Student Name: Youngsu Shin
Thesis Title: RADIATION-TRIGGERED DEPOLYMERIZATION OF POLY(ALDEHYDE) FOR PHASE CHANGE, PAYLOAD RELEASE, AND DRY-DEVELOP LITHOGRAPHY
Thesis Advisor: Paul Kohl
Thesis Co-Advisor: N/A
Committee Members: Dr. Satish Kumar, School of Materials Science and Engineering; Dr. Will Gutekunst, School of Materials Science and Engineering; Dr. Fani Boukouvala, School of Chemical and Biomolecular Engineering; Dr. Julia Yang, School of Chemical and Biomolecular Engineering
Date: 06/26/2026
Time: 10 am
Location: Bunger-Henry 388
]]>Student Name: Sucharita Vijayaraghavan
Thesis Title: Tuning Active Site Structure and Reactivity of Pd-based Catalysts for Thermochemical Oxygen Reduction Reactions
Thesis Advisor: David W. Flaherty
Thesis Co-Advisor: N/A
Committee Members: Faisal M. Alamgir (MSE), Marta C. Hatzell (ChBE), Andrew J. Medford (ChBE), Micah S. Ziegler (ChBE)
Date: 06/29/2026
Time: 1 PM
Location: Ford ES&T L1255
]]>
Date: Tuesday, July 14th, 2026
Time: 12PM to 2PM ET
Location: GTMI 114 or via Teams
Kathryn Kelly
Robotics Ph.D. Student
Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Committee:
Dr. Christopher Saldaña (advisor) -- George W. Woodruff School of Mechanical Engineering
Dr. Thomas Kurfess -- George W. Woodruff School of Mechanical Engineering
Dr. Shreyes Melkote -- George W. Woodruff School of Mechanical Engineering
Dr. Kyle Saleeby -- Georgia Tech Manufacturing Institute
Dr. Sean Wilson -- Georgia Tech Research Institute
Abstract:
Wire-arc additive manufacturing has been widely studied in the pursuit of large-scale metal additive processes. Compared to other additive methods that deposit material at grams-per-hour, wire-arc can deposit material at kilograms-per-hour. The addition of a robot manipulator allows for the development of more complex and much larger geometries than traditional machining platforms. When depositing large amounts of material, many variables become more critical to the process effecting final product quality. Many of these variables, such as heat input, are directly controlled by the thermal properties fo the material selected. While trajectories for robotic additive manufacturing have been studied, the design of optimal trajectories that prioritize local thermomechanical conditions are not as well understood. This dissertation aims to address this gap through: (1) exploration of the efficacy of intersecting path-plan strategies for the infill of wire-arc parts, (2) investigating the impact of various intersecting path-plan strategies on thermomechanical response and part quality, and (3) studying how these strategies might be implemented for closed-loop online control algorithms. This work is impactful in providing new tools and methods for manufacturing process developers to optimize trajectories that enable deposition during the cooling process, while reducing thermal gradients and improving geometric accuracy and surface finish. Most importantly, it reduces the time needed to print large-scale components over current robotic additive methods.
]]>Date: Thursday, July 2nd (7/2), 2026
Time: 12:00-2:00pm ET
Location: Coda C1315 (Grant Park)
Zoom: https://gatech.zoom.us/j/95337401341?pwd=UA3HGBasOpWtv4JIanQFoBKtyovP9y.1
Benjamin Cobb
Computer Science Ph.D. Candidate
School of Computational Science and Engineering
Georgia Institute of Technology
Committee:
Dr. Richard W. Vuduc (co-advisor), CSE, Georgia Institute of Technology
Dr. Haesun Park (co-advisor), CSE, Georgia Institute of Technology
Dr. Edmond Chow, CSE, Georgia Institute of Technology
Dr. Grey M. Ballard, CS, Wake Forest University
Dr. Ramakrishnan Kannan, Discrete Algorithms, Oak Ridge National Laboratory
Abstract:
As our capacity to collect and generate data continues to outpace our capacity to store and analyze said data, low-rank tensor factorizations offer a solution to alleviate this issue for tensorized data. The process of computing low-rank tensor factorizations is computationally expensive and requires efficiently leveraging computational resources to enable feasibility. This thesis proposes several novel algorithms for low-rank tensor factorizations to enable interpretable analysis and compression of massive multiway datasets. To this end, we focus on three overarching themes: efficiently leveraging computational resources to compute large-scale tensor decompositions, efficiently enforcing nonnegativity constraints to improve interpretability, and incorporating additional information into the factorization to improve solution quality.
