Thesis Defense: Qi Wang, “System Dynamics and Machine Learning Techniques for Studying Resilience in Public Health”





Doctoral Candidate


Tuesday, December 22

11:00 AM

Contact Elena Shichkova for access to this presentation.

“System Dynamics and Machine Learning Techniques for Studying Resilience in Public Health”

Systems Dynamics (SD) and Machine Learning (ML) are analytical methods that are becoming more broadly applied to studies in public health. This dissertation focuses on public health aspects of resilience, with an emphasis on distressed populations. We analyze a broad array of topics, including community resilience to natural disasters, suicide prevention interventions, cognitive resilience to aging effects, and interventions to mitigate the impacts of community violence on children.  We also include SD and ML studies in early childhood educational investment and a Bayesian analysis of wiki-surveys.

We begin with the development and calibration of a system dynamics model of community resilience, COPEWELL. We propose a stepwise approach to developing a measure set that combines domain expertise with statistical analyses. For our second study, we use SD to analyze processes of suicide prevention for refugees living in migration camps in Thailand to understand their causes, consequences, and mechanisms. An SD model is used to show the different impacts of proposed interventions on rates of suicide ideation and attempts. In our third study, we explore a novel application of supervised ML to study time-varying processes of cognitive reserve and resilience in an aging population in Baltimore City. For our fourth study, we worked with researchers at the United Stated Agency for International Development (USAID) and World Vision International to build an SD model of community violence in gang-controlled urban neighborhoods in Honduras and El Salvador. The long-term goal in this collaborative effort is to design interventions that would promote child protection against violence. An interactive webpage was developed for stakeholders to visualize the model and explore influential factors. In our fifth study, we use reinforcement learning to model parental investment in early childhood development. We integrate this model with an economics-based model of children human capital formation. We use Bayesian updating to assess the future state of the stock of human capital. In our sixth study, we construct and calibrate an SD model to examine the influence of community factors on suicide risk. This study lays the foundation for a larger project to examine the impact on community-based support programs such as home visitation programs on the rates of suicidal behaviors. In the final application of this thesis, we demonstrate a scoring and ranking approach for analyzing the results of wiki-survey voting using a maximum a posteriori probability (MAP) estimate. Two case studies are examined to test the accuracy of the estimating procedure.

This thesis demonstrates the broad applicability of system dynamics and machine learning methods on high-priority public health research topics. Mathematical details and software code are provided in the appendix for researchers interested in this growing direction of research.


Thesis Defense: Shravan Kumar Reddy Kotha, “Parametrically Homogenized Constitutive Models for Titanium Alloys ”





Doctoral Candidate


Monday, December 14, 2020

3 PM

Contact Elena Shichkova for access to this presentation.

“Parametrically Homogenized Constitutive Models for Titanium Alloys”

            Structural analysis of heterogeneous materials using phenomenological constitutive models, is often faced with inaccuracies stemming from the lack of connection with the material microstructure and underlying physics. Pure micromechanical analysis, on the other hand, is computationally prohibitive on account of the large degrees of freedom needed to represent the entire structure. Hierarchical multiscale models based on computational homogenization, have been proposed to determine the homogenized material response for heterogeneous materials that can be used in component-scale analysis. However, for nonlinear problems involving history-dependent constitutive relations, many multiscale methods incur prohibitive computational costs from solving the micromechanical problem for every macroscopic point in the computational domain.

To overcome these shortcomings, this thesis develops a computationally efficient, Parametrically Homogenized Constitutive Model (PHCM) for dual-phase α/β Titanium alloys such as Ti6242S. PHCMs incorporate characteristic microstructural features as well as underlying physical mechanisms of deformation in macro-scale constitutive models. A size, rate and temperature dependent Crystal Plasticity Finite Element Model (CPFE) has been used to characterize the micro-mechanisms of deformation in dual-phase Titanium alloys. Statistically Equivalent Representative Volume Elements (SERVEs) are constructed to study the influence of different microstructural morphological and crystallographic distributions such as crystallographic orientation distribution, misorientation distribution, grain size distribution etc. on homogenized response. A detailed sensitivity analysis is performed to identify important microstructural distributions that govern the macroscopic material response and Representative Aggregated Microstructural Parameters (RAMPs) that quantify these distributions are defined. The constitutive equations in PHCM are then chosen to represent different homogenized mechanical behaviors observed from CPFE analysis such as elasto-plastic anisotropy, tension-compression asymmetry, grain size, strain rate and temperature dependency etc.. A database of SERVEs with different morphology and crystallographic distributions is created and CPFE simulations are performed under a variety of loading cases. The constitutive parameters of PHCM equations are calibrated to match its response with that obtained from CPFE analysis. These constitutive parameters are related to corresponding RAMPs using functional forms obtained from machine learning. A finite deformation formulation of PHCM constitutive equations is implemented in Abaqus as a user material subroutine. Using PHCM, microstructure-sensitive structural simulations of a representative ortho-grid panel are performed to demonstrate the microstructural dependency of structural response and the computational efficiency of PHCM. Moreover, the PHCM based predictions are compared with those obtained from isotropic elasticity and J2 plasticity models to demonstrate their deficiency.

