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Raymond H. Tu

Assistant Clinical Professor

Machine Learning

Using statistical and algorithmic techniques to give computer systems the ability to "learn" from data, without being explicitly programmed.

Cybersecurity

Protecting computer systems from theft or damage to their hardware, software or electronic data, as well as from disruption or misdirection of the services they provide.

Computer Science

Studying the theory, experimentation, and engineering that form the basis for the design and use of information, computation, and their implementation in computer systems.

# FIRE Capital One Machine Learning Logo


FIRE Capital One Machine Learning Poster

Hi, I'm the founding faculty and research educator of FIRE Capital One Machine Learning, a FIRE: First-Year Innovation & Research Experience innovation & research stream experience at the University of Maryland.

FIRE Capital One Machine Learning is a CURE: Course-based Undergraduate Research Experience that immerses first-year (new freshman, freshman connection, transfer) undergraduate students in state-of-the-art research and faculty-led mentorship experience in machine learning, deep learning, and artificial intelligence.

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.

Our recent machine learning projects include:

  • 3D Object Detection and Localization for Autonomous Vehicles
  • Object Recognition and Tracking for Surveillance Videos
  • Extreme Image Compression from Learned Objects

If you are a prospective or newly admitted new freshman, freshman connection, or transfer student and would learn more about applying to join FIRE Capital One Machine Learning, please find the program application information here.

Upon the completion of the FIRE Capital One Machine Learning stream experience, students will also have the opportunity to receive recommendation help them succeed in their next steps, such as becoming an accredited AMP peer mentor, be hired at a company and startup, be recruited as a student researcher, or be admitted as a graduate student at our university or beyond.

If you are prospective academic, industry, or government partner and would like to collaborate with FIRE Capital One Machine Learning on a research project, please find the collaboration information here.

# Contact

Email: [email protected]
Location: 3136 A.V. Williams Building, University of Maryland, College Park