We start by proposing the Fused In-place Sequentially Truncated Higher Order Singular Value Decomposition (FIST-HOSVD) algorithm as the first in-place method for computing the dense Tucker decomposition, increasing the problem size that can be factorized by up to 3x. We demonstrate the effectiveness of the proposed FIST-HOSVD algorithm by decreasing the auxiliary memory consumption by over 135x when computing the dense Tucker decomposition on two combustion simulation compression applications. We then provide an in-depth study of several state-of-the-art methods for Nonnegative Matrix Factorization (NMF). As part of this, we provide a comprehensive survey of Nonnegative Least Squares (NNLS) solvers used to enforce the nonnegativity of the factors. In doing so, we propose a Fast Active-Set Thresholding NNLS (FAST-NNLS) solver which outperforms existing NNLS methods for broad classes of problems. We then introduce a GPU-accelerated Hierarchical NMF K-Means initialization method for large-scale protein clustering on commodity-grade hardware. We then propose the Low Rank Approximations with Constraints at Exascale (LORACX) framework for computing large-scale distributed NMF. We demonstrate that LORACX yields unprecedented performance and scalability by achieving 0.67 exaflops in double-precision on 8,192 nodes of the Frontier supercomputer when computing NMF of a 2.1 petabyte matrix. We then extend the NMF objective function to incorporate additional multiway data, culminating in a Joint Nonnegative Coupled Matrix-Tensor Factorization (Joint-NCMTF) framework. We demonstrate that the proposed Joint-NCMTF method yields improved clustering quality and additional dimensions of insight relative to traditional matrix-based methods.
]]>Committee:
Dr. Ralph, Advisor
Dr. Cai, Chair
Dr. Pestourie
]]>Committee:
Dr. Daniel Molzahn, ECE, Chair, Advisor
Dr. Saman Zonouz, ECE
Dr. Constance Crozier, ISyE
Dr. Jean-Paul Watson, Lawrence Livermore Nat Lab
Dr. Santiago Grijalva, ECE
]]>Committee:
Dr. Jennifer Hasler, ECE, Chair, Advisor
Dr. Callie Hao, ECE
Dr. Sung-Kyu Lim, ECE
Dr. Aaron Lanterman, ECE
Dr. Sahil Shah, U of Maryland
]]>
Date: Monday, July 6, 2026
Time: 11:00 AM - 1:00 PM ET
Location: TSRB523A + Remote
Remote Meeting Link: https://gatech.zoom.us/j/99856255882?pwd=hgnvB6OKoETUl5k8DZGsXbioS7k43o.1
Remote Meeting ID: 998 5625 5882 | Passcode: 318520
Cem Okan Yaldiz
Robotics Ph.D. Candidate
School of Electrical and Computer Engineering
Georgia Institute of Technology
Committee
Dr. Omer Inan (Advisor) - School of Electrical and Computer Engineering, Georgia Institute of Technology
Dr. Thomas Ploetz - School of Interactive Computing, Georgia Institute of Technology
Dr. Vince Calhoun - School of Electrical and Computer Engineering, Georgia Institute of Technology
Dr. Reza Sameni - Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology
Dr. Aaron Young – George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology
Abstract
This dissertation develops data-driven frameworks for cardiac-centered physiological analysis across multiple time horizons, leveraging multi-modal data acquired from wearable devices. At its core, the work emphasizes rigorous temporal modeling to capture the evolving dynamics of physiological states, enabling the design of predictive systems. The first contribution focuses on the early prediction of exertional heat stroke, a problem that inherently requires long-horizon inference to capture degradation in physiological state. By integrating deep learning with anomaly detection, the results show that multi-modal fusion achieves strong predictive performance, allowing the models to more faithfully represent the progression of physiological strain and stress. The dissertation then shifts to a short-horizon forecasting setting, targeting the precise timing of impending cardiac events such as aortic opening and closing. In this context, incorporating multi-modal inputs into a Kalman filter-based autoregressive framework is shown to significantly enhance accuracy, robustness, and computational efficiency. The work then examines the structural relationships among cardiac signals without target-based supervision to characterize a shared latent representation. By scaling the training data and primarily exploiting short windows of cardiac signals, the study demonstrates the effectiveness of structural self-supervised learning, particularly for distinguishing blood volume status levels. Finally, the scope is extended to cross-modal analysis of neural and cardiac signals, where multi-modal interactions are leveraged to enable the classification of major depressive disorder and preconscious responses. Taken together, this dissertation establishes that combining multi-modal fusion with principled temporal learning is key to modeling the complexity of physiological systems. Centered on cardiac data, the proposed approaches offer adaptable methodological frameworks, yielding both deeper physiological insight and robust, practical modeling strategies.
