Austin McEver

PhD Candidate at the University of California, Santa Barbara

Graduate Student Researcher in Vision Research Lab

Interests: weak supervision, computer vision, machine learning, deep learning

Contact: mcever at ucsb.edu

Education

  • PhD in Computer Science at University of California, Santa Barbara. Fall 2017 - June 2022 (expected)

  • BS in Computer Science at University of Tennessee, Knoxville. Class of 2017

Current Projects

Habitat Recognition in Underwater Vehicle Video

Marine Applied Research and Exploration (MARE) has collected hundreds of hours of video using their unmanned underwater vehicles and annotated each video with species and habitat labels. We are adapting scene recognition algorithms to their video in order to predict habitat temporal spans, and, later, recognize and detect vertebrate and invertebrate species. See some preliminary results in the video to the left.

Semantic Segmentation of Underwater Images of Sessile Organisms

The Marine Science Institute at UCSB has worked to photograph the sea floor near the Channel Islands and annotate their data using point labels. We are adapting state of the art algorithms for segmenting natural images to work with their data. With this small amount of additional supervision, we can achieve impressive semantic segmentation results comparable with fully supervised methods by using convolutional neural networks to learn semantic affinity and localization cues.

BisQue

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.

Professional Experience

  • 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 specialize on the xView dataset

  • Summer 2017: OSIsoft LLC, Data Visualization 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 provided by 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

Graduate Courses

  • Digital Image Processing

  • Computer Imaging

  • Information Theory

  • Matrix Analysis

  • Machine Learning

  • Pattern Recognition

  • Computer Vision

  • Topics in Cybersecurity

  • Operating Systems

Skills / Frameworks

  • Python

  • Pytorch

  • OpenCV

  • Scikit-Learn

  • Ubuntu / Linux

  • Git

  • Docker

  • Scikit-image

  • TensorFlow

  • Java

  • C,C++

  • MATLAB

  • HTML

  • Javascript

  • tmux

  • vim