Finally, the PHCM model for dual-phase Titanium alloys is extended to include the effects of damage. An anisotropic, coupled plasticity-damage model is formulated in which plasticity and damage are coupled via a Helmholtz free energy density function. Based on homogenized stress-strain response and crack propagation behaviors observed from coupled crystal plasticity-phase field simulations, an anisotropic damage surface and damage evolution equations are proposed. The proposed plasticity-damage model accounts for tension-compression asymmetry and strain rate dependency of damage and its thermodynamic consistency is established. Numerical results that demonstrate different aspects of the coupled plasticity-damage model are discussed at the end.

JOB OPPORTUNITY: Full-time Lecturer in the Area of Systems Engineering

The Whiting School of Engineering at Johns Hopkins University seeks applicants for a full-time teaching position (Lecturer) in the area of systems engineering to be part of the teaching faculty in the department of Civil and Systems Engineering (CaSE). This position will serve a key role in the department’s new program in Systems Engineering and the growing Center for Systems Science and Engineering (CSSE). More experienced candidates may be considered for a Senior Lecturer position.

This is a career-oriented, renewable appointment that is responsible for the development and delivery of undergraduate and graduate courses to those majoring in Civil and/or Systems Engineering. Some potential areas for instruction could include: data sciences, mathematical modeling, risk analysis, network modeling, optimization, or any course at the intersection of systems engineering and civil infrastructure. The Whiting School of Engineering has a well-established non-tenure track career path for full-time teaching faculty culminating in the rank of Teaching Professor.

The successful candidate will be expected to help articulate the role of systems engineering in undergraduate and graduate education, particularly in the context of a department that broadly covers the areas of systems, structures, and mechanics of materials. See the department website for more information ( Teaching faculty members are encouraged to engage in departmental and university service and may have advising responsibilities. In particular, this position is likely to include some administrative responsibilities related to oversight of our full-time M.S. degree program in Systems Engineering and to coordination of the ABET accreditation process for Systems Engineering. Such activities will offset some of the teaching responsibilities of the position.


Applicants for the position must have a Ph.D. degree in Systems Engineering or a closely related field. Related professional experience is considered a plus. Successful applicants must also have demonstrated excellence in, and commitment to, teaching, and have excellent communication skills.

Application Instructions

Applicants should apply online at Please include a cover letter and a CV and, optionally, a brief teaching portfolio including (sample) course evaluations from any classes taught by the applicant. While candidates who complete their applications by January 15, 2021 will receive full consideration, the Department will consider exceptional applicants at any time.

The Johns Hopkins University is an equal opportunity and affirmative action employer. All qualified applicants will receive consideration without regard to any characteristic protected by law.

Center for Systems Science and Engineering (CSSE) Seeks PhD Candidates on Grand Societal Challenges Using Systems Thinking and Methods

The Department of Civil and Systems Engineering (CaSE) has recently launched a PhD Program in Systems Engineering. The Center for Systems Science and Engineering  (CSSE), housed in CaSE, has already had major impact with the COVID tracking dashboard used around the world. We are seeking highly motivated applicants (either master’s or exceptional undergraduate students) interested in pursuing doctoral research on grand societal challenges using systems thinking and methods.

Our faculty’s cutting-edge research addresses a range of domains including healthcare, public health, transportation, space, and energy. We are also closely tied to other major centers at Johns Hopkins including the Malone Center for Engineering in Healthcare, the Institute for Assured Autonomy, the Applied Physics Laboratory, and the Bloomberg School of Public Health. Our multidisciplinary program allows the students to explore topics from different fields including applied math, computer science, operations research, operations management, environmental sciences, and data sciences.

