Upcoming Doctoral Dissertations

"Traditional cementitious materials exhibit excellent compressive strength but suffer from low flexural strength and toughness. In addition, their high carbon emissions and thermal conductivity contribute to energy-intensive and environmentally unsustainable construction practices. To address these limitations, this research draws inspiration from natural materials, aiming to replicate their unique structure to improve the flexural strength, toughness, sustainability, and multifunctionality of cementitious systems. This dissertation is organized into two main research thrusts." Read more

"Functionally graded materials (FGMs) offer considerable potential to replicate the complex compositional and mechanical transitions found in native musculoskeletal tissues. However, their clinical implementation has remained limited due to challenges in controlling fine-scale gradients, incomplete understanding of tissue-specific mechanical and structural properties, and the lack of robust quantitative models for material design. This thesis aims to overcome these barriers through an integrated approach combining comprehensive literature review, precision bioprinting, detailed structural and mechanical analyses, and advanced machine-learning (ML) modeling." Read more

"This dissertation presents a comprehensive investigation into the thermal transport properties of two-dimensional (2D) materials, with a focus on how strain engineering can modulate phonon and electron transport. A combination of experimental fabrication, advanced characterization, and first principles simulations was employed to uncover the underlying mechanisms governing heat and charge conduction in low-dimensional systems." Read more

"As machine learning models are increasingly deployed in socially impactful domains, addressing algorithmic bias in model design and deployment has become a critical concern. This thesis presents a series of contributions aimed at mitigating bias to improve both individual- and group-level fairness across several learning paradigms, including active learning, federated learning, and large language models ." Read more

"Nitro compounds are widely used in pharmaceutical, mining, and defense industries. Their high solubility, chemical stability, and resistance to degradation contribute to their persistence in industrial wastewater. This poses significant environmental and public health risks. This dissertation evaluates biochar-supported nanoscale zero-valent iron (nZVI-BC) composites as a sustainable treatment technology for nitro compound removal from aqueous systems. Nitroglycerin (NG), commonly found in both pharmaceutical and munitions wastewater, was selected as a model compound." Read more

"Coastal urban areas face increasing flood risk from both storm surge and extreme rainfall events, with the potential for these hazards to co-occur, compounding their flooding and potentially worsening impacts. Traditional flood risk assessments and forecasting systems often neglect the complex interactions between coastal and pluvial (rainfall-driven) flooding processes, leading to underestimation of flood hazards and inadequate preparedness strategies. This dissertation advances the understanding and prediction of compound coastal-pluvial flooding through integrated modeling approaches and machine learning techniques, as informed by a novel urban flood observation network and recent extreme and record-setting flood events." Read more

"Designing viscoplastic, yield-stress fluids that flow smoothly under applied stress yet form solid-like structures when at rest remains a persistent challenge across fields such as food engineering and biomaterials. This dissertation presents an integrated rheological and data-driven framework that transforms standard laboratory measurements—small-amplitude oscillatory shear and torque data from steady torsional flow— into predictive tools for characterizing shear viscosity and guiding processing across a wide range of chemically diverse systems. Central to this framework is a slip-aware multigap protocol that first extracts the yield stress from torque versus apparent shear rate data, then corrects for wall slip to obtain the true shear rate." Read more

"The three essays presented in my dissertation examine the application of advanced generative AI techniques to financial market modeling and high-frequency trading. They explore flow matching-based imitation learning for adaptive market making, optimal execution under stochastic volatility, and byte-level generative modeling of orderbook dynamics." Read more

"The three essays presented in my dissertation examine how gender empowerment, managerial strategy, and financial innovation shape firm outcomes under market and political uncertainty. Together, these essays contribute to a deeper understanding of how social progress, executive decision-making, and financial market developments interact to influence corporate behavior and performance ." Read more

"Financial systems are inherently interconnected, with shocks propagating through institutions in complex ways. Understanding contagion dynamics is crucial for identifying vulnerabilities, particularly during periods of financial turmoil. This dissertation explores these broader themes by combining network theory, statistical validation, and advanced risk modeling to analyze both traditional financial networks and decentralized finance (DeFi) systems." Read more

"Multivariate time series (MTS) is widely used in various domains, including healthcare, finance, and industrial applications. While machine learning shows exceptional performance in MTS forecasting, primary challenges remain: 1) difficulties in early-stage prediction due to data scarcity; 2) limited integration of prior experience, as most existing work is isolated without knowledge transfer from related tasks; 3) the underutilization of complex exogenous factors, such as news, policies, and geolocations. This defense utilizes cutting-edge machine learning to improve MTS analysis accuracy and robustness for data-driven decision-making." Read more