You are here

7 Graduate Students Earn External Fellowships

Highly competitive national awards help graduate students conduct research

Each year, the Pratt School of Engineering encourages graduate students to seek support from external fellowship awards. Earning these competitive fellowships goes a long way toward helping emerging scientists conduct their research while raising Duke Engineering’s profile and research portfolio in turn.

This year, Duke Engineering graduate students manage to nab seven such fellowships from two funding agencies—the National Science Foundation (NSF) and the Department of Defense (DoD).

The NSF Graduate Research Fellowship Program is the oldest graduate fellowship of its kind in the United States, with a long history of selecting recipients who achieve high levels of success in their future academic and professional careers. This competitive fellowship program recognizes and supports outstanding graduate students who pursue research-based master's and doctoral degrees at accredited United States institutions of higher learning.

The DoD offers several different fellowship opportunities, including the National Defense Science and Engineering Graduate (NSDEG) Fellowship, and the Science, Mathematics And Research for Transformation (SMART) Scholarship for Service Program.

The NSDEG Fellowship is awarded to applications who will pursue a doctoral degree in one of 15 disciplines of interest to the DoD as a means of increasing the number of U.S. citizens and nationals trained in science and engineering disciplines of military importance. The SMART Scholarship supports students pursuing technical degrees in STEM disciplines to increase the number of civilian scientists and engineers working at DoD laboratories.

“These are highly competitive national programs,” said Jennifer West, the Fitzpatrick Family University Professor of Engineering and Associate Dean for Ph.D. Education at Duke. “It is gratifying to see the talent and hard work of our exceptional doctoral students recognized at this level.”

Congratulations to this year’s recipients.

Kristen Hagan – Advancing Optical Coherence Tomography

Kristen Hagan

NSF Graduate Research Fellowship
Biomedical Engineering
Undergraduate Institution: The University of Texas at Austin
Advisor: Joseph Izatt

Adaptive Optics Optical Coherence Tomography

Optical coherence tomography (OCT) can provide high-resolution volumetric images of the human retina in a non-invasive manner, leading to its ubiquitous presence in ophthalmology clinics. Adaptive optics OCT (AO-OCT) systems can increase the lateral resolution of retinal images and help correct for patient-specific aberrations caused by the manipulation of the light's wavefront when traveling through the eye.

Abbi Helfer - Developing Cardiac Tissue

Abbi Helfer

NSF Graduate Research Fellowship
Biomedical Engineering
Undergraduate Institution: University of Washington
Advisor: Nenad Bursac

Understanding Left-Right Patterning in Early Cardiac Development Using a Microfluidic Platform

The human body is not perfectly symmetrical. In fact, proper heart development in the embryo relies on the generation of asymmetrical anterior-posterior and left-right axes. However, most in vitro studies of human heart development fail to incorporate the asymmetrical patterning seen in gastrulation. I propose to generate a microfluidic platform capable of modeling anterior-posterior and left-right axes in a population of human stem cells by using spatiotemporally controlled gradients of signaling molecules. This platform would allow us to better understand the role that asymmetry plays in the earliest stages of heart development.

Anastasia Varanko - Drug Depots for Sustained Delivery

Anastasia VarankoNSF Graduate Research Fellowship
Biomedical Engineering
Undergraduate Institution: Cornell University
Advisor: Ashutosh Chilkoti

Molecular Drug Depots for Sustained Antibody Delivery

My project focuses on using elastin-like polypeptides (ELPs) to create subcutaneous antibody depots for continuous, long-term release of therapeutic antibodies. The technology fuses antibiotics to a heat-sensitive ELP in a solution that can be injected into the skin through a standard needle. Once injected, the solution reacts with body heat to form a biodegradable gel-like "depot" that slowly releases the drug as it dissolves.

