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"Twenty years from now you will be more disappointed by the things you didn’t do than those you did. So throw off the bowlines. Sail away from safe harbor. Catch the wind in your sails. Explore. Dream. Discover.”  H. Jackson Brown Jr.

First-principles calculations of structural plasma-facing materials

Tungsten (W) and tungsten alloys are being considered as leading candidates for structural and functional materials in future fusion energy devices. Significant neutron-induced transmutation happens in these tungsten components during nuclear fusion reactions, creating impurity elements such as Re, Os, and Ta that change the mechanical response of the material.

In this project, we use first-principles calculations based on density functional theory to investigate the mechanical response of tungsten alloys exposed to fusion-like environments. In particular, we focus on how the elastic constants, elastic properties, ideal tensile strength, and ductility evolve as the alloy composition changes during the course of irradiation.


Collaborators: Mark R. Gilbert (Culham Science Centre for Fusion Energy), Lucile Dezerald (Université de Lorraine), Duc Nguyen (Culham Science Centre for Fusion Energy) .

Unraveling the structure-property relationships in fiber-composite materials using Machine Learning and Global Sensitivity Analysis

In this project, we are developing a machine-learning based model that exploits a combination of the physical knowledge of the microstructure with data-driven techniques to predict the local strain field in the material. In particular, we are applying this method to extract the structure-property linkages in a two-dimensional metal matrix composite (MMC), by using a variety of neural networks. As part of the model, global sensitivity analysis is also employed to identify the most prominent microstructural features that drive the mechanical behavior of the material.

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Collaborators: Lori Graham-Brady (Johns Hopkins University), Francisco L. Jimenez (UC Boulder), Jessica A. Krogstad (University of Illinois at Urbana-Champaign), Michael D. Shields (Johns Hopkins University).

Enhancing the bonding strength of dental restorations: a bioinspired approach

Human teeth present a structure composed of two hard tissues, enamel and dentin, which are strongly bonded at the so-called Dentino-Enamel-Junction (DEJ). Despite the elastic mismatch between enamel and dentin, teeth rarely fail at the DEJ because of the highly mineralized transition layer and its variable mechanical properties. Conversely, the mechanical properties of resin composites are quite uniform, creating a mismatch in properties of adjacent layers that frequently leads to the development of interlaminar stresses and subsequent failure.

In this project we aim to increase the current lifespan of tooth restorations by creating and validating a bio-inspired approach that studies the mechanics of bonded interfaces.


Collaborators: Santiago Orrego (Temple University), Carolina Montoya (Temple University), Kavan Hazeli (University of Arizona).

Stronger and stickier: design of bio-inspired metamaterials with advanced mechanical properties

How can we guarantee that the materials used in aerial robots will allow for safe and efficient adhesion to the targeted surfaces? Ants are one of a few examples offered by Nature where species with extremely diverse weights are capable of successfully attaching and detaching at their own will.

The aim of this project is to design metamaterials with advanced mechanical properties that mimic the structure-property relationships observed in adhesive ant pads.


Collaborators: Alyssa Stark (Villanova University), Grace Gu (UC Berkeley), Gang Feng (Villanova University), Sylvie Lorente (Villanova University).

Predicting the Risk of Spontaneous Premature Births in First-Time Mothers Using Clinical Data and Machine Learning

According to the World Health Organization, premature birth (babies born before 37 completed weeks of gestation) affects approximately 15 million neonates around the world every year. In the United States, the premature birth rate is still 10%, and complications associated with these early births represent the leading cause of death in children under five years of age.  Two-thirds of these occur unexpectedly, with the subsequent risk for moms and babies. 

Recent studies have improved the accuracy to identify spontaneous preterm birth (sPTB) of women having at least one prior birth but there is still a high level of uncertainty when there is limited information about a previous pregnancy. Doctors thus remain unable to adequately assess the risk in first-time mothers. How can we provide quantitative predictions of these preterm births when no information about a previous at-risk pregnancy is available?  This project will address this question by creating and validating a machine learning algorithm that can determine the risk; this algorithm would subsequently form the basis for a clinical decision support system (CDSS) to aid physician intervention and reduce sPTB in first-time mothers.


Collaborators: C. Nataraj (Villanova University), Heather H. Burris (Children's Hospital of Philadelphia), Michal A. Elovitz (University of Pennsylvania, Perelman School of Medicine).

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