]]>
Advisor: Prof. Karl Jacob
will propose a doctoral thesis entitled,
Manufacturing-Aware Deviation Encoding for Robust Inverse Design of Additively Manufactured Lattice Metamaterials
On
Friday, June 26th, 2026
1:00 pm EST
Zoom Link: https://gatech.zoom.us/j/98511662676
Abstract
Additive manufacturing (AM) of lattice metamaterials enables fabrication of diverse structures with highly tunable mechanical responses, but process-induced geometric deviations and variability create uncertainty when comparing as-fabricated performance to predictions. Conventional inverse design and topology optimization frameworks often assume ideal reproductions of as-designed geometries, while methods that address uncertainty often treat manufacturing variability as stochastic noise to be managed probabilistically rather than as structured geometric information to be directly encoded. As a result, current workflows struggle to deliver robust, uncertainty-aware designs where predicted properties remain accurate under real process variability. This dissertation introduces the Manufacturing-Aware Deviation Encoding (MADE) vector: a compact, empirically-derived descriptor that encodes the process-specific geometric deviation fingerprint, which can be embedded directly into surrogate modeling and the inverse design loop. MADE is derived from a minimal set of CT-extracted geometric deviation features that explain dominant variance in mechanical performance over a range of lattice geometries. Demonstrated on fused filament fabrication (FFF)-printed thermoplastic polyurethane (TPU) re-entrant auxetic lattice samples, this framework enables surrogate models that propagate manufacturing uncertainty through the inverse design loop, helping bridge the gap between computational design intent and reality.
Committee
Abstract: Enhanced weathering (EW) is a promising carbon dioxide removal (CDR) strategy that could contribute to meeting the Paris Agreement goal of limiting global warming to well below 2°C above pre-industrial levels. EW involves the application of crushed silicate or carbonate rocks to terrestrial or marine environments to accelerate naturally slow weathering processes through increasing reactive surface area. During weathering, atmospheric CO₂ reacts with minerals and water to form dissolved bicarbonate and cations, which are ultimately transported to the ocean for storage on timescales of millennia. However, several challenges and uncertainties lingers and raises concerns around deploying EW at large scales. These include feedstock availability, the fate of weathering-derived carbon during riverine transport, and uncertainties in ocean biogeochemical feedbacks that may alter long-term carbon storage. This dissertation employs Earth system model of intermediate complexity cGENIE as a main tool to address these challenges through three complementary studies. First, it demonstrates that industrial by-products, including steel slag and cement waste, can serve as alternative EW feedstocks, with a cumulative carbon removal potential of ~ 400 Gt CO₂ by 2100. Second, it shows that riverine carbon loss through carbonate-system re-equilibration of different river/stream systems has a negligible impact on long-term carbon removal efficiency, because ocean uptake largely compensates for riverine carbon loss when alkalinity is retained and delivered to the ocean. Third, it evaluates the sensitivity of EW performance to uncertainties in ocean circulation and biogeochemistry, identifying key physical and biogeochemical controls on carbon removal efficiency and climate response.
Date, time, and location: 24 June 2026, 9:30am., L1114 and https://gatech.zoom.us/j/9267172570?omn=94533274204
Committee members: Dr. Christopher Reinhard (advisor), Dr. Shuang Zhang (external), Dr. Takamitsu Ito, Dr. Martial Taillefert, Dr. Yuanzhi Tang.