Prospective students should review our website, consult the academic requirements and the application process. Contact either Professors Lauren Gardner ([email protected]), Kimia Ghobadi ([email protected]), Gregory Falco ([email protected]), or Tak Igusa ([email protected]) for more information.


Autonomy OWL Lab Seeks PhD Candidates with Interests in Space Systems, Smart Cities, Space Habitation, Critical Infrastructure, or Space Systems Cybersecurity

Interested in doing a PhD on Space SystemsSmart Cities, Space Habitation, Critical Infrastructure, or Space Systems Cybersecurity? Consider applying for a PhD in the Department of Civil and Systems Engineering and joining the Autonomy OWL lab!

All OWL PhD students will have an office at our new lab space at the Institute for Assured Autonomy and benefit from exposure to both the Whiting School of Engineering and the Applied Physics Laboratory. They will also be fully funded for the duration of their PhD studies.

Our lab is highly interactive and has close ties to industry, the startup ecosystem and government sponsors making for a vibrant environment to conduct field-defining research.

We encourage applicants that have two or more of the following:

  • Prior industry experience;
  • Some research background, exposure/interest;
  • Interest in using AI or investigating questions of AI assurance/trust/security/safety;
  • Ambitions of starting companies or entrepreneurial tendencies;
  • A really hard systems problem that needs solving.

A technical educational background is strongly encouraged.

Contact [email protected] for more information.

Announcing the Fall 2020 CaSE Seminar Series Speakers

Below is a listing for the speakers being showcased during the Fall 2020 Civil and Systems Engineering Graduate Seminar Series. All civil engineering graduate seminars are FREE and open to the public. Attendance is required for all enrolled Civil and Systems Engineering graduate students. For information on individual seminars, please refer to the Events Calendar.

All seminars will be held Thursdays from 12-1:25 PM EDT and hosted online until further notice. Please contact Carla Diaz for connection details.

October 1 – Marcelo Giguale, Director World Bank

October 8 – Jean -Francois Molinari, Director, Computational Solid Mechanics Laboratory, EPFL

October 15 – Robert Bachus, Senior Principal, Geosyntec Consultants

October 29 – Steve WaiChing Sun, Associate Professor, Dept. of Civil Engineering and Engineering Mechanics, Columbia University

November 5 – Amy M. Cohn, Arthur F. Thurnau Professor, Dept. of Industrial & Operations Engineering, University of Michigan

November 12 – Lesley Sneed, Professor and Stirrat Faculty Scholar, Dept. of Civil, Architectural and Environmental Engineering, Missouri S&T University

November 19 – Karen Smilowitz, James N. and Margie M. Krebs Professor in Industrial Engineering and Management Sciences, Northwestern University

December 3 – Luke Bisby, Chair of Fire and Structures, Dept. of Civil and Environmental Engineering, University of Edinburgh

Thesis Defense: Aakash Bangalore Satish, “Multimodel Bayesian estimation of total uncertainty and its application to modeling the yield strength of structural aluminum at elevated temperature”





Doctoral Candidate


Monday, August 24, 2020

1 PM

Contact Elena Shichkova for access to this presentation.

“Multimodel Bayesian estimation of total uncertainty and its application to modeling the yield strength of structural aluminum at elevated temperature”

In the multimodel approach, inference is based on an ensemble of model classes.  Uncertainties in the model  probabilities and parameter values are estimated from data using Bayesian inference. While the epistemic uncertainty in the estimates of parameter values and model form are accounted for in literature on Bayesian multimodel inference, the epistemic uncertainty in the estimates of model probabilities is often ignored. When working with small data sets, however, there might be large epistemic uncertainty in the model probabilities.

This thesis presents a Bayesian multimodel approach to quantify the total uncertainty in random variables quantified from limited data, which often serve as inputs to computational models of engineering systems. The novelty of this approach is that it builds on existing Bayesian multimodel methods by integrating into them the treatment of epistemic uncertainty in model probabilities. In this approach, model probabilities are treated as random variables and their posterior distribution is obtained by Bayesian inference. The mode of this posterior distribution quantifies the epistemic uncertainty in the model form, and the marginals quantify the epistemic uncertainty in the model probabilities.

