PhD Candidate at the University of California, Santa Barbara
Graduate Student Researcher in Vision Research Lab
Interests: weak supervision, partial supervision, computer vision, machine learning, deep learning, recommender systems
Contact: mcever ⓐt ucsb.edu
PhD in Computer Science at University of California, Santa Barbara. Fall 2017 - Present
MS in Computer Science at University of California, Santa Barbara. Class of 2021
BS in Computer Science at University of Tennessee, Knoxville. Class of 2017
June 2021 - September 2021: Facebook, Software Engineer Intern - Machine Learning
Fall 2018 - Present: Vision Research Lab, Graduate Student Researcher
Summer 2018: Mayachitra Inc, Computer Vision Research Intern
Collaborated with senior researchers to participate in the Defense Innovation Unit Experimental (DIUx) xView detection challenge in overhead satellite imagery
Experimented with adapting the YOLOv3 convolutional neural network to optimize for the DIUx's xView public dataset
Summer 2017: OSIsoft LLC, Research Intern
Connected OSISoft's PI System data infrastructure to Esri ArcGIS to facilitate a real time data visualization of CURENT's real time power grid simulation
Compared the OSISoft PI System and Esri ArcGIS visualization with custom Python visualization software developed during time with CURENT
January 2016 - May 2017: CURENT UTK, Undergraduate Researcher
Created custom visualizations for power system simulations using wxPython
Traveled to Southeast University in Nanjing, China to assist in developing a genetic algorithm to find solutions to microgrid power system simulation stability issues
Marine Applied Research and Exploration (MARE) has collected hundreds of hours of video using their unmanned, underwater, remote-operated vehicles (ROVs). In order to better survey and understand life in California's costal waters, MARE has annotated each video with species and substrate labels.
My implementation of a Context-Driven Detector enables detection, tracking, and counting of invertebrate species while simultaneously generating temporal labels for substrates present in the MARE's videos as demonstrated on the soon-to-be public Dataset for Underwater Invertebrate Analysis (DUSIA).
Habitat Recognition in Underwater Vehicle Video
As a first step toward creating the Context-Driven Detector, I first implemented a method using a convolutional neural network (CNN) capable of generating temporal labels for DUSIA. A ResNet-based classification models classifies the video frame by frame, and a median filter temporally smoothes the classification results.
Semantic Segmentation of Underwater Images of Sessile Organisms
The Marine Science Institute at UCSB regularly photograph the sea floor near the Channel Islands and annotate their data using point labels, indicating the species present in a small group of pixels. I adapted a weakly supervised segmentation network designed for natural images to work with their data. A first draft of this work is available on arxiv at https://arxiv.org/abs/2007.05615.
BisQue is an open source platform for storing, visualizing, organizing, and analyzing images in the cloud, largely maintained by UCSB’s Vision Research Lab. My responsibilities include software engineering tasks such as integrating Keycloak user authentication, developing machine learning modules for public use, assisting with Docker issues, and advising undergraduate research students who participate in the project.
Deep Superpixel Features
Superpixels refer to an over segmentation used to group pixels into homogenous regions based on some criteria. They have numerous applications, but describing them computationally is not as straightforward or powerful as it could be. I am working to adapt new convolutional neural networks (CNNs) that generate superpixels to simultaneously generate deep features for those superpixels.
Digital Image Processing
Topics in Cybersecurity
Skills / Frameworks
Ubuntu / Linux