]]>
Title: Impact of Leakage due to Ill-fitting Earplugs on the Dissipation of High-Amplitude Sound into Vorticity
Thesis Advisory Committee:
- Prof. Spencer H. Bryngelson (Advisor), School of Computational Science and Engineering
- Prof. Krishan K. Ahuja (Co-Advisor), School of Aerospace Engineering
- Prof. Lakshmi N. Sankar, School of Aerospace Engineering
- Prof. Julien Meaud, School of Mechanical Engineering
- Prof. Qi Tang, School of Computational Science and Engineering
- Prof. Beckett Zhou, School of Aerospace Engineering
Date: Wednesday, July 1, 2026
Time: 11:00 AM – 1:00 PM EST
Location: Coda Building, Room C1315 (Grand Park), Georgia Tech
Online (Microsoft Teams): https://teams.microsoft.com/meet/232516160666016?p=3HkHdR0erDtez6bFab
Abstract: "Personnel exposed to high sound pressure level (SPL) noise rely on earplugs to prevent noise-induced hearing loss, yet non-absorbing earplugs depend on the seal between the earplug body and the ear-canal wall rather than on bulk absorption. The small air gaps that remain when the fit is imperfect can therefore dominate the acoustic performance. At high SPL, an additional dissipation mechanism becomes active in which incident acoustic energy is converted into vorticity at the sharp edges of a small opening and is then dissipated by viscosity, the same mechanism that governs acoustic liners, perforated plates, nozzles, and other small-aperture elements. This dissertation quantifies the acoustic dissipation of small, rigid-walled airborne leak paths under high-SPL excitation and resolves how the incident energy partitions between viscous attenuation and conversion into vorticity.
The work combines three methods. A custom two-sided impedance tube measures acoustic absorption directly using an impulse technique that reaches peak levels above 150 dB at the test article, with O-ring face seals that make it suitable for small leak-path test articles. Direct numerical simulation is performed with the open-source, GPU-accelerated compressible-flow solver MFC, to which this work contributed an immersed-boundary treatment of complex geometries. Spectral modal analysis is then applied to the simulation data to separate the dissipation pathways.
Using these methods, the dissertation first studies leakage in modeled earplug-canal geometries. Experiment and simulation cross-verify on a uniform leak path, reproducing the same spectral shape and SPL ordering of the reflection, transmission, and absorption coefficients. Two- and three-dimensional simulations of a nonuniform ill-fitting earplug then resolve substantial conversion of acoustic energy into vorticity around leak paths at high SPL, with the frequency dependence interpreted through the Strouhal number. To the author's knowledge, this is the first direct numerical evidence that the SPL-dependent vortex-formation mechanism documented for isolated slits and orifices activates under earplug-like leakage conditions. Because the modeled geometry omits soft-tissue compliance, contact mechanics, and the middle-ear termination, these results are interpreted at the mechanism level rather than as predictions for a specific human ear.
The dissertation then resolves the mechanism in a canonical acoustically-driven slit. A direct numerical simulation database spanning incident sound pressure level, Strouhal number, and Reynolds number reveals two distinct absorption regimes: a vortex-dominated regime at high amplitude in which the Strouhal number is the primary controlling parameter, and a viscosity-dominated regime at low SPL in which the Reynolds number plays a comparable role. Spectral proper orthogonal decomposition separates, mode by mode and frequency by frequency, the kinetic-energy and viscous-loss contributions to the dissipation and links each to its coherent spatial structure, which the integrated absorption coefficient cannot resolve. The partition between the two pathways is governed by the coupled effect of the Strouhal number, the Reynolds number, the SPL, and the Keulegan-Carpenter number. A triadic orthogonal decomposition further identifies the nonlinear inter-frequency energy transfer, showing a forward cascade in which the forced fundamental feeds higher harmonics through near-mouth and corner-localized convection before viscous removal.
These contributions trace a single physical picture from a motivating application to its underlying mechanism, quantify how high-SPL sound is dissipated at small rigid openings, and provide a frequency- and space-resolved basis for the design of leak paths, slit resonators, and related small-aperture acoustic elements.”
]]>
Dissertation Title: “Three Essays on the Economic Impacts of Climate Change: Evidence from Migration, Health, and Agricultural Trade”
Abstract:
This dissertation examines the economic impacts of climate change in Türkiye across three related domains: internal migration, public health, and agricultural trade. Drawing on province-level and bilateral panel data and a range of econometric methods, the study analyzes how variation in temperature and precipitation influences economic behavior and economic outcomes.
The first essay investigates the relationship between climate conditions and internal migration. Applying a structural gravity framework, it finds that higher temperatures increase out-migration, particularly in drought-prone provinces, indicating that climatic stress acts as an important push factor in migration decisions.
The second essay examines the health consequences of climate change using a monthly province-level panel. The analysis identifies a U-shaped relationship between temperature and elderly mortality: both extreme cold and extreme heat increase mortality, while moderate temperatures are associated with lower mortality. The results further suggest that adaptation, proxied by residential electricity consumption, can attenuate vulnerability to temperature extremes.
