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<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a></div> <!----></nav> <ul class="sidebar-links"><li><section class="sidebar-group depth-0"><p class="sidebar-heading open"><span>Home</span> <!----></p> <ul class="sidebar-links sidebar-group-items"><li><a href="/#curriculum-overview" class="sidebar-link">Curriculum Overview</a><ul class="sidebar-sub-headers"></ul></li><li><a href="/#introduction" class="sidebar-link">Introduction</a><ul class="sidebar-sub-headers"></ul></li><li><a href="/#our-people" class="sidebar-link">Our People</a><ul class="sidebar-sub-headers"><li class="sidebar-sub-header"><a href="/#students-and-alumni" class="sidebar-link">Students and Alumni</a></li><li class="sidebar-sub-header"><a href="/#peer-research-mentors" class="sidebar-link">Peer Research Mentors</a></li><li class="sidebar-sub-header"><a href="/#faculty-leader" class="sidebar-link">Faculty Leader</a></li></ul></li><li><a href="/#program-structure" class="sidebar-link">Program Structure</a><ul class="sidebar-sub-headers"><li class="sidebar-sub-header"><a href="/#learning-outcomes" class="sidebar-link">Learning Outcomes</a></li><li class="sidebar-sub-header"><a href="/#course-syllabi" class="sidebar-link">Course Syllabi</a></li><li class="sidebar-sub-header"><a href="/#class-discussions" class="sidebar-link">Class Discussions</a></li><li class="sidebar-sub-header"><a href="/#research-projects" class="sidebar-link">Research Projects</a></li></ul></li><li><a href="/#research-outcomes" class="sidebar-link">Research Outcomes</a><ul class="sidebar-sub-headers"><li class="sidebar-sub-header"><a href="/#code-repositories" class="sidebar-link">Code Repositories</a></li><li class="sidebar-sub-header"><a href="/#research-posters" class="sidebar-link">Research Posters</a></li><li class="sidebar-sub-header"><a href="/#demo-videos" class="sidebar-link">Demo Videos</a></li><li class="sidebar-sub-header"><a href="/#research-paper" class="sidebar-link">Research Paper</a></li></ul></li><li><a href="/#career-outcomes" class="sidebar-link">Career Outcomes</a><ul class="sidebar-sub-headers"><li class="sidebar-sub-header"><a href="/#hiring-companies" class="sidebar-link">Hiring Companies</a></li><li class="sidebar-sub-header"><a href="/#graduate-programs" class="sidebar-link">Graduate Programs</a></li></ul></li><li><a href="/#frequently-asked-questions" class="sidebar-link">Frequently Asked Questions</a><ul class="sidebar-sub-headers"></ul></li><li><a href="/#contact" class="sidebar-link">Contact</a><ul class="sidebar-sub-headers"></ul></li></ul></section></li></ul> </aside> <main class="home"><header class="hero"><img src="/logo-banner-FIRE-COML.png" alt="hero"> <!----> <!----> <!----></header> <div class="features"><div class="feature"><h2>WHAT WE DO</h2> <p>We help students learn real, employable skills by working on projects in deep learning and AI using recently developed techniques, perspectives, and apply them to market-relevant areas such as computer vision, NLP, and data analytics.</p></div><div class="feature"><h2>WHY IT MATTERS</h2> <p>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.</p></div><div class="feature"><h2>WHAT YOU LEARN</h2> <p>You will learn to analyze and apply state-of-the-art techniques from recent scholarly research and open-source repositories to perform data preprocessing, modeling, training, evaluation, deployment of machine learning models, and communicating them to a broad audience.</p></div></div> <div class="theme-default-content custom content__default"><a href="/poster-FIRE-COML-Stream-Information-Poster.pdf"><img loading="lazy" width="100%" src="/poster-FIRE-COML-Stream-Information-Poster.webp" alt="FIRE Capital One Machine Learning Poster" style="border:1px solid #ccc;"></a> <h2 id="curriculum-overview"><a href="#curriculum-overview" class="header-anchor">#</a> Curriculum Overview</h2> <details><summary class="viewmore">ML/AI Education</summary> <ul><li>Learn ML/AI topics from lectures and readings.</li> <li>Practice coding with ML/AI tutorials.</li> <li>Experience small group mentoring with the faculty leader and peer research mentors.</li> <li>Discuss recent ML/AI research developments, projects, topics, and career opportunities.</li></ul></details> <details><summary class="viewmore">Hands-on Experience</summary> <ul><li>Engage in a small-group research project based on the agile process.</li> <li>Find, analyze, process, and visualize datasets or simulation environments.</li> <li>Analyze and apply ML/AI models and techniques from scholarly publications and code repositories.</li> <li>Design, build, train, and test neural network models for potential real-world applications.</li> <li>Showcase work at the end of the program with visual and demo presentations.</li></ul></details> <details><summary class="viewmore">Personal Growth</summary> <ul><li>Develop personal accountability and effective work habits.</li> <li>Articulate thoughts and ideas clearly and effectively in written and oral forms.</li> <li>Build collaborative relationships representing diverse cultures, races, ages, genders, religions, lifestyles, and viewpoints.