversity in participants and school locations, particularly in urban areas, restrict the applicability of these findings. Nonetheless, differences across administrator demographics and school contexts indicate persistent uncertainty about how to implement equity-driven practices consistently, pointing to the need for professional development and systemic reform
3D Chromatin Structure and Gene Regulation: Integrated Genome Structure, Transcriptomics and Epigenetics
No full textThe three-dimensional (3D) organization of the genome is essential for regulating gene expression, replication, and stability. Chromatin folding mediates enhancer–promoter communication, but bulk Hi-C data obscure single-cell variability, while sparse single-cell Hi-C limits structural resolution. This dissertation develops computational approaches to reconstruct single-cell chromatin conformations, integrate them with functional genomics, and make them broadly accessible. I present a framework that combines Hi-C with expression quantitative trait loci (eQTL) data to identify statistically significant contacts and reconstruct ensembles of single-cell structures. These analyses reveal that spatial proximity between eQTL–eGene pairs enhances regulatory effects and that many-body interactions underpin tissue-specific expression. To enable systematic exploration, I created ChromPolymerDB, a high-resolution public database of ~10⁸ reconstructed chromatin structures at 5 kb resolution across 50 human cell types. With interactive visualization and multi-omics integration, ChromPolymerDB supports investigations of enhancer–promoter contacts, structural rewiring, and disease-associated remodeling. Finally, I demonstrate that FoldRec promoter–enhancer interactions strongly correlate with cell-type-specific expression and that chromatin heterogeneity gives rise to distinct structural subpopulations with different regulatory potential. Together, these advances provide methodological innovations, community resources, and biological insights into how genome architecture encodes regulatory logic at the single-cell level, laying foundations for studies in development, disease, and precision medicine
Decoding the Sequence-Dependent Properties of Intrinsically Disordered Proteins
No full textA core principle of structural biology is the sequence-structure-function paradigm, whereby a proteins sequence codes for a specific structure that complements its binding partner, thus imparting functionality. Intrinsically disordered proteins (IDPs) lack a well-defined structure yet manage to interact with a variety of partners. At a basic level we can use amino-acid sequences to predict disordered content, but the sequence profile of an IDP can tell us so much more. By leveraging all-atom MD simulations, this dissertation aims to dissect the sequence-dependent properties of IDPs to develop governing rules that can be applied to the wider disordered proteome.
We first look at sequence-dependent dynamics. Using long timescale MD simulations on a set of diverse IDPs, we show that fast motions can be attributed to glycine residues, while transient secondary structure and local or long-range intramolecular interactions facilitate slow dynamics. We noted that slowed regions had significant overlap with drug-interacting residues in IDPs. With that in mind, we reparametrized our IDP-dynamic prediction method (SeqDYN) with chemical shift perturbation data to develop DIRseq, a prediction method for identifying IDP drug binding regions.
IDPs are known to readily undergo homogenous liquid-liquid phase separation (LLPS) under certain conditions, however variations in salt concentration have been shown to have disparate effects and can even enhance phase separation beyond 1 M NaCl. Through the lens of A1-LCD we simulate an 8-chain model in low- to high-NaCl concentrations and identify direct and indirect roles salt plays in IDP-LLPS. We use this to then classify a group of 26 IDPs into four modes of sequence-dependent salt-LLPS effects.
Membranes also serve as key IDP binding partners, as they can impart both function and dysfunction. We first investigated tau K19, which forms amphipathic helices in acidic membranes. The nature of the tau-membrane interaction mimics that of the tau-microtubule interaction as seen in cryo-EM studies, and we propose a method in which membrane mediates tau transfer to microtubules. Next, we use enhanced sampling techniques to determine a membrane-assisted fibril forming pathway for the IDP α-synuclein, which gives us insight to Parkinson’s disease specific fibril formation
Multiphase Flow Dynamics in Confined Domains—A DNS Study of Bubbly, Thermal and Riblet-Induced Turbulence
No full textThis thesis presents a comprehensive investigation of multiphase and wall-bounded turbulent flows using high-fidelity direct numerical simulations (DNS), with an emphasis on the fundamental mechanisms governing heat transfer, turbulence modulation, and instability-driven transition. By systematically studying three canonical configurations—liquid–liquid emulsions in Rayleigh–Bénard convection, buoyancy-driven bubbly flows in vertical channels, and channel flows over riblet-structured walls—this work advances the physical understanding of multi-physics interactions across a broad spectrum of thermofluid systems relevant to energy, chemical, and aerospace applications.