The third essay analyzes the effects of climate conditions on agricultural exports using both province-level and gravity-model approaches. Higher temperatures reduce export performance across vegetables, fruits, and cereals, with cereals emerging as the most climate-sensitive category, while precipitation generally supports exports. Consistent with gravity theory, an importer's GDP raises exports, and distance reduces them.
Together, these essays show that climate change shapes economic outcomes through multiple, interconnected channels: it increases migration pressures, raises mortality risks, and reduces agricultural export performance. The findings provide evidence relevant to researchers and policymakers seeking to evaluate the broader economic consequences of climate change and to design effective adaptation strategies.
Committee:
Dr. Matthew E. Oliver (Chair), Associate Professor, School of Economics, Georgia Institute of Technology
Dr. Shatakshee Dhongde, Professor, School of Economics, Georgia Institute of Technology
Dr. Tibor Besedes, Professor, School of Economics, Georgia Institute of Technology
Dr. Daniel Dench, Assistant Professor, School of Economics, Georgia Institute of Technology
Dr. Anthony R. Harding, Assistant Professor, School of Public Policy, Georgia Institute of Technology
Date: 06/26/2026
Location: Old Civil Engineering Building, Conference Room 204
Time: 1:00 PM
Online Link: https://teams.microsoft.com/meet/291440431554140?p=9CUOxbMg5P9yZHIppW
]]>
Doctor of Philosophy in Physics
School of Physics Thesis Dissertation Defense
Alex Warhover
Dr. Roman Grigoriev, School of Physics, Georgia Institute of Technology (Advisor)
A Model for Multiphase Transport in Channels with Porous Walls
Date: Monday, June 29, 2026
Time: 12:30 p.m.
Location: Howey N201/202
Virtual: https://gatech.zoom.us/j/93684214860?pwd=i99JMzQ2BRJbJpUzbXkalbJMwQNFwQ.1
Passcode: 356786
Thesis Committee:
Dr. Sven Behrens, School of Physics, Georgia Institute of Technology
Dr. Michael Schatz, School of Physics, Georgia Institute of Technology
Dr. Ari Glezer, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology
Dr. Matthew Realff, School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
Abstract:
Porous media are used in a wide range of applications because they exhibit a large internal surface area. Modeling transport in these applications is challenging because the dynamics are multiscale, involve several coupled processes, and can depend on interactions with nonporous regions. This dissertation develops a coarse-grained model from first principles for multiphase transport in channels with porous walls, motivated by temperature swing adsorption (TSA) carbon capture. The model begins with pore-scale physics, which are extended to the macroscopic scale through volume averaging. The model is then extended to describe gaseous species transport, carbon dioxide adsorption, liquid-water transport, phase change, and heat transfer. Reduced models based on rapid local equilibration are derived to address numerical stiffness while preserving the relevant macroscopic dynamics. The resulting model is applied to multiple stages of TSA operation to identify characteristic time scales. The results show that diffusion through the porous wall plays a central role in setting these time scales, together with advection in the adjacent nonporous flow regions. Accordingly, this dissertation demonstrates how multiscale physics can be integrated into a tractable continuum model for predicting multiphase behavior in porous systems.
]]>
Informing Global Polio Eradication with an Integrated Operations Research Modeling Framework
Date
June 24, 2026
Time
1:00 PM – 3:00 PM EST
Format
In-person: Groseclose 402
Online: https://teams.microsoft.com/meet/293288392873511?p=9MHkA2eP5JkxcgeDoc
· Meeting ID: 293 288 392 873 511
· Passcode: Nb7UA9wt
Candidate
Yuming Sun
Ph.D. Candidate in Operations Research
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Thesis Committee
Abstract
Despite advances in biological and medical science, infectious disease control and prevention continue to face persistent challenges arising from delayed detection, heterogeneous population immunity, and limited resources. These challenges are particularly acute for pathogens that spread largely asymptomatically, such as poliovirus, for which transmission may remain undetected until outbreaks are well established. Effective disease control requires coordinated strategies that integrate disease transmission, surveillance for early detection, and vaccination for outbreak mitigation.