The quantified uncertainty is propagated through computational models of engineering systems using samples drawn from the posterior predictive distribution, which incorporates the aleatory uncertainty as well as the epistemic uncertainty in the model probabilities, model form, and parameters of the set of input model classes. Then, importance sampling is employed to explicitly quantify the uncertainty in the outputs due to each kind of input uncertainty, by post-processing the outputs.

The proposed approach was applied to the estimation of total uncertainty in the yield strength of aluminum 6061-T6 at elevated temperatures, using data derived from an extensive testing program. A total of 100 steady-state uniaxial tension tests were conducted at six temperatures using material sourced off-the-shelf from nine different batches to study the variability in the stress-strain response of aluminum 6061-T6. Multimodel inference is employed using this data to estimate the design value and reduction factor for tensile yield strength incorporating epistemic uncertainty in the model probability, model form, and parameters used to model the variability in the yield strength.

Thesis Defense: Sirui Bi, “On the Processing, Microstructure and Strength of Brittle Foams”





Doctoral Candidate


Tuesday, August 25, 2020

3 PM

Contact Elena Shichkova for access to this presentation.

“On the Processing, Microstructure and Strength of Brittle Foams”

This dissertation focuses on the nonlinear mechanics of open-cell foams and in particular the connection between their cellular microstructure and their macroscopic strength. We first examine the quasi-brittle failure of Reticulated Vitreous Carbon (RVC) foams under compression. The carbon foam microstructure is analyzed using microcomputed tomography. It is shown to follow closely the polyhedral structure of the precursor polymer foam, that is pyrolyzed to produce its carbon counterpart. Scanning single foam-ligaments reveals that their cross-sectional area is a hypocycloid with a non-uniform distribution across the ligament length. X-ray tomography also shows several processing-induced defects in the form of anisotropy and remaining closed-cell faces. Specimens of different geometries and dimensions are crushed between rigid surfaces in order to examine the effect of load distribution, specimen size, relative density and cell-size on the resulting response and the associated crushing strength of the foam. In-situ testing and image analysis is utilized to observe the failure mechanism and associate it with the recorded force-displacement response.

In the second part of this thesis, additive manufacturing is employed to examine the strength of brittle foams with controlled microstructural characteristics. Tessellation-based topologies are used to generate realistic microstructures of open-cell foams that are subsequently 3D-printed by stereolithography. The stress-strain curve and fracture strength of the base photopolymer are measured using tensile tests on small dog-bone specimens with the dimensions of foam ligaments. Synthesized foams are scanned by microcomputed tomography and manufacturing-induced variations are quantified through image analysis. Characterization shows that there is a small amount of volume shrinkage of the material caused by the additive manufacturing process, but all other microstructural features are accurately reproduced. We then perform a series of experiments to measure the compressive response and strength of the 3D-printed foams and connect it to load-transferring conditions, the strength of the base solid material and the foam relative density.

Finally, we examine the compressive response of open-cell aluminum foams under high temperatures. Foam specimens with different cell-sizes are compressed within an environmental chamber under a range of temperatures from 20 °C to 300 °C. The localization and evolution of collapse are monitored and analyzed using Digital Image Correlation (DIC) and the overall force-displacement response is measured. The results indicate that high temperatures significantly affect all mechanical properties of aluminum foams. Both the limit and plateau stresses were found to decrease linearly with temperature. More importantly, their drop is not proportional to the corresponding one in the base material’s yielding stress. The densification strain, that is a measure of the plateau extension, also follows a linear trend albeit in an increasing manner. This increase is attributed to changes of the collapse mechanism, which at high temperatures involves localization in different zones within the foam, as well as increased compaction at the cell-level caused by the base material softening. Finally, we measure the reduction in the strain energy absorption capacity of the foam caused by high temperatures.

Thesis Defense: Marietta Squire, “Mathematical Modeling and the Prevention of Healthcare-Associated Infections”





Doctoral Candidate

Marietta Squire

Monday, July 27, 2020

11 AM

Contact Elena Shichkova for access to this presentation.

“Mathematical Modeling and the Prevention of Healthcare-Associated Infections”

Hospital construction, renewal, and sustainment involve complex processes as a result of rapid change in healthcare technology and information systems technology, changing practices of care, and the ongoing presence of varied bacterial and viral threats. Quality of care is in many ways predicated upon the quality and safety of the environment upon which the patient receives care. Hospitals operate twenty-four hours a day at staff levels that are often not adequate for the number of patients being seen. This further emphasizes the importance of ensuring that a hospital is designed and operated in the safest manner possible, for both patients and staff.