</li> <li>Participate in community and professional development activities with peers.</li> <li>Join seminars and workshops regarding post-graduation preparation and career planning.</li></ul></details> <h2 id="introduction"><a href="#introduction" class="header-anchor">#</a> Introduction</h2> <p><a href="https://umd-fire-coml.github.io/" target="_blank" rel="noopener noreferrer">FIRE Capital One Machine Learning<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> is an undergraduate research program, under the <a href="https://fire.umd.edu/coml" target="_blank" rel="noopener noreferrer">FIRE: First-Year Innovation & Research Experience<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> initiative at the <a href="https://www.umd.edu/" target="_blank" rel="noopener noreferrer">University of Maryland<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>.</p> <div class="custom-block tip"><p class="custom-block-title">NOTE</p> <p>After 5 amazing years, the FIRE Capital One Machine Learning stream and research mentorship program officially ended in December 2022.</p> <p>For other undergrad research opportunities in ML, AI, or Data Science at UMD, please check out the other <a href="https://www.fire.umd.edu/cluster-tas" target="_blank" rel="noopener noreferrer">FIRE Tech & Applied Science<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> streams, the <a href="https://gemstone.umd.edu/" target="_blank" rel="noopener noreferrer">Gemstone Honors Program<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>, the <a href="https://www.cs.umd.edu/projects/reucaar/index.html" target="_blank" rel="noopener noreferrer">REU-CAAR Program<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>, and the research opportunities posted on the <a href="https://mcur.umd.edu/mcur-programs/maryland-student-researchers-database" target="_blank" rel="noopener noreferrer">Maryland Student Researchers Database<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>.</p> <p>If you were an alumnus and would like to stay in touch with your peers, please join our <a href="https://www.linkedin.com/groups/14097215/" target="_blank" rel="noopener noreferrer">Students and Alumni Group<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> on LinkedIn.</p> <p>Thank you Capital One for funding our program, and the leadership team at FIRE for your guidance and administrative support during the program!</p></div> <p>Since our founding in 2018, FIRE Capital One Machine Learning has graduated 100+ undergrad students with degrees in Computer Science, Engineering, Information & Data Science, Mathematics & Statistics, Physical Science, Geographical Information Systems, Computational Biology, and related disciplines.</p> <p>Our mission is to help students develop research expertise and gain career-ready skills in machine learning, deep learning, and artificial intelligence that drives accelerated personal growth and professional development.</p> <p>We specialize in providing mentorship, professional development, and introduction to state-of-the-art research in machine learning. Most of our students had their first-ever research experiences in machine learning, deep learning, and artificial intelligence.</p> <p>We offer a multi-semester, faculty-led, project-focused, mentorship-driven, individual & collaborative, course-based undergraduate research experience for undergraduate students that have joined FIRE and completed their FIRE semester 1 course.</p> <p>Our students experience collaborative work in an agile team, focusing on analyzing, designing, and implementing a machine learning model for market-relevant applications such as computer vision, natural language processing, and data analytics, based on state-of-the-art research and authentic applications of machine learning, deep learning, and artificial intelligence.</p> <p>Our projects provide prototype solutions to real-world problems such as:</p> <ul><li>2D/3D Object Detection</li> <li>Audio Recognition</li> <li>Autonomous Driving</li> <li>Content Recommendation</li> <li>Face Recognition</li> <li>Game Playing</li> <li>Image Colorization</li> <li>Image Generation</li> <li>Image Super-Compression</li> <li>Image Super-Resolution</li> <li>Malware Detection</li> <li>Music Generation</li> <li>Speaker Recognition</li> <li>Speech Generation</li> <li>Text Generation</li> <li>Time-Series Forecasting</li> <li>Video Generation</li> <li>Video Object Tracking</li> <li>and many more.</li></ul> <p>Our projects were developed using state-of-the-art architectures and techniques such as:</p> <ul><li>Convolutional Models</li> <li>Transformer Models</li> <li>Vector Quantization Models</li> <li>Generative Adversarial Networks</li> <li>Deep Q-Learning Networks</li> <li>Representation Learning</li> <li>Hard Example Mining</li> <li>Transfer Learning</li> <li>and etc.</li></ul> <p>Our open-sourced code repositories are implemented in Python using recent frameworks such as:</p> <ul><li>TensorFlow/Keras</li> <li>PyTorch</li> <li>Scikit-Learn</li> <li>Pandas</li> <li>Numpy</li> <li>and many more.