This dissertation develops an integrated operations research modeling framework that links these components through compartmental simulation models, with applications to global polio eradication. The framework consists of three interacting modules: a transmission module that characterizes spatiotemporal disease spread, a vaccination module that evaluates intervention strategies under resource constraints, and a surveillance module that captures imperfect outbreak detection. Together, these modules provide a unified platform for evaluating strategies to predict, detect, and respond to polio outbreaks.
The first part of the dissertation focuses on modeling poliovirus transmission in large, heterogeneous populations. The transmission module extends a classical compartmental simulation model by incorporating heterogeneous immunity resulting from vaccination and infection events, co-circulating virus strains arising from viral mutation, age structure, and geographic heterogeneity. The model is calibrated and validated against confirmed paralytic polio cases using time-series cross-validation that preserves the temporal dependence of transmission dynamics. In a case study of poliovirus transmission in Nigeria, the validated model predicts persistent circulation in under-vaccinated areas and re-emergence in others through spatial exportation. The results suggest that the national vaccination plan may be insufficient to interrupt transmission and highlight the need for proactive preparedness for additional outbreak response activities.
Building on this transmission module, the second and third parts of the dissertation investigate how limited vaccine stockpiles should be allocated during outbreak response. In both studies, the vaccination module completely observes the progression of transmission and determines outbreak response strategies consisting of multiple vaccination campaigns that vary in timeliness, coverage, target populations, geographic scope, vaccine type, campaign duration, and vaccine allocation.
The second part examines the tradeoff between timeliness and coverage while evaluating vaccine allocation based on true immunity. Assuming individual immunity is observable, the model demonstrates the substantial benefits of prioritizing low-immunity children, defined as those lacking sufficient protection against poliovirus infection, compared with the current strategy of vaccinating all eligible children equally. Prioritizing low-immunity children consistently reduces infections and improves outbreak control, even under less favorable levels of timeliness and coverage. The results further demonstrate that, under this targeted allocation strategy, improving timeliness generally yields greater benefits than increasing coverage.
The third part addresses the more realistic setting in which true immunity is not directly observable. Instead, vaccination history is used as a proxy for immunity, and the performance of prioritizing under-vaccinated children is compared with that of prioritizing low-immunity children. The results reveal the consistent superiority of immunity-based allocation, as vaccination history does not fully capture other important determinants of immunity such as prior infection. Nevertheless, prioritizing under-vaccinated children still outperforms the current allocation strategy by reducing infections while using fewer vaccine doses, highlighting the operational value of vaccination history as a practical decision-making tool in resource-limited settings.
The fourth part relaxes the assumption of complete observability of disease transmission by incorporating a surveillance module that captures partially observable transmission dynamics. Multiple surveillance mechanisms are evaluated, including clinical surveillance that detects symptomatic infections, and environmental surveillance that monitors asymptomatic transmission. The interactions among transmission, surveillance, and vaccination are explicitly examined. Results show that improved surveillance sensitivity consistently enables earlier outbreak detection, triggers more timely responses, and reduces disease transmission. As asymptomatic transmission becomes increasingly important in highly vaccinated populations, environmental surveillance emerges as a critical complement to clinical surveillance. Furthermore, expanding environmental surveillance to previously uncovered areas provides greater benefits than intensifying surveillance in already covered areas or uniformly improving clinical surveillance.
The fifth part concludes the dissertation with a critical review of polio modeling studies conducted over the past 25 years. This review highlights successful examples in which modeling informed public health decision-making, identifies simplifying assumptions that have limited the practical implementation of modeling results, and discusses instances in which policy actions diverged from model-based recommendations. It also summarizes key knowledge gaps and outlines priorities for future methodological and applied research.
Overall, this dissertation demonstrates how an integrated operations research modeling framework can support evidence-based polio eradication strategies under real-world constraints. By linking transmission, vaccination, and surveillance, the framework generates actionable insights for policymakers and contributes analytical tools that promote efficient, timely, and equitable strategies for infectious disease control and prevention.
]]>Ph.D. Candidate: Mariam Tomori
Dissertation Title: Developing Technical Identity of Construction Engineering and Management Students with Mixed Reality
Wednesday, June 24, 2026 11:00am–1:00 PM.
