About
My background in data science, mechanical engineering, and biomedical engineering provides a unique skill set for solving problems.
I am particularly interested in computer vision and natural language processing challenges.
While attending graduate school, I have taken every opportunity to develop my data science and machine learning skillset through coursework and research opportunities.
My coursework has included Machine Learning, Deep Learning, and Artificial Intelligence classes.
My current research has also provided opportunities to develop these skills, especially those related to image processing and analysis.
I am a Ph.D. student at the University of Utah performing research in the Fracture and Fatigue of Skeletal Tissues Lab
with Professor Claire Acevedo.
My research involves 3D visualization and analysis of bone's microstructure using confocal microscopy and synchrotron radiation micro-computed tomography techniques.
My research involves developing new methods to extract information about bone health by analyzing bone's microstructure. This development
requires a combination of skills from computer science, mechanical engineering, and biomedical engineering.
Hobbies
I am native to Salt Lake City, Utah. I enjoy spending time in the nearby canyons doing activities such as biking, climbing, skiing, and hiking.
Projects
Roadway Sign Detection and Classification
This was a capstone project for the
Deep Learning Certificate at the University of Utah. In this project, a team of three graduate students partnered with Blyncsy Inc. to solve a relevant problem.
Our team developed an approach to detect and classify roadway signs in dashcam images. Our solution consisted of two stages, detection using Faster R-CNN and
classification using EfficientNet..
A key challenge in this problem was obtaining representative data for over 600 classes of roadway signs. To overcome this obstacle,
we generated synthetic data using standard example images of roadway signs from the MUTCD.
This approach achieved modest results with relatively little development time.
This project was selected for a podium presentation at the IEEE MFI conference
Deep Learning For Image Denoising & Segmentation
Deep learning has proven to be an incredibly useful tool in image processing. This project used a convolutional neural network to perform both denoising (regression) and segmentation tasks on micro CT data.
We used the U-net architechture presented by Ronneberger to process the images. I developed a custom
implementation of the U-net in Pytorch to perform the denoising and segmentation tasks.
The code for the U-net can be viewed here.
Light-Weight Machine Learning Library
While enrolled in the Machine Learning course (CS 6350)
at the University of Utah, I developed a lightweight machine learning library. The library is written in Python and includes implementations of the following algorithms:
- Decision Tree
- Adaboost
- Bagging
- Random Forest
- Linear Regression
- Perceptron
- Support Vector Machine
- Logistic Regression
- Neural Network
Developing this library provided a valuable experience to gain better understanding of how these algorithms learn to perform classification and regression tasks.
The code for this lightweight library can be viewed here.
SPIE: Incorporating machine learning with Raman spectroscopy to differentiate bone types
I had the opportunity to present experimental work at the SPIE conference in 2020 about using Raman spectroscopy and machine learning (ML)
to differentiate bone types. This project used Raman spectra of similar tissues as input to ML algorithms to learn their classification.
The samples included rat long bone, rabbit long bone, and rabbit crania.
I tested the effect of different data processing and techniques before classification. These approaches included using intensities at all
wavenumbers, dimension reduction through principal component analysis (PCA), and feature extraction using domain knowledge.
I tested the performance of a variety of ML classification algorithms on the bone samples.
This project's most successful classification approach included extracting features according to domain knowledge and performing the classification with support vector machine.
This analysis was performed using R.
The abstract for this project can be found here.
Publications
-
Unraveling the Effect of Collagen Damage on Bone Fracture Using in-situ Synchrotron Microtomography with Deep Learning
Oct, 2022 | Nature Communication Materials
-
Multi-tissue Analysis Using Synchrotron Radiation Micro-CT Images
Oct, 2022 | IEEE eScience
-
Automated road asset data collection and classification using consumer dashcams
Sept, 2022 | IEEE MFI
-
Physical characterization of swine and human skin: Correlations between Raman spectroscopy, Tensile testing, Atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Multiphoton microscopy (MPM)
Nov 20, 2020 | Skin Research & Technology
-
Cortical and Cancellous Bone Regeneration in Cranioplasty and Spinal Arthrodesis Models Using Autologous Homologous Bone Construct (AHBC)
Jun 26, 2020 | Journal of Regenerative Medicine
-
Incorporating machine learning with Raman spectroscopy to differentiate bone types
Feb 21, 2020 | SPIE
-
Effect of Principal Component Analysis Centering and Scaling on Classification of Mycobacteria from Raman Spectra
2016 | Applied Spectroscopy
-
Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform
2016 | Journal Of Visualized Experiments
-
The Use of microfluidics and dielectrophoresis for separation, concentration, and identification of bacteria
2016 | SPIE Photonics
Presentations
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IEEE eScience Oct 2022
Multi-tissue Analysis Using Synchrotron Radiation Micro-CT Images
Salt Lake City UT - Podium
-
IEEE International Conference on Multisensor Fusion and Integration Sept 2022
Automated road asset data collection and classification using consumer dashcams
Cranfield, UK - Podium (remote)
-
Gordon Conference Aug 2022
Spatial Control of Perilacunocanalicular Mineral During Lactation
Andover NH - Poster
-
Swiss Bone Mineral Society 2021
Pushing the limits of Synchrotron Micro-Tomography Understanding Bone’s Fracture Mechanism Using Digital Volume Correlation Enabled by Deep Learning
Switzerland - Podium (remote)
-
American Society for Bone and Mineral Research 2020
The Role of Matrix Metalloproteinase-13 on Perilacunar/Canalicular Remodeling During Lactation
Virtual - Poster
-
SPIE Photonics 2020
Incorporating Machine Learning with Raman Spectroscopy to Differentiate Bone Types
San Francisco CA - Podium
Resume
Contact
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