</li></ul> <h2 id="our-people"><a href="#our-people" class="header-anchor">#</a> Our People</h2> <h3 id="students-and-alumni"><a href="#students-and-alumni" class="header-anchor">#</a> Students and Alumni</h3> <details open="open"><summary>2022</summary> <img loading="lazy" src="/photo-students-2022.webp" alt="2022 Students"></details> <details><summary>2021</summary> <img loading="lazy" src="/photo-students-2021.webp" alt="2021 Students"></details> <details><summary>2020</summary> <img loading="lazy" src="/photo-students-2020.webp" alt="2020 Students"></details> <details><summary>2019</summary> <img loading="lazy" src="/photo-students-2019.webp" alt="2019 Students"></details> <details><summary>2018</summary> <img loading="lazy" src="/photo-students-2018.webp" alt="2018 Students"></details> <h3 id="peer-research-mentors"><a href="#peer-research-mentors" class="header-anchor">#</a> Peer Research Mentors</h3> <details open="open"><summary>2022</summary> <div class="person-div"><img loading="lazy" src="/photo-siyuan-peng.webp" class="person-img"> <div class="person-name">Siyuan Peng</div></div> <div class="person-div"><img loading="lazy" src="/photo-allen-tu.webp" class="person-img"> <div class="person-name">Allen Tu</div></div> <div class="person-div"><img loading="lazy" src="/photo-jacob-ginzberg.webp" class="person-img"> <div class="person-name">Jacob Ginzberg</div></div> <div class="person-div"><img loading="lazy" src="/photo-priyanka-mehta.webp" class="person-img"> <div class="person-name">Priyanka Mehta</div></div> <div class="person-div"><img loading="lazy" src="/photo-sagar-saxena.webp" class="person-img"> <div class="person-name">Sagar Saxena</div></div> <div class="person-div"><img loading="lazy" src="/photo-siddhesh-gupta.webp" class="person-img"> <div class="person-name">Siddhesh Gupta</div></div> <div class="person-div"><img loading="lazy" src="/photo-jonathan-g.webp" class="person-img"> <div class="person-name">Jonathan Ginsberg</div></div> <div class="person-div"><img loading="lazy" src="/photo-matthew-s.webp" class="person-img"> <div class="person-name">Matthew Snyder</div></div> <div class="person-div"><img loading="lazy" src="/photo-rithvik-b.webp" class="person-img"> <div class="person-name">Rithvik Bobbili</div></div> <div class="person-div"><img loading="lazy" src="/photo-varun-a.webp" class="person-img"> <div class="person-name">Varun Ayyappan</div></div></details> <details><summary>2021</summary> <div class="person-div"><img loading="lazy" src="/photo-derek-zhang.webp" class="person-img"> <div class="person-name">Derek Zhang</div></div> <div class="person-div"><img loading="lazy" src="/photo-joshua-lo.webp" class="person-img"> <div class="person-name">Rung-Chuan (Joshua) Lo</div></div> <div class="person-div"><img loading="lazy" src="/photo-richard-gao.webp" class="person-img"> <div class="person-name">Richard Gao</div></div> <div class="person-div"><img loading="lazy" src="/photo-siyuan-peng.webp" class="person-img"> <div class="person-name">Siyuan Peng</div></div> <div class="person-div"><img loading="lazy" src="/photo-allen-tu.webp" class="person-img"> <div class="person-name">Allen Tu</div></div> <div class="person-div"><img loading="lazy" src="/photo-jacob-ginzberg.webp" class="person-img"> <div class="person-name">Jacob Ginzberg</div></div> <div class="person-div"><img loading="lazy" src="/photo-priyanka-mehta.webp" class="person-img"> <div class="person-name">Priyanka Mehta</div></div> <div class="person-div"><img loading="lazy" src="/photo-sagar-saxena.webp" class="person-img"> <div class="person-name">Sagar Saxena</div></div> <div class="person-div"><img loading="lazy" src="/photo-siddhesh-gupta.webp" class="person-img"> <div class="person-name">Siddhesh Gupta</div></div> <div class="person-div"><img loading="lazy" src="/photo-siyao-li.webp" class="person-img"> <div class="person-name">Siyao Li</div></div></details> <details><summary>2020</summary> <div class="person-div"><img loading="lazy" src="/photo-derek-zhang.webp" class="person-img"> <div class="person-name">Derek Zhang</div></div> <div class="person-div"><img loading="lazy" src="/photo-joshua-lo.webp" class="person-img"> <div class="person-name">Rung-Chuan (Joshua) Lo</div></div> <div class="person-div"><img loading="lazy" src="/photo-jessica-qin.webp" class="person-img"> <div class="person-name">Jessica Qin</div></div> <div class="person-div"><img loading="lazy" src="/photo-richard-gao.webp" class="person-img"> <div class="person-name">Richard Gao</div></div> <div class="person-div"><img loading="lazy" src="/photo-vladimir-leung.webp" class="person-img"> <div class="person-name">Vladimir Leung</div></div> <div class="person-div"><img loading="lazy" src="/photo-siyuan-peng.webp" class="person-img"> <div class="person-name">Siyuan Peng</div></div></details> <details><summary>2019</summary> <div class="person-div"><img loading="lazy" src="/photo-timothy-lin.webp" class="person-img"> <div class="person-name">Timothy Lin</div></div> <div class="person-div"><img loading="lazy" src="/photo-derek-zhang.webp" class="person-img"> <div class="person-name">Derek Zhang</div></div> <div class="person-div"><img loading="lazy" src="/photo-joshua-lo.webp" class="person-img"> <div