Caddell Building, Conference Room 212
Microsoft Teams meeting
Meeting ID: 255 838 920 094 052
Passcode: Tx7UQ3Wd
Abstract
The construction industry remains one of the most hazardous sectors globally, with high rates of worker fatalities and injuries. These occupational injuries have negatively impacted the economy, leading to project delays, worker absenteeism, increased compensation claims, and reduced productivity. Additionally, safety challenges, coupled with increasing complexity in construction processes, necessitate innovative solutions for hazard identification, risk mitigation, and efficiency enhancement. To address these challenges, sensing technologies such as drones, laser scanners, radio-frequency identification (RFID), and wearable sensors have emerged as transformative tools. However, the widespread adoption of these technologies is hindered by a critical shortage of skilled technical workers, a problem projected to worsen as the demand for skilled workers continues to outpace supply. To bridge this gap, there is an urgent need for educational approaches that equip construction engineering students with the technical skills and professional identities required to implement sensing technologies in the construction industry. A strong professional identity fosters students’ perceptions of their technical competence, recognition, and interest in the field, which significantly influence students’ motivation, persistence, and career trajectories. Also, students with high career identities are more likely to perform successfully in their future roles in the construction industry.
However, due to the high costs of these technologies, logistical challenges, and limited opportunities for real-world construction site experiences, it is difficult to provide students with adequate practical, technology-focused learning experiences. Mixed reality (MR) environments offer an innovative solution by providing immersive, hands-on learning experiences in a safe and controlled setting. While research on professional identity development has grown, understanding how MR environment influence construction engineering students' technical identities remains limited. This research aims to investigate the potential of MR environments to foster professional technical identity development among construction engineering students. Rooted in positioning theory, the study explores how interactions with data-sensing technologies within a mixed reality learning environment influence students' perceptions of their technical competence, interest, and recognition. The study refined a MR environment developed in prior research, featuring interactive learning with sensing technologies and a virtual learning assistant.
The research employs a mixed-methods approach with three primary objectives: 1) Identify professional technical identity practices through interviews with experienced construction professionals who interacted with the MR environment; 2) Assess the psychological risks of MR environment after refining the environment with professional identity practices and storytelling-based virtual assistants; and 3) Assess how MR environment designed for technical identity development strengthen students' perceptions of their technical abilities, interests, and recognition. Objective 3 was achieved through surveys, critical-incident interviews, and performance assessments with construction engineering students from minority and non-
minority-serving institutions. Quantitative methods, including the evaluation of cognitive, emotional, and physiological factors, and perceived usability of the environment, were used to assess the impact of the MR environment. By combining qualitative and quantitative methods, this research is expected to yield theoretical frameworks of professional technical identity practices in implementing sensing technologies, an MR environment that effectively supports technical identity development, a deeper understanding of the psychological and physiological risks associated with MR environments, and an understanding of how and why a mixed reality environment strengthen technical identity development in construction engineering students. Additionally, the research will provide insights into mixed reality design interventions that promote positive learning experiences and inclusivity for all students. Ultimately, this work aims to contribute to the advancement of construction education, bridging the gap between academic learning and industry demands by equipping the future workforce to address complex challenges and drive innovation in the industry.
Committee
· Dr. Omobolanle Ogunseiju - School of Building Construction, Georgia Tech. (advisor)
· Dr. Jing Wen - School of Building Construction, Georgia Tech.
· Dr. Eunhwa Yang- School of Building Construction, Georgia Tech.
· Dr. Karen Franklin -Teaching & Learning Faculty, Center for Teaching & Learning, Georgia Tech.
· Dr. Manideep Tummalapudi - Construction Management, Lyles College of Engineering, California State University, Fresno
Title: PHYSICS-BASED APPROACHES TO INCLUDING SUPRAGLACIAL MELT
LAKES IN EARTH SYSTEM MODELS
Danielle Grau
Committee Members: Dr. Alex Robel, Dr. Winnie Chu, Dr. Yi Deng, Dr. Jingfeng Wang, and Dr. Sammie Buzzard
Date: Friday, June 26th, 2026 10 AM
Location: ES&T L1205
Zoom: Zoom link upon request
Abstract/Summary:
As anthropogenic induced climate change continues to raise Earth’s ambient temperature, the formation of supraglacial melt lakes is expected to increase. These features have been observed across the fringes of Greenland, the Antarctic Peninsula, and several ice shelves in Antarctica. A large-scale ice shelf collapse event in April 2002 of Larsen B ice shelf is believed to have been caused by an abundance of supraglacial melt lakes that formed on the surface in the months prior inducing a large-scale hydrofracture event. Since this large collapse event, the ice shelf has not been able to recover since. Despite the large impact that supraglacial melt lakes have on glacier processes, these features are not well represented in large scale earth system models. This thesis bridges this gap in knowledge by developing a new, easily-adaptable parameterization of the mean area fraction and depth of supraglacial melt lakes on self-affine glacial surfaces. These parameterizations are implemented within a large scale ice-sheet model. Simulations of the future evolution of the Antarctic Ice Sheet show that including the effect of supraglacial melt lake depth on hydrofracture-driven calving accelerates project future ice sheet mass loss by nearly 100% compared to simulations without calving and 30% compared to simulations that do not represent the depth of supraglacial lakes.
