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Purdue Bilsland Dissertation

AAE PhD students Andrea Nicolas, Gayathri Shivkumar and Pei Zhang have each received a Bilsland Dissertation Fellowship, which is administered by the Purdue Graduate School.

AAE PhD students Andrea Nicolas, Gayathri Shivkumar and Pei Zhang have each received a Bilsland Dissertation Fellowship, which is awarded by the Purdue Graduate School.

The Fellowship provides support to outstanding PhD candidates in their final year of doctoral degree completion. The students are nominated and selected by members of the graduate faculty.

About Andrea

Andrea Nicolas

Andrea is a 4th year PhD student of Professor Michael Sangid. Her dissertation is titled “Relationships Between Galvanic Driving Force and Strain Energy Density Accumulation.” In her research, she looks at the integrated evolution of corrosion from both a mechanical and chemical point of view, which is very innovative since usually corrosion is separately evaluated from either perspective but never simultaneously from both. She enjoys playing violin and running.

“I am really happy and grateful to be awarded this fellowship because I can continue working in something I really love surrounded by amazing people,” Andrea says. “I am extremely thankful toward AAE, my advisor and my awesome labmates/classmates for being so supportive and welcoming throughout my studies here at Purdue.”

About Gayathri

Gayathri Shivkumar

Gayathri is a PhD student of Professor Alina Alexeenko. Her dissertation is titled “Coupled Plasma, Fluid and Thermal Modeling of Low-Pressure and Microscale Gas Discharges.” Part of her research focuses on the modeling of hydrogen microwave plasma for the chemical vapor deposition (CVD) of graphitic nanopetals, graphene and carbon nanotubes and introduction of a pillar for growth enhancement. She enjoys playing sports and listening to music.

“I feel very honored and humbled to be chosen as a fellowship recipient from a department with many excellent PhD candidates,” Gayathri says. “I also feel motivated to keep up my performance and work hard to improve it. I am extremely grateful to my advisor, Professor Alina Alexeenko, for all her support and guidance.”

About Pei

Pei Zhang

Pei is a PhD student of Professor Haifeng Wang. Her dissertation is titled “Modeling of Multi-regime Turbulent Combustion: Challenges and Advances.” Her research focuses on extending the traditional single regime combustion models to predict multi-regime combustion, which will ultimately help the design and optimization of combustors. Outside of the classroom and lab, she enjoys watching comedy shows and cooking.

“I am deeply honored to receive the fellowship and very grateful to the department and graduate school for providing the fellowship,” Pei says. “I would like to thank my advisor, Professor Haifeng Wang, for his patient guidance and constant support over the past four years.”

Fangning He, a PhD Candidate in the Lyles School of Civil Engineering, has been named as recipient of the 2016 Bilsland Dissertation Fellowship Award.

Fangning He, a PhD Candidate in the Lyles School of Civil Engineering, has been named as recipient of the 2016 Bilsland Dissertation Fellowship Award. The title of Fangning’s dissertation is "Dense Point Cloud Generation from Passive and Active Sensors."

Fangning’s current research deals with precise point cloud generation from passive and active remote sensing systems. In this regard, he has been developing a prototype system for the automated  recovery of the orientation parameters for a set of images that has been captured by low-cost digital cameras onboard Unmanned Aerial Vehicles (UAVs) as well as generation of dense 3D point clouds from this data. More specifically, the developed prototype system has several advantages, including: 1) it utilizes an improved orientation recovery for UAV flight missions in the presence of prior information regarding the platform trajectory; 2) it can handle imagery with repetitive texture; and 3) it adopts a modified semi-global dense matching technique, which optimizes the 3D reconstruction in object space, for image-based dense point cloud generation. In the meantime, to consider the complementary characteristics of data acquired from different remote sensing systems, he is also introducing procedures for the registration of image-based and laser-based point clouds. Such development will enhance the performance of existing dense image matching techniques as well as extraction/interpretation of available features in the imaged objects.

Fangning is advised by Professor Ayman Habib.