class="person-name">Rung-Chuan (Joshua) Lo</div></div> <div class="person-div"><img loading="lazy" src="/photo-jessica-qin.webp" class="person-img"> <div class="person-name">Jessica Qin</div></div></details> <h3 id="faculty-leader"><a href="#faculty-leader" class="header-anchor">#</a> Faculty Leader</h3> <div class="person-div"><a href="https://huahongtu.me"><img loading="lazy" src="/photo-raymond-huahong-tu.webp" class="person-img"></a> <div class="person-name">Dr. Raymond H. Tu</div></div> <h2 id="program-structure"><a href="#program-structure" class="header-anchor">#</a> Program Structure</h2> <p><img loading="lazy" src="/figure-fire-flow.webp" alt="FIRE Capital One Machine Learning Timeline"></p> <p>The FIRE Capital One Machine Learning research mentorship program 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. Each year 35-40 students 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.</p> <p>Other than 1 hour of scheduled class meetings per week, the FIRE Capital One Machine Learning research experience requires each student to commit 4-6 additional lab hours for independent and collaborative activities, meetings, and discussions each week.</p> <img loading="lazy" width="100%" src="/photo-lab-fire-capital-one-machine-learning-2022.webp" alt="FIRE Capital One Machine Learning Lab"> <p>Each student throughout the semester will work individually and collaboratively as a team member of a mentor-advised project. 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 team research progress. Lab hours will focus on 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.</p> <p>During the mid-point of the FIRE Capital One Machine Learning research experience, our students can apply for the <a href="https://www.fire.umd.edu/summer" target="_blank" rel="noopener noreferrer">FIRE Summer<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> program that provides an immersive summer experience that helps students develop leadership capabilities, strengthens relationship with the FIRE Faculty Leader, and builds further stream community. FIRE summer students will be required to fully commit a specific number of hours per week. Each summer student will truly be a dedicated member of a research team during the summer period. The FIRE summer program offers an excellent opportunity to rapidly advance the student's research and professional skills.</p> <p>Upon the completion of the FIRE Capital One Machine Learning research experience, students will also have the opportunity to continue their research and professional development by becoming a <a href="https://www.fire.umd.edu/leadership" target="_blank" rel="noopener noreferrer">peer research mentor<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>. The FIRE leadership program help students improve their leadership skills by providing meaningful impact on another FIRE student's experience in our stream. Peer Research Mentors also receive outstanding recommendation letters that help them succeed in their <a href="https://www.fire.umd.edu/next-steps" target="_blank" rel="noopener noreferrer">next steps<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>, such as be <a href="https://careers.umd.edu/" target="_blank" rel="noopener noreferrer">hired<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> at a company, be recruited as a <a href="http://www.ugresearch.umd.edu/searchnew.php" target="_blank" rel="noopener noreferrer">student researcher<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a>, or be admitted as a <a href="https://gradschool.umd.edu/" target="_blank" rel="noopener noreferrer">graduate student<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a> at our university or beyond.</p> <h3 id="learning-outcomes"><a href="#learning-outcomes" class="header-anchor">#</a> Learning Outcomes</h3> <p>Upon completion of this research experience, students will be able to:</p> <ul><li>Practice knowledge of fundamental concepts and techniques in machine learning, neural networks, and deep learning.</li> <li>Analyze and apply state-of-the-art models and techniques from scholarly publications and code repositories.</li> <li>Find, analyze, process, and visualize datasets or simulation environments.</li> <li>Design, build, train, and test neural network models for applications such as computer vision, natural language processing, data analytics, or automation.</li> <li>Perform data processing, model building, training, optimization, and evaluation using deep learning programming frameworks and high-performance cloud computing instances.</li> <li>Communicate scientific ideas and findings clearly and effectively through reports, data visualizations, and presentations.</li> <li>Build collaborative relationships representing diverse cultures, races, ages, genders, religions, lifestyles, and viewpoints.</li> <li>Demonstrate sound reasoning to analyze issues, make decisions, and overcome problems.</li> <li>Develop personal accountability and effective work habits.