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Author:

Zawadzki, Brianna

Title:

Investigating Planet Formation Through Simulation, Observation, and Machine Learning

Graduate Program:

Astronomy and Astrophysics (PHD)

Keywords:

astronomy
protoplanetary disks
exoplanets
planet formation
machine learning
interferometry
imaging

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Committee Members:

Bradford Foley, Outside Unit & Field Member
Rebekah Dawson, Major Field Member
Kevin Luhman, Major Field Member
Robin Ciardullo, Program Head/Chair
Ian Czekala, Chair & Dissertation Advisor
Eric Ford, Major Field Member

Author:

Zhou, Chen

Title:

Application of machine learning to optical sensing and imaging

Graduate Program:

Electrical Engineering (PHD)

Keywords:

Optical sensing
optical imaging
machine learning

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Committee Members:

Zhiwen Liu, Chair & Dissertation Advisor
Christine Keating, Outside Unit & Field Member
Seyedehaida Ebrahimi, Major Field Member
Madhavan Swaminathan, Program Head/Chair
Xingjie Ni, Major Field Member

Author:

La Chat, Grant

Title:

Identification of Rossby Wave Breaking Flavors using Self-Organizing Maps

Graduate Program:

Meteorology and Atmospheric Science (MS)

Keywords:

machine learning
self-organizing maps
anticyclones
cyclogenesis
Potential vorticity
Rossby waves
Wave breaking
Synoptic meteorology
moisture transport

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Committee Members:

David Stensrud, Program Head/Chair
Melissa Marie Gervais, Thesis Advisor/Co-Advisor
Steven B Feldstein, Committee Member
Kevin Andrew Bowley, Thesis Advisor/Co-Advisor
Colin M. Zarzycki, Committee Member

Author:

Di Domenico, Nicolle

Title:

Quantifying the Spatiotemporal Inundation Patterns of Tundra Polygonal Ground and Its Relationship with Arctic Climate

Graduate Program:

Geography (MS)

Keywords:

geomorphology
Arctic
polygonal ground
geography
remote sensing
machine learning
climate change

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Committee Members:

Shujie Wang, Thesis Advisor/Co-Advisor
Manzhu Yu, Committee Member
Brian King, Program Head/Chair
Elizabeth Herndon, Special Signatory

Author:

Saha, Gourab

Title:

A Vision Towards Developing a Spatially And Temporally Robust Data-Driven Modeling Framework To Predict Continuous Stream Nitrate Concentration At Data-Scarce Locations

Graduate Program:

Agricultural and Biological Engineering (PHD)

Keywords:

LSTM
stream nitrate modeling
deep learning
machine learning
water quality
c-Q relationship

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Committee Members:

Suat Irmak, Program Head/Chair
Long He, Major Field Member
Cibin Raj, Chair & Dissertation Advisor
Chaopeng Shen, Outside Field Member
Jonathan Duncan, Outside Unit & Field Member