In the first part, DNS of multiphase Rayleigh–Bénard convection is performed using the VOF-MTHINC method to explore the coupled effects of dispersed-phase volume fraction, viscosity ratio, and thermal diffusivity ratio on turbulent convection. The study reveals that immiscible liquid–liquid emulsions profoundly alter heat transport and energy transfer across scales. At fixed Rayleigh and Prandtl numbers, increasing the dispersed-phase volume fraction enhances heat transfer by up to 10\% due to droplet-induced energy transfer to smaller turbulent scales, even though global turbulence intensity is reduced. Varying viscosity ratios further amplifies mixing: at a volume fraction of 20\% and a viscosity ratio of 10, heat transport rises by ~25\% owing to intensified turbulence in the less viscous carrier phase. Conversely, introducing a dispersed phase with higher thermal diffusivity suppresses convective transport, reducing the Nusselt number by as much as 50\% through accelerated conduction and the depletion of near-wall droplets. Analysis of droplet size distributions identifies distinct scaling regimes dominated by coalescence and breakup, confirming the multiscale nature of droplet dynamics in thermal convection. These results establish how droplet rheology and transport properties dictate the interplay between turbulence and thermal efficiency in emulsions.
The second part investigates buoyancy-driven bubbly flows in a vertical channel, where interface-resolved DNS with a conservative diffuse-interface (CDI) method provides full spatiotemporal resolution of bubble–turbulence interactions. By systematically varying the Galilei (390–1100) and Eötvös (0.85–8.5) numbers at a constant void fraction of 2.7\%, the study isolates the effects of inertia and deformability on pseudo-turbulence. The results demonstrate that bubble deformability critically dictates spatial distribution and turbulent regimes. At low Eötvös numbers, bubbles remain near the walls, producing stratified layers with suppressed core mixing. At higher Eötvös numbers, bubbles deform, break up, and redistribute toward the channel center, where they strongly enhance turbulence and velocity fluctuations. Increasing Galilei number further intensifies rise velocities, vorticity generation, and wall-bounded turbulence, leading to stronger shear-layer instabilities and near-wall fluctuations. Energy budget analyses confirm that pseudo-turbulence emerges from the coupling of interfacial deformation, inertial forces, and vorticity generation. These findings identify distinct dynamical regimes of bubble-induced turbulence, bridging experimental observations and providing predictive insights for optimizing bubbly flows in chemical reactors, nuclear thermal-hydraulics, and environmental systems.
The third part examines single-phase channel flows modified by large riblet structures, focusing on laminar-to-turbulent transition. Using an immersed boundary method on Cartesian grids, DNS resolves riblet-induced near-wall instabilities in both two- and three-dimensional domains. The parametric study considers height blockage ratio (HBR) and length blockage ratio (LBR) as key geometric controls, systematically mapping transition thresholds. The results reveal that increasing riblet height or decreasing riblet spacing lowers the critical Reynolds number, with nonlinear destabilization observed for . Dimensionality strongly influences stability: three-dimensional domains consistently transition earlier than two-dimensional counterparts, underscoring the role of spanwise instabilities, secondary flows, and vortex lodging. Flow diagnostics highlight Kelvin–Helmholtz instabilities over riblet crests as the dominant transition mechanism, with riblet-induced adverse pressure gradients driving shear-layer roll-up and vortex shedding. Pressure distributions exhibit strong stagnation–suction asymmetry scaling with riblet geometry. These results provide DNS-based guidelines for tailoring riblet dimensions to either suppress transition (for drag reduction) or promote early turbulence (for mixing enhancement), with direct implications for aerospace, thermal management, and microfluidics.
Taken together, this thesis establishes a unified framework for understanding turbulence modulation across three distinct yet interconnected domains: emulsions modulating thermal convection, bubbles driving pseudo-turbulence in buoyancy-driven flows, and riblets dictating transition dynamics in wall-bounded shear flows. By integrating high-resolution DNS, advanced interface-capturing techniques, and systematic parameter studies, the work identifies multiscale mechanisms—ranging from droplet coalescence and bubble deformability to riblet-induced shear instabilities—that control momentum and heat transfer in complex flows. Beyond advancing fundamental fluid dynamics, the findings provide actionable physical insights for the design of multiphase reactors, thermal systems, and surface-engineered flow-control technologies
Modeling Genome Organization in Eukaryotic and Prokaryotic Cells Using Folding Determinant Interactions
No full textThe three-dimensional organization of genomes is fundamental to gene regulation, DNA replication, and cell identity. A key challenge, however, lies in disentangling stochastic folding driven by polymer physics from specific interactions mediated by nuclear landmarks and regulatory factors. In this thesis, I develop polymer-based chain-growth Monte Carlo frameworks to investigate genome folding in both mammalian and bacterial systems. For mammalian cells, I model how lamina-associated domains (LADs) and specific fold interactions (SFIs) contribute to nuclear architecture. By contrasting random polymer ensembles with models incorporating sparse specific interactions, I demonstrate that LAD–lamina tethering and SFIs act in a complementary manner to reproduce Hi-C features, compartmentalization, and insulation scores, while also accounting for the structural heterogeneity observed in single-cell imaging. For Escherichia coli, I extend this approach to its circular chromosome by integrating Hi-C data. The analysis reveals how Ori/Ter asymmetry, macrodomains, and supercoiling-sensitive regions influence chromosome compaction and long-range contacts. Comparisons with experimental data confirm that a small subset of specific interactions, beyond random polymer effects, is sufficient to capture the observed organization at both the population and single-cell levels. Together, these studies establish a unified framework for dissecting genome folding across organisms. By isolating SFIs from background polymer constraints, this work demonstrates how nuclear landmarks and topological features synergize with physical principles to generate robust yet heterogeneous genome structures