</li></ul> <h3 id="course-syllabi"><a href="#course-syllabi" class="header-anchor">#</a> Course Syllabi</h3> <details open="open"><summary>Spring Semester</summary> <iframe loading="lazy" src="https://docs.google.com/presentation/d/e/2PACX-1vT1ggKMrf8l14EgGNuzE-Uqp9IwJc0G6VP8NdI--OVMPAKnEDYQ9oFN2BW8XdqcEwv92dpMzuvI1xLr/embed?start=false&loop=false&delayms=3000" frameborder="0" width="100%" height="100%" allowfullscreen="allowfullscreen" mozallowfullscreen="true" webkitallowfullscreen="true" style="aspect-ratio:10 / 6;"></iframe></details> <details><summary>Fall Semester</summary> <iframe src="https://docs.google.com/presentation/d/e/2PACX-1vTOSFHx_d4DW_P0FQMAWHRIiZDenbz9dAHBuXHropCAWontepl5wsWdCUtP7NlJSHYMyPkpTCsmfXGA/embed?start=false&loop=false&delayms=3000" frameborder="0" width="100%" height="100%" allowfullscreen="allowfullscreen" mozallowfullscreen="true" webkitallowfullscreen="true" style="aspect-ratio:10 / 6;"></iframe></details> <h3 id="class-discussions"><a href="#class-discussions" class="header-anchor">#</a> Class Discussions</h3> <details open="open"><summary>Spring Semester</summary> <iframe loading="lazy" src="https://drive.google.com/embeddedfolderview?id=1yQTALN3sHGsjXQQTPi9nUN9rGuXZqT-O&orderBy=name#list" frameborder="0" width="100%" height="100%" style="aspect-ratio:10 / 6;"></iframe></details> <details><summary>Summer Semester</summary> <iframe src="https://drive.google.com/embeddedfolderview?id=0AAni1OkRoldlUk9PVA&orderBy=name#list" frameborder="0" width="100%" height="100%" style="aspect-ratio:10 / 2;"></iframe></details> <details><summary>Fall Semester</summary> <iframe src="https://drive.google.com/embeddedfolderview?id=1WiAXACid89nPoMZUDLGmz6M_DAslaDQM&orderBy=name#list" frameborder="0" width="100%" height="100%" style="aspect-ratio:10 / 6;"></iframe></details> <h3 id="research-projects"><a href="#research-projects" class="header-anchor">#</a> Research Projects</h3> <details open="open"><summary>2022</summary> <ul><li>Music Recommender System</li> <li>Song Identification and Similarity Search</li> <li>Text-Prompted Music Generation</li> <li>Atari Game Playing</li> <li>Text to Talking Face Generation</li></ul></details> <details><summary>2021</summary> <ul><li>
3D Object Detection
</li> <li>
Image Super-Resolution
</li> <li>
Facial Expression Recognition
</li> <li>
Speaker Recognition
</li> <li>
Image Colorization
</li> <li>
Game Playing
</li> <li>
Music Generation
</li> <li>
Malware Detection Classification
</li> <li>
Monocular Depth Estimation
</li> <li>
Text-to-Image Generation
</li></ul></details> <details><summary>2020</summary> <ul><li>
Object Detection In Aerial Images
</li> <li>
Speech Recognition
</li> <li>
Image Stylization
</li> <li>
Image Super-Resolution
</li> <li>
Medical Object Detection
</li> <li>
Text Generation
</li> <li>
Face Generation
</li> <li>
Game Playing
</li> <li>
Video Object Tracking
</li> <li>
3D Object Detection
</li></ul></details> <details><summary>2019</summary> <ul><li>3D Object Detection and Localization of Camera, LIDAR, and RADAR Objects for Self-Driving Cars</li> <li>Visual Object Detection and Tracking for Surveillance Videos and Self-Driving Cars</li> <li>Extreme Image Compression from Learned Objects for Images and Videos</li> <li>Image Caption Generation for Visually Impaired Users</li></ul></details> <details><summary>2018</summary> <ul><li>Instance-level Object Tracking and Segmentation Across Video Frames</li> <li>Using Neural Networks to Identify Individual Bats of the Myotis Vivesi Species</li> <li>Tracking Bee Identities and Behaviors with Convolutional Neural Networks</li> <li>Improving 9-1-1 Call Operations Efficiency with Natural Language Processing</li> <li>Detecting Driver Drowsiness and Attentiveness Through Facial Recognition</li></ul></details> <h2 id="research-outcomes"><a href="#research-outcomes" class="header-anchor">#</a> Research Outcomes</h2> <h3 id="code-repositories"><a href="#code-repositories" class="header-anchor">#</a> Code Repositories</h3> <p></p><h4>AI Recommends Songs Based On Playlist</h4> <div data-github="umd-fire-coml/2022-t1-convolutional" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Learns by using Transformer model</h4> <div data-github="umd-fire-coml/2022-t2-transformer" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Finds Similar Soundtracks</h4> <div data-github="umd-fire-coml/2022-t3-vector-quantization" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Generates Music based on Genre</h4> <div data-github="umd-fire-coml/2022-t4-generative-adversarial" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Plays Atari Games</h4> <div data-github="umd-fire-coml/2022-t5-deep-q-learning" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Recognizes Speaker Gender</h4> <div data-github="umd-fire-coml/2021-Speaker-Recognition" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Detects 3D Objects</h4> <div data-github="umd-fire-coml/2021-3D-Object-Detection" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Predicts Facial Age and Gender</h4> <div data-github="umd-fire-coml/2021-Facial-Age-Gender-Recognition" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Detects Malware</h4> <div data-github="umd-fire-coml/2021-Malware-Detection-Classification" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Plays