# FIRE Capital One Machine Learning Logo

FIRE Capital One Machine Learning Poster

FIRE Capital One Machine Learning is a multi-semester CURE: Course-based Undergraduate Research Experience that immerses undergraduate students that have completed their FIRE semester 1 course in faculty-led research and mentorship experience in machine learning.

We focus on helping students develop research and career-ready skills by training them to work on technical projects in the field of machine learning, deep learning, and artificial intelligence using recently developed techniques and perspectives, and apply them to market-relevant areas such as computer vision, natural language processing, automation, and data analytics.

# Research Educator

Dr. Raymond H. Tu

# Peer Research Mentors

Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin
Richard Gao
Vladimir Leung
Siyuan Peng
Timothy Lin
Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin

# Students & Alumni

2020 2020 Students
2019 2019 Students
2018 2018 Students

# Team Projects

  • Object Detection In Aerial Images
  • Protein Structure Prediction
  • Speech Recognition
  • Image Stylization
  • Image Super-Resolution
  • Human Pose Estimation
  • Medical Object Detection
  • Text Generation
  • Face Generation
  • Game Playing
  • Object Tracking
  • 3D Object Detection
  • 3D Object Detection and Localization of Camera, LIDAR, and RADAR Objects for Self-Driving Cars
  • Visual Object Detection and Tracking for Surveillance Videos and Self-Driving Cars
  • Extreme Image Compression from Learned Objects for Images and Videos
  • Image Caption Generation for Visually Impaired Users
  • Instance-level Object Tracking and Segmentation Across Video Frames
  • Using Neural Networks to Identify Individual Bats of the Myotis Vivesi Species
  • Tracking Bee Identities and Behaviors with Convolutional Neural Networks
  • Improving 9-1-1 Call Operations Efficiency with Natural Language Processing
  • Detecting Driver Drowsiness and Attentiveness Through Facial Recognition

# Course Syllabi


# Description

FIRE Capital One Machine Learning Timeline

The FIRE Capital One Machine Learning research experience provides a multi-semester course sequence that spans over a full year, with the first day of the course sequence starting at the Spring semester each year. Students from all degree majors that have completed their FIRE semester 1 (FIRE120) course will have the opportunity to enroll in our FIRE198 and FIRE298 course sequence during the Spring and Fall semesters.

Other than 1 hour of scheduled class meetings per week, the FIRE Capital One Machine Learning research experience requires each student to commit 5-6 additional hours of independent and collaborative activities, meetings, and events each week.

Each student throughout the semester will work both individually and as a team member of a faculty-led project. Research sessions will focus on faculty-led research, including collaboration with peers, communication of ideas, troubleshooting unexpected outcomes, as well as giving students relevant experiences that seek to build resiliency and critical analysis skills.

Scheduled class meetings will focus on training in current discipline-specific methods and practices, discussion of primary literature, troubleshooting research issues, and continual review of individual and group research progress.

During the mid-point of the FIRE Capital One Machine Learning research experience, students can apply for the FIRE Summer Scholars program or the FIRE Summer Fellows program that enables a fully immersive research experience. FIRE summer students will be required to fully commit a specific number of hours per week. The FIRE Summer programs help students stay connected to their stream and continue their research and professional development over the summer. Each summer student will truly be a member of a working research group led by the Research Educator during the summer period. The FIRE summer programs offers an excellent opportunity to rapidly advance the student's research and professional skills.

Upon the completion of the FIRE Capital One Machine Learning research experience, students will also have the opportunity to continue their FIRE experience by becoming a peer research mentor. Continuing their FIRE experience as a Peer Research Mentor will provide them with a number of benefits beyond the chance to provide a meaningful impact on another FIRE student's experience and the opportunity to continue their own research. Peer Research Mentors also receive outstanding recommendation letters that help them succeed in their next steps, such as be hired at a company or startup, be recruited as a student researcher, or be admitted as a graduate student at our university or beyond.

# Learning Outcomes

Upon completion of this research experience, students will be able to:

  • Articulate thoughts and ideas clearly and effectively in written and oral forms (career readiness - oral and written communications).
  • Build collaborative relationships representing diverse cultures, races, ages, genders, religions, lifestyles, and viewpoints (career readiness - teamwork & collaboration).
  • Demonstrate personal accountability and effective work habits (punctuality, working productively with others, time and workload management - career readiness - professionalism & work ethic).
  • Demonstrate knowledge of fundamental concepts and ideas in neural networks and deep learning.
  • Analyze state-of-the-art techniques from recent scholarly papers and code repositories.
  • Perform data preprocessing, training, optimization, and evaluation of machine learning models using deep learning frameworks (such as Keras, Tensorflow, and PyTorch).
  • Collaborate in a teamwork environment with the research leader and a team of student researchers to design, implement, and apply machine learning models for real-world usage.
  • Lead and drive machine learning projects.
  • Work with machine learning tools, libraries, and modules.
  • Download, manage, and preprocess large datasets.
  • Design, build, and train neural networks for applications such as computer vision, natural language processing, data analytics, or automation.
  • Fine-tune and optimize the model for real-world applications.
  • Critically evaluate findings on the implementation results.
  • Communicate scientific ideas and findings through reports, data visualizations, and multimedia presentations.

# Research Outcomes

# Code Repositories

# Research Posters

3D Object Detection and Localization Object Tracking Image Compression Muliple Object Segmentation Nuscenes 3D Detection Bat Call Identification

# Acknowledgements

We sincerely thank the following sponsors for providing us with the funds, services, and tools to help FIRE Capital One Machine Learning provide a better innovation & research experience for our students.