This thesis develops novel methods and toolsets that can be implemented during the hospital design phase as well as during hospital operations, to help ensure patient and staff safety are paramount. These methods quantify the clinical impacts of infection control as well as associated costs and savings, prior to intervention implementation. The Hospital Energy model addresses the management and sustainment of critical care when operating within a distressed power grid environment. These quantitative tools provide an objective assessment of how to best allocate resources and energy within fiscal constraints. Both the Infection De-escalation model and Hospital Energy model are then adapted and expanded to address SARS-CoV-2 transmission in hospitals. The excess energy and economic cost of implementing both ultraviolet light decontamination and negative pressure treatment rooms in hospitals are evaluated through the integration of these two models.

Thesis Defense: Xiaohui Tu, “Developing Image-based Crystal Plasticity Models for Deformation and Crack Propagation in Polycrystalline 7000-series Aluminum Alloys”





Doctoral Candidate

Xiaohui Tu

Thursday, July 23, 2020

11 AM

Contact Elena Shichkova for access to this presentation.

Developing Image-based Crystal Plasticity Models for Deformation and Crack Propagation in Polycrystalline 7000-series Aluminum Alloys

This dissertation develops various models for image-based crystal plasticity FEM modeling of deformation and fracture mechanisms of 7000 series Aluminum alloys. The work begins with the development of preprocessors for micromechanical analysis of polycrystalline-polyphase microstructures of Al alloys, such as Al7075-T651. Starting from input data in the form of electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM) maps of orthogonal surfaces of experimental specimens, a robust methodology is created for generating 3D statistically equivalent virtual microstructures (3D-SEVMs) by a 3D stereological projection of 2D statistical distribution and correlation functions using a genetic algorithm (GA)-based numerical algorithm. Validation of the SEVM reconstruction process is conducted by comparing the SEVM statistics with morphological and crystallographic distributions of grains and precipitates from the experiments. Microstructure-based statistically equivalent representative volume element (M-SERVE) that corresponds to the minimum sized SERVE for convergence of morphological or crystallographic distributions are established using the Kolmogorov–Smirnov (KS) tests. Property-based statistically equivalent RVE (P-SERVE), defined as the smallest SERVE for predicting response functions (both effective and local), is estimated by conducting crystal plasticity finite-element simulations. Convergence plots of material response functions are used to assess the P-SERVE. These convergence analyses reveal that the controlling factor for the SERVE size is local extreme values of stress and strain, as well as the two-point correlation function of precipitates and precipitate-grain correlations.

A coupled crystal plasticity-phase field (CP-PF) model is next used for analyzing crack nucleation and propagation in polycrystalline-polyphase microstructures of metallic alloys. The model explicitly represents elastic and plastic anisotropies, tension-compression asymmetry, and the crack surface topology in the material. The phase-field model incorporates a regularization length-scale 𝑙𝑙𝑐𝑐 that controls the sharpness of the phase-field approximation to the discrete crack. Recently, to incorporate fracture energy anisotropy, the scalar fracture toughness 𝐺𝐺𝑐𝑐 is extended to an orientation-dependent tensor form and is represented in terms of crystallographic planes and their corresponding fracture energies. The development enables favorable crack growth on intrinsically weak planes in crystals. The coupled crystal plasticity-crack phase-field variational formulation is solved by a novel, wavelet-enriched adaptive FE framework. It has the unique capability of optimally resolving high gradients in the phase-field order parameter near the crack surface, and creating adaptive, multi-resolution wavelet-based hierarchical enrichment of the FE model.

Coupled deformation and crack nucleation-propagation simulations in polyphase-polycrystalline microstructures of Aluminum 7000 alloys are performed under monotonic (mode I, II) and cyclic loading conditions. As shown in these micromechanical analyses, the crack evolution in Aluminum 7000 alloys occurs in three stages, viz. crack initiation and propagation inside precipitates, the coalescence of precipitate cracks and crack propagation in the matrix. Surface precipitates play a dominant role in both the precipitate cracking and matrix cracking stages. Surface precipitates generally fail earlier compared to interior precipitates. Dominant cracks are formed by coalescence of precipitate pairs that include surface precipitates.

Back to top