Super Mario Bros</h4> <div data-github="umd-fire-coml/2021-Game-Playing" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Colorizes Grayscale Images</h4> <div data-github="umd-fire-coml/2021-Image-Colorization" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Generates Bird Images</h4> <div data-github="umd-fire-coml/2021-Text-to-Image-Generation" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Estimates Depth in RGB Image</h4> <div data-github="umd-fire-coml/2021-Monocular-Depth-Estimation" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Generates Music</h4> <div data-github="umd-fire-coml/2021-Music-Generation" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <p></p><h4>AI Enhances Low-Resolution Images</h4> <div data-github="umd-fire-coml/2021-Image-Super-Resolution" data-width="100%" data-theme="default" class="github-card"></div> <p></p> <script async="async" src="https://cdn.jsdelivr.net/github-cards/latest/widget.js"></script> <a href="https://github.com/umd-fire-coml?q=2020-&type=all&language=&sort=name">See more on GitHub</a> <h3 id="research-posters"><a href="#research-posters" class="header-anchor">#</a> Research Posters</h3> <a href="/poster-image-colorization-2022.pdf"><img loading="lazy" src="/poster-image-colorization-2022.webp" alt="Image Colorization" style="border:1px solid #ccc;"></a> <br> <br> <a href="/poster-autonomous-driving-2022.pdf"><img loading="lazy" src="/poster-autonomous-driving-2022.webp" style="border:1px solid #ccc;"></a> <br> <br> <a href="/poster-game-playing-2022.pdf"><img loading="lazy" src="/poster-game-playing-2022.webp" style="border:1px solid #ccc;"></a> <br> <br> <a href="/poster-speech-to-face-animation-2022.pdf"><img loading="lazy" src="/poster-speech-to-face-animation-2022.webp" style="border:1px solid #ccc;"></a> <br> <br> <a href="/poster-rotational-representation-2022.pdf"><img loading="lazy" src="/poster-rotational-representation-2022.webp" style="border:1px solid #ccc;"></a> <br> <br> <a href="/poster-3D-DETECTION-FIRE-SUMMIT-2019.pdf"><img loading="lazy" src="/poster-3D-DETECTION-FIRE-SUMMIT-2019.webp" alt="3D Object Detection and Localization"></a> <br> <br> <a href="/poster-TRACKING-FIRE-SUMMIT-2019.pdf"><img loading="lazy" src="/poster-TRACKING-FIRE-SUMMIT-2019.webp" alt="Object Tracking"></a> <br> <br> <a href="/poster-IMAGE-COMPRESSION-FIRE-SUMMIT-2019.pdf"><img loading="lazy" src="/poster-IMAGE-COMPRESSION-FIRE-SUMMIT-2019.webp" alt="Image Compression"></a> <br> <br> <img loading="lazy" src="/poster-MULTISEG.UGRD-2019.png" alt="Muliple Object Segmentation" style="border:1px solid #ccc;"> <br> <br> <img loading="lazy" src="/poster-NUSCENES.UGRD-2019.png" alt="Nuscenes 3D Detection" style="border:1px solid #ccc;"> <br> <br> <img loading="lazy" src="/poster-BATCALL.UGRD-2019.png" alt="Bat Call Identification" style="border:1px solid #ccc;"> <br> <br> <h3 id="demo-videos"><a href="#demo-videos" class="header-anchor">#</a> Demo Videos</h3> <div><blockquote 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style="background-color:#F4F4F4;border-radius:4px;flex-grow:0;height:14px;width:60px;"></div></div></div><div style="padding:19% 0;"></div> <div style="display:block;height:50px;margin:0 auto 12px;width:50px;"><svg width="50px" height="50px" viewBox="0 0 60 60" version="1.1" xmlns="https://www.w3.org/2000/svg" xmlns:xlink="https://www.w3.org/1999/xlink"><g stroke="none" stroke-width="1" fill="none" fill-rule="evenodd"><g transform="translate(-511.000000, -20.000000)" fill="#000000"><g><path d="M556.869,30.41 C554.814,30.41 553.148,32.076 553.148,34.131 C553.148,36.186 554.814,37.852 556.869,37.852 C558.924,37.852 560.59,36.186 560.59,34.131 C560.59,32.076 558.924,30.41 556.869,30.41 M541,60.657 C535.114,60.657 530.342,55.887 530.342,50 C530.342,44.114 535.114,39.342 541,39.342 C546.887,39.342 551.658,44.114 551.658,50 C551.658,55.887 546.887,60.657 541,60.657 M541,33.886 C532.1,33.886 524.886,41.1 524.886,50 C524.886,58.899 532.1,66.113 541,66.113 C549.9,66.113 557.115,58.899 557.115,50 C557.115,41.1 549.9,33.886 541,33.886 M565.378,62.101 C565.244,65.022 564.756,66.606 564.346,67.663 C563.803,69.06 563.154,70.057 562.106,71.106 C561.058,72.155 560.06,72.803 558.662,73.347 C557.607,73.757 556.021,74.244 553.102,74.378 C549.944,74.521 548.997,74.552 541,74.552 C533.003,74.552 532.056,74.521 528.898,74.378 C525.979,74.244 524.393,73.757 523.338,73.347 C521.94,72.803 520.942,72.155 519.894,71.106 C518.846,70.057 518.197,69.06 517.654,67.663 C517.244,66.606 516.755,65.022 516.623,62.101 C516.479,58.943 516.448,57.996 516.448,50 C516.448,42.003 516.479,41.056 516.623,37.899 C516.755,34.978 517.244,33.391 517.654,32.338 C518.197,30.938 518.846,29.942 519.894,28.894 C520.942,27.846 521.94,27.196 523.338,26.654 C524.393,26.244 525.979,25.756 528.898,25.623 C532.057,25.479 533.004,25.448 541,25.448 C548.997,25.448 549.943,25.479 553.102,25.623 C556.021,25.756 557.607,26.244 558.662,26.654 C560.06,27.196 561.058,27.846 562.106,28.894 C563.154,29.942 563.803,30.938 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<div style="background-color:#F4F4F4;border-radius:4px;flex-grow:0;height:14px;width:144px;"></div></div></a><p