Veronica Gough - Single Base Editing for mRNA Splicing

Veronica Gough

NSF Graduate Research Fellowship
Biomedical Engineering
Undergraduate Institution: Rice University
Advisor: Charlie Gersbach

Single Base Editing to Investigate mRNA Splicing

The improper splicing of mRNA transcripts can cause diseases such as Duchenne muscular dystrophy (DMD). The CRISPR/Cas9 system is a tool for editing the genome at specific loci that can be used to restore mRNA transcripts, but the conventional enzyme generates breaks that result in random mutations in the DNA. A new system has been developed to precisely and efficiently edit single DNA bases, and this "base editor" will be targeted to the regions that regulate mRNA splicing. By making single base changes at these locations, we will study how splice sites can be modified to regulate mRNA splicing and develop an exon-skipping strategy to correct DMD.

Jon Stewart – Plasmonic Nanoantennas

Jon Stewart

NSDEG Fellowship
Electrical and Computer Engineering
Undergraduate Institution: University of Colorado at Boulder
Advisor: Maiken Mikkelsen

Infrared Enhancement of Thin-Film Silicon Photodetectors with Plasmonic Nanoantennas

The goal of the proposed research is to understand and leverage control of light-matter interactions in plasmonic nanomaterials for optoelectronic devices. Plasmonics is a growing field that studies when light under specific conditions can excite plasmons, i.e. collective charge oscillations of electrons that are confined to metal surfaces. When this plasmon decays, the energy from the collective electron oscillation is imparted to a single hot electron. Metallic nanostructures can thus be designed to increase the absorption of light at wavelengths which correspond to the plasmon resonance of the nanostructures as well as efficiently generate hot electrons for photodetection. Further insight into these two aspects may enable a range of photonic and optoelectronic devices with new or improved functionalities. The key research objectives are to (1) elucidate plasmon interactions in sub-10 nm structures at optical frequencies, (2) demonstrate a plasmonic-enhanced infrared silicon photodetector with higher quantum efficiencies than previously shown, and (3) investigate previous claims about the preferential excitement of electrons near the Fermi energy by plasmon decay.

Kevin McHugh - Modeling Large Deflection Nonlinearities

Kevin McHughSMART Scholarship
Mechanical Engineering and Materials Science
Undergraduate Institution: Lafayette College
Advisor: Earl Dowell

Modelling Large Deflection Nonlinearities in Beams and Plates for Aeroelastic Analyses

During the design process for a structure moving relative to a fluid, engineers must consider dynamic instabilities from the fluid-structure interaction. While widely studied for its criticality to system design, many analyses use overly simplified linear methods. With the advent of nonconventional composite materials used for lightweight structures and with the growing need for highly deflected or even folded and deployable structures, these linear techniques are not sufficient. During my time at Duke University in Professor Earl Dowell's aeroelasticity research group, I have studied nonlinearities associated with structures undergoing large (nonlinear) deflections. By developing a three-dimensional, nonlinear fluid-structure interaction mathematical model for highly deflected beams, plates and shells, I am able to study dynamic instabilities and responses of complex geometries. My plans are to now eliminate dynamic instabilities of military structures such as aircraft rotors, wings, and turbomachine blades.

Jim Turner - Machine Learning for Robotic Control

Jim Turner

NSDEG Fellowship
Mechanical Engineering and Materials Science
Undergraduate Institution: North Carolina State University
Advisor: Brian Mann

Data-Efficient Reinforcement Learning for Constrained Underactuated Nonlinear Dynamical Systems

Limitations on control, such as underactuation and power, stress, and torque constraints, are common to most robotics problems. Engineers often avoid these constraints by adding powerful actuators and using strong components, but this increases weight, bulkiness, energy consumption and cost. Naive reinforcement learning techniques can help robots learn subject to constraints, but they require the robot to accumulate a lot of data through experience. My research interest is to develop practical learning techniques which function effectively for constrained nonlinear systems with limited data. Enabling robotic systems to learn quickly will enhance their adaptability to new and unfriendly environments, reduce energy expenditure, increase agility and expand their capabilities to more complex tasks.