Impact of Hydroxyurea on Neurocognitive Function and Quality of Life in Children with Sickle Cell Anemia
No full textBackground: Beginning in infancy, children with sickle cell anemia (SCA) experience severe anemia, acute and chronic pain, fatigue, and progressive multi-organ damage including a substantially increased risk of stroke during childhood. Neurocognitive deficits and poor health-related quality of life (HRQL) are also well recognized. Early screening for stroke risk with Transcranial Doppler ultrasound (TCD) and initiation of monthly red blood cell transfusions in children with SCA and abnormal TCD velocities has been proven to substantially decrease the risk of overt stroke; however, chronic transfusion is associated with a heavy patient burden and increased morbidity that can reduce access and make its long-term use untenable. The NIH funded, phase-III, multi-center TCD with Transfusion Changing to Hydroxyurea (TWiTCH) randomized controlled trial established that after a year of monthly blood transfusion, children with abnormal TCD velocities can safely transition to oral hydroxyurea for primary stroke prevention. However, the effect of this transition on neurocognitive and HRQL outcomes was not previously known. This dissertation research utilizing data from the TWiTCH trial provides the first longitudinal comparison of the effects of monthly transfusion and oral hydroxyurea therapy on neurocognitive and HRQL outcomes including fatigue in children with SCA.
Results: After a mean 23-months of follow-up, compared to children randomized to continue monthly blood transfusions, the children who transitioned to hydroxyurea did not experience any significant decline in neurocognitive function and in some domains, specifically processing speed and attention, demonstrated significant improvement compared to those who continued transfusions. Additionally, children who transitioned to hydroxyurea reported significantly greater treatment associated quality of life, and improvement in general fatigue scores compared to the children who continued monthly transfusions, with no significant worsening of general or sickle cell disease specific HRQL measures.
Discussion: These findings provide additional evidence supporting implementation of clinical guidelines regarding transition from blood transfusion to hydroxyurea for primary stroke prevention, which has the potential to increase access to stroke prevention measures for children with SCA in the U.S. and globally. This analysis also adds to the growing body of evidence supporting hydroxyurea therapy for the prevention of neurocognitive sequelae in children with SCA, and for the use of hydroxyurea to optimize HRQL and reduce fatigue. Increased adoption and utilization of hydroxyurea has the potential to reduce both the individual and public health burden of sickle cell disease
From Alerts to Defense: Towards Building Adaptive Frameworks for Detection, Correlation, and Response
No full textSecurity Operation Centers work under sustained alert load and must investigate a significant
number of alerts daily, and must do so with confidence. This dissertation presents three
frameworks that operate on commodity audit telemetry and help analysts correlate activity,
investigate alerts with publicly available intelligence in the loop, and take guarded endpoint
actions.
We first present Ostinato, a cross-host correlation system that turns Windows and Linux
audit feeds into a typed provenance graph and aligns similar subgraphs across machines. The
alignment preserves causal roles, so process to file writes and process to socket opens retain
meaning even when names and exact tools differ. The result is a compact scenario graph and
timeline that shows scope, pivots, and ordering, and remains stable when clocks drift or logs
arrive out of order.
We then introduce Citar, a tool that brings cyber threat intelligence into day-to-day alert
investigations. Citar maps alerts to potential apt groups, and tries to find other artifacts
related to an attack that could be present in the host. It then correlates matched artifacts to
a seed alert through a point-to-point traversal, so the outcome is a readable scenario rather
than scattered hits. When paired with existing detection mechanisms, Citar increases coverage
by up to 57% in our study of public datasets and enterprise-style simulations, while reducing
investigation times.
Finally, we present Spade, a response framework that assists containment on endpoints while
keeping humans in control. Spade encodes a typed view of the host state and lets a learned
agent propose an action to mitigate or disrupt attacker actions. Every proposal passes through
a safety gate that masks non-applicable or unsafe choices and exposes the mask to the analyst.
Training uses replayed traces with the same encoder used at deployment, so observations remain
stable. Agents escalate from monitor to higher impact actions only when corroborating signals
accumulate, which reduces attacker progress while preserving availability.
All three tools consume the same host-level audit level logs that are widely utilized and then
normalize them into provenance graphs with different enrichment techniques. All three prefer
small, faithful explanations over large graphs or opaque scores and keep analysts in the loop
through narratives they can review, intelligence-driven checks they can run, and guarded actions
they can control. We evaluate the tools on public multi-host traces, operationally realistic
replays, and our custom-generated apt dataset that follows community technique mappings.
Across these settings, Ostinato reconstructs concise multi-host narratives from noisy input,
Citar improves investigative efficiency and coverage, and Spade proposes safe actions that
reduce time to containment while maintaining service continuity
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