style="color:#c9c8cd;font-family:Arial,sans-serif;font-size:14px;line-height:17px;margin-bottom:0;margin-top:8px;overflow:hidden;padding:8px 0 7px;text-align:center;text-overflow:ellipsis;white-space:nowrap;"><a href="https://www.instagram.com/tv/Cgci3LWK9eW/?utm_source=ig_embed&utm_campaign=loading" target="_blank" style="color:#c9c8cd;font-family:Arial,sans-serif;font-size:14px;font-style:normal;font-weight:normal;line-height:17px;text-decoration:none;">A post shared by UMD FIRE (@umdfire)</a></p></div></blockquote> <script async="async" src="//www.instagram.com/embed.js"></script></div> <br> <br> <div><iframe loading="lazy" src="https://www.youtube.com/embed/p893AIFZmdM" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" frameborder="0" width="100%" height="100%" allowfullscreen="allowfullscreen" mozallowfullscreen="true" webkitallowfullscreen="true" style="aspect-ratio:16 / 9;"></iframe></div> <br> <br> <div><iframe loading="lazy" src="https://www.youtube.com/embed/2MaxElaNBO0" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" frameborder="0" width="100%" height="100%" allowfullscreen="allowfullscreen" mozallowfullscreen="true" webkitallowfullscreen="true" style="aspect-ratio:16 / 9;"></iframe></div> <br> <br> <div><iframe loading="lazy" src="https://www.youtube.com/embed/XvMK9od52J0" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" frameborder="0" width="100%" height="100%" allowfullscreen="allowfullscreen" mozallowfullscreen="true" webkitallowfullscreen="true" style="aspect-ratio:16 / 9;"></iframe></div> <br> <br> <div><iframe loading="lazy" src="https://www.youtube.com/embed/GSBCkKRRw1Y" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" frameborder="0" width="100%" height="100%" allowfullscreen="allowfullscreen" mozallowfullscreen="true" webkitallowfullscreen="true" style="aspect-ratio:16 / 9;"></iframe></div> <br> <br> <h3 id="research-paper"><a href="#research-paper" class="header-anchor">#</a> Research Paper</h3> <iframe loading="lazy" src="https://docs.google.com/viewer?url=https://arxiv.org/pdf/2112.04421&embedded=true" frameborder="0" width="100%" height="100%" style="aspect-ratio:1 / 1;"></iframe> <br> <br> <h2 id="career-outcomes"><a href="#career-outcomes" class="header-anchor">#</a> Career Outcomes</h2> <p>We are proud to share that many of our students and alums have acquired professional careers or have pursued Master's/PhD programs at institutions that will further enhance the prospects for their future careers.</p> <h3 id="hiring-companies"><a href="#hiring-companies" class="header-anchor">#</a> Hiring Companies</h3> <p>These are some of the companies that have hired or have made professional job offers to our students:</p> <ul><li>Capital One</li> <li>Accenture</li> <li>Align Technology</li> <li>Amazon/AWS</li> <li>Applied Physics Laboratory</li> <li>Army Research Laboratory</li> <li>Bank of America</li> <li>Clark Construction Group</li> <li>Coinbase</li> <li>Databricks</li> <li>Discovery</li> <li>Environmental Protection Agency</li> <li>Epic Systems</li> <li>Goldman Sachs</li> <li>Google</li> <li>HSBC</li> <li>Liberty Mutual Insurance</li> <li>Los Alamos National Lab</li> <li>M&T Bank</li> <li>Mastercard</li> <li>Meta/Facebook</li> <li>Microsoft</li> <li>NASA</li> <li>National Institutes of Health</li> <li>Northrop Grumman</li> <li>OneMain Financial</li> <li>Palantir</li> <li>Peraton</li> <li>Prudential Financial</li> <li>Spotify</li> <li>Squarespace</li> <li>Stripe</li> <li>Synopsys</li> <li>Tencent</li> <li>The Aerospace Corporation</li> <li>Tyler Technologies</li> <li>Uber</li> <li>USPS</li></ul> <h3 id="graduate-programs"><a href="#graduate-programs" class="header-anchor">#</a> Graduate Programs</h3> <p>These are some of the universities that have admitted or have made Master's/PhD program offers to our students:</p> <ul><li>University of Maryland</li> <li>Arizona State University</li> <li>Harvard University</li> <li>Johns Hopkins University</li> <li>Michigan State University</li> <li>University of California Irvine</li> <li>University of Massachusetts Amherst</li></ul> <h2 id="frequently-asked-questions"><a href="#frequently-asked-questions" class="header-anchor">#</a> Frequently Asked Questions</h2> <details><summary class="viewmore">What is FIRE Capital One Machine Learning?</summary> <p>FIRE Capital One Machine Learning a multi-semester, faculty-led, project-focused, mentorship-driven, course-based, individual & collaborative, undergraduate research program for undergraduate students that have joined FIRE and completed their FIRE semester 1 course.</p></details> <details><summary class="viewmore">How do I join FIRE Capital One Machine Learning?</summary> <p>The FIRE Capital One Machine Learning research experience is meant for FIRE students that have completed their FIRE Semester 1 during their first fall semester at UMD. During their FIRE Semester 1 course, students will be able to complete a stream preferencing form to indicate their interest in joining a FIRE stream. Students assigned to FIRE Capital One Machine Learning will have a chance to join our stream by registering for our course during the initial spring registration period.