Date and Time:
Tuesday, June 30, 2026, at 11:00 AM Eastern Time
Location:
In-person: Caddell Building, Flex Space
Online: Microsoft Teams meeting
Meeting ID: 253 269 875 550 033
Passcode: Jz2dn342
Committee:
Title:
Fostering Sustainability Literacy and Construction Career Development Through Interactive Virtual Reality: An Experiential Learning Approach for High School Students
Abstract:
The Architecture, Engineering, and Construction (AEC) sector is a critical driver of global decarbonization, yet the industry faces a widening gap between the demand for green building competencies and the supply of qualified professionals. Cultivating a future-ready workforce requires early educational interventions, but high school students often lack exposure to the AEC industry as a technology-enabled and socially meaningful career path. Furthermore, existing outreach efforts frequently treat sustainability education and career exposure as separate entities, leaving a critical gap in how we jointly support environmental comprehension and vocational development. To bridge this gap, this study investigates GreenBuildVR, an interactive virtual reality-based intervention designed for high school students. Grounded in the Green Building Literacy framework and Social Cognitive Career Theory (SCCT), this study outlines four core objectives: 1) evaluating the intervention’s impact on students’ sustainability literacy; 2) assessing its effect on sustainable construction career development; 3) examining the predictive relationship between these two domains; and 4) exploring how these effects vary across contextual factors. The empirical assessment will be conducted with high school students in a summer camp setting using a pretest–posttest intervention evaluation design. This research is expected to yield an empirically grounded understanding of how VR-based intervention supports both sustainability literacy and career development among high school students. It will clarify how literacy-related constructs are associated with SCCT-based career development constructs and how student background, prior experience, and VR-related learning experience factors moderate intervention effects. The study contributes by integrating sustainability literacy and career development into a coherent VR-based intervention model, offering a construct-measure framework for pre-college sustainable construction education, and providing design guidance for AEC outreach programs seeking to support future sustainable workforce pathways.
School of Psychology- Ph.D Dissertation Defense Meeting
Date: Friday June 26, 2026
Time: 10:00 – 12:00
Location: Virtual
Zoom Link: https://gatech.zoom.us/j/96632680979
Advisor:
Dr. Hsiao-Wen Liao, Ph.D. (Georgia Tech)
Committee Members:
Dr. Paul Verhaeghen - (Georgia Tech)
Dr. Dingjing Shi - (Georgia Tech)
Dr. Tammy Tran - (Georgia Tech)
Dr. Sarah Barber - (Georgia State University)
Title: Self-Representation Stability and Intraindividual Similarities of Autobiographical Past and Future Thinking
Abstract: Autobiographical memory research has extended its focus to include episodic future thinking, finding that the way people imagine mirrors the way they remember. However, most research primarily focuses on between-individual similarities in how past and future events are experienced (phenomenology). There has been less attention to within-individual similarity between past and future thinking, as well as to other aspects that may demonstrate a parallel. Drawing on the Self-Memory System model and narrative identity research, the current research examined the role of self in autobiographical past and future thinking, specifically whether having a clear and stable sense of self correlates with greater intraindividual similarities of past and future thinking. Study 1 revealed that self-concept clarity was significantly associated with phenomenological similarities and marginally associated with motivational similarities, which paved ways for an experiment in Study 2. Study 2 experimentally manipulated self-concept clarity and tested its impact on the parallel of autobiographical past and future events among younger and older adults. The experimental effect on intraindividual similarities of past and future thinking was not found. Instead, self-concept clarity assessed at baseline appeared to predict greater phenomenological and reasoning in mental time travel, regardless of age. Self-concept clarity also predicted greater achievement and relationship motivations in younger adults’ autobiographical events. These findings suggest that individual differences in the experience of and reflection on mental time travel may be grounded in individual differences in self-representations.
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