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WHAT WE DO

We focus on technical projects in the field of machine learning, deep learning, and artificial intelligence using recently developed techniques, perspectives, and applications for market-relevant areas such as computer vision, natural language processing, and data analytics.

WHY IT MATTERS

We work on projects that could lead to broad impact by applying state-of-the-art research and recent computational tools. Machine learning is a crucial and sought-after skill with the recent growth in big data and the use of AI across various industries.

WHAT YOU LEARN

You will learn to analyze and apply state-of-the-art techniques from recent scholarly sources and open-source repositories, and perform data preprocessing, training, evaluation, and optimization of machine learning models using recent deep learning frameworks.

FIRE Capital One Machine Learning Poster

FIRE Capital One Machine Learning (opens new window) is a Technology & Applied Science stream of the FIRE: First-Year Innovation & Research Experience (opens new window) research program that offers a Course-based Undergraduate Research Experiences (CURE) (opens new window) to undergraduate students that have completed their FIRE semester 1 course (FIRE120) in faculty-led research and mentorship experience in the field of machine learning.

We focus on helping students develop research and career-ready skills by training them to collaboratively work on technical projects (opens new window) 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.

During the mid-point of the FIRE Capital One Machine Learning research experience, students can apply for the FIRE Summer Scholars (opens new window) program or the FIRE Summer Fellows (opens new window) 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 program 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 program 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 (opens new window). 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 (opens new window), such as be hired (opens new window) at a company or startup, be recruited as a student researcher (opens new window), or be admitted as a graduate student (opens new window) at our university or beyond.

If you are a prospective or newly admitted new freshman, freshman connection, or transfer student and would like to learn more about applying to join FIRE, please find the program application information here (opens new window).

If you are prospective academic, industry, or government partner, and would like to collaborate with FIRE Capital One Machine Learning on a research project, or would like to provide sponsorship, please find the partnership and sponsorship information here (opens new window).

# Research Educator

Dr. Raymond H. Tu

# Peer Research Mentors

2021
Derek Zhang
Rung-Chuan (Joshua) Lo
Richard Gao
Siyuan Peng
Allen Tu
Jacob Ginzberg
Priyanka Mehta
Sagar Saxena
Siddhesh Gupta
Siyao Li
2020
Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin
Richard Gao
Vladimir Leung
Siyuan Peng
2019
Timothy Lin
Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin

# Students & Alumni

2021 2021 Students
2020 2020 Students
2019 2019 Students
2018 2018 Students

# Team Projects

2020
  • Object Detection In Aerial Images
  • Speech Recognition
  • Image Stylization
  • Image Super-Resolution
  • Medical Object Detection
  • Text Generation
  • Face Generation
  • Game Playing
  • Video Object Tracking
  • 3D Object Detection
2019
  • 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
2018
  • 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

2021
Spring
2020 Fall, Spring
2019 Fall, Spring
2018 Fall, Spring

# 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 (opens new window) program or the FIRE Summer Fellows (opens new window) 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 (opens new window). 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 (opens new window), such as be hired (opens new window) at a company or startup, be recruited as a student researcher (opens new window), or be admitted as a graduate student (opens new window) at our university or beyond.

# Learning Outcomes

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

  • Demonstrate sound reasoning to analyze issues, make decisions, and overcome problems
  • Articulate thoughts and ideas clearly and effectively in written and oral forms
  • Build collaborative relationships representing diverse cultures, races, ages, genders, religions, lifestyles, and viewpoints
  • Demonstrate personal accountability and effective work habits
  • 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.
  • Design, build, and train neural networks for applications such as computer vision, natural language processing, data analytics, or automation.
  • Perform data preprocessing, training, optimization, and evaluation of machine learning models using deep learning frameworks.
  • Critically evaluate findings on the implementation results.
  • Communicate scientific ideas and findings through reports, data visualizations, and 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

# Career Outcomes

# Hiring Companies

These are some of the companies that have hired or have made job offers to our students:

  • Accenture
  • Amazon
  • Bank of America
  • Capital One
  • Chartmetric
  • Clark Construction Group
  • Coinbase
  • Epic Systems
  • Facebook
  • Google
  • Liberty Mutual Insurance
  • Los Alamos National Lab
  • Northrop Grumman
  • Perspecta Labs
  • Prudential Financial
  • Squarespace
  • Tencent
  • The Aerospace Corporation

# Graduate and Scholarship Programs

These are some of the universities that have accepted or have made enrollment and scholarship offers to our students:

  • University of Maryland
  • Johns Hopkins University
  • Michigan State University