</p> <p>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 Semester 1, please find the program application information <a href="https://fire.umd.edu/open-house/" target="_blank">here</a>.</p></details> <details><summary class="viewmore">How is this program different compared to an AI/ML course or bootcamp?</summary> <p>We provide a research mentorship program that is different from an AI/ML course or bootcamp in the following ways:</p> <ul><li>Multi-Semester: We understand that most courses end within a semester and do not provide a runway for undergrad students to fully utilize the knowledge to create and reiterate. We provide students with a multi-semester program that spans over a year to give them enough runway to transition from trainee to experienced practitioner. After the required one year of research experience, our students also have the opportunity to continue as peer research mentors to help them further strengthen their technical expertise and develop their leadership skills until the end of their undergrad studies.</li> <li>Learn-to-Create: Instead of focusing on performing well on tests or exams, our students are trained to become product creators by driving their research projects to completion and communicating their results to a broad audience.</li> <li>Mentorship & Collaboration: Unlike a lecture-driven course, our students spend more time in the lab for a hands-on experience of individual & collaborative work, and regularly receive mentorship on their work.</li> <li>Personal Growth: In addition to developing technical skills, our students also learn to develop professional skills through seminars, community events, collaboration, communication, mentorship, effective work habits, and personal accountability.</li> <li>Diversity: Our students come from diverse backgrounds. We understand not everyone has the privilege of having a head start in life. Our students are not selected based on the "best" prior experience, we instead focus more on creating a cohort of students with diverse backgrounds.</li></ul></details> <details><summary class="viewmore">Are students expected to have prior knowledge of AI/ML or coding?</summary> <p>FIRE Capital One Machine Learning is meant to be an inclusive program for all types of undergraduate students. Having some computer programming/coding or computer science experience is a plus, but no prior knowledge or experience in machine learning, artificial intelligence, programming/coding, or computer science is required. Most of our students had their first-ever research experiences in machine learning or coding. All you need is curiosity around these topics and a STRONG desire to learn!</p></details> <details><summary class="viewmore">What kind of commitment (in hours/week) is required to participate in FIRE Capital One Machine Learning?</summary> <p>The FIRE Capital One Machine Learning undergraduate research program is meant to be an in-person and deeply immersive research experience for our students. Other than 1 hour of scheduled class meetings per week, participating in the FIRE Capital One Machine Learning research experience requires the student to commit 4-6 additional lab hours for independent and collaborative activities, meetings, and discussions each week.</p></details> <details><summary class="viewmore">Are students expected to supply their own computers with an Nvidia GPU?</summary> <p>Students will need to have a working laptop/computer with reliable internet access. Most of our work are done with resources on the cloud. We do not require students to have a Nvidia GPU installed on their computers.</p></details> <details><summary class="viewmore">What is the program's stance on diversity, equity, and inclusion?</summary> <p>FIRE Capital One Machine Learning strives to create a diverse, equitable, and inclusive learning environment for students from all walks of life. We welcome students historically underprivileged and underrepresented in the computer science field, including, but not limited to:</p> <ul><li>Women.</li> <li>People of LGBTQ+.</li> <li>Families of low socioeconomic status.</li> <li>Indigenous Peoples, Black, Hispanic or Latinx, Pacific Islander, and Southeast Asian.</li> <li>Raised in a home where English was a second language.</li> <li>Raised in a single-parent household.</li> <li>Future first-generation college students.</li></ul></details> <h2 id="contact"><a href="#contact" class="header-anchor">#</a> Contact</h2> <p>Contact: <a href="https://huahongtu.me" target="_blank" rel="noopener noreferrer">Dr. Raymond H. Tu<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a><br>
LinkedIn: <a href="https://www.linkedin.com/groups/14097215/" target="_blank" rel="noopener noreferrer">Students and Alumni Group<span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg> <span class="sr-only">(opens new window)</span></span></a></p></div> <div class="footer">
FIRE Capital One Machine Learning is an undergraduate research program under the First-Year Innovation & Research Experience initiative at the University of Maryland.
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