AI Summer Program
AI Summer Program
The Iribe Initiative for Inclusion and Diversity at the University of Maryland, College Park is pleased to offer an AI Summer Program experience at the University of Maryland.
About
The AI Summer Program is a two-week long, nonresidential computer programming and artificial intelligence (AI) summer camp at the University of Maryland. Students will come away from the camp knowing how AI can be used to help people and an idea of what kinds of careers there are in AI. Accepted students will have to complete ~25 hours of asynchronous content prior to the start date.
The camp will be offered to rising 10th, 11th, and 12th graders. Students will be exposed to personal growth, education, and hands-on experiences presented by faculty, guest lecturers, and University of Maryland students.
This program intends to create a more inclusive and diverse field of artificial intelligence by targeting and serving underrepresented communities. Students will be given the opportunity to use artificial intelligence to address problems of a probabilistic and numeric nature. Participants will explore the field of AI through team projects, industry field trips, and presentations from guest speakers. There will also be opportunities to engage with faculty, staff and researchers who have been leaders in AI. Students will be exposed to a breadth of knowledge in the field with the goal of leveraging AI for social good.
The program will focus on three aspects:
- AI education and inspiration
- Personal growth
- Hands-on research experience.
Typical Camp Day
- 9:00-12:00pm Classroom Instruction*
12:15-1:15pm Lunch
1:30-5:15pm Classroom Instruction
*Field trips and guest speakers are scheduled during Classroom Instruction time blocks.
Lab Meetings will take place in the Brendan Iribe building.
AI Education
- Formal AI education curriculum instruction by a local AI high school teacher
- Guest Speakers by UMD professors and industry professionals
- In-depth introduction to ongoing research projects from faculty
- Field trip to AI industry leaders where students are introduced to people, topics and career opportunities
Personal Growth
- Discussions lead by experts in career and personal development
- Small group mentoring with AI faculty and graduate students
- Social events with peers
Hands-on Experience
- Small-group research project led by faculty or graduate students; projects focus on using AI for societal good
- Group presentations showcasing work at the end of the program
- Applicant must be able to attend both weeks.
- Applicant must be a rising 10th, 11th, or 12th grader.
- Applicant will be required to submit family and student information.
- Financial Assistance is available for those with displayed need by completing our Scholarship Application.
- Must submit academic transcripts (unofficial transcripts are welcome for application review).
- Emails for teacher recommendations are required
- I4C's AI Summer Program is no longer affiliated with AI4ALL as of 2023. For more information, please visit: https://medium.com/ai4allorg/changes-at-ai4all-a-message-from-ai4alls-ce...
Dates & Links
Program Date and Quick Info
Dates: July 10- July 21, 2023
2-week nonresidential program experience
Target Student: Rising 10th, 11th, and 12th graders (focus on the DC, MD, and VA areas)
2023 I4C Summer Academy applications are now live!
Apply at: go.umd.edu/hsapp23
Need-based scholarships are avaialble for eligible students living in D.C., Virginia, and Maryland: go.umd.edu/need23
Projects
2020 Projects
Generative Adversarial Networks (GANs) is a modern machine learning technique to produce realistic but fake samples. GANs have two AI components, often modeled as deep neural networks, that play a zero-sum game to improve each other’s performance.
Learning a probability model from data is a key challenge in machine learning and statistics. A classical approach to this problem is to fit (approximately) an explicit probability model to the training data via a maximum likelihood estimation. However, there has been another approach to this problem using Generative Adversarial Networks (GANs). GANs view this problem as a game between two sets of functions: a generator whose goal is to generate fake samples that are close to the real data training samples and a discriminator whose goal is to distinguish between the real and fake samples. In this project, we aim to train some of the state-of-the-art GAN architectures on image datasets and produce fake but believable images. Through this, students will gain some familiarity with Python and PyTorch.
Researchers
Assistant Professor, UMD Department of Computer Science
Graduate Student
People can glance at an object and identify it. Now we are understanding how to give computers the same ability. Visual object recognition can have an impact on a wide variety of real world problems. Recognizing objects in images can allow us to organize personal photos or search for images on the internet based on their content. Recognizing the species of plants and animals in images can assist biologists in biodiversity.studies. Recognizing microbes or different cell types can help in many medical applications. This project will show how current computer vision systems are able to recognize objects in images.
Researchers
Professor, UMD Department of Computer Science
Graduate Student
Smartphone cameras have improved a lot in recent years, and a key reason is that they use more and more sophisticated, AI-based image processing. In this project you will learn how AI can improve your photographs, and you will experiment with AI-based techniques to remove graininess and increase image resolution.
Researchers
Acting Chair, Reginald Allan Hahne Endowed E-nnovate Professorship, UMD Department of Computer Science
Graduate Student, UMD Department of Computer Science
Imagine that you are searching for a lost person in a forested environment. Or you are a first-responder searching for a survivor to rescue after a disaster. Searching for people or objects in the physical world is an important task in many societally important scenarios. In many of these scenarios, robots that are equipped with appropriate sensors (e.g., RGB and thermal cameras) can be used as scouts. These robots can assist human searchers, multiply their capabilities since they can operate tirelessly, and can reduce the risk to human lives especially when searching in unknown, potentially dangerous environments. In this project, we will develop algorithms for efficiently searching with a team of robots. https://youtu.be/BjJ2IhITya8
Researchers
Assistant Professor, UMD Department of Computer Science
Graduate Student
Graduate Student
Register
2023 I4C Summer Academy applications are now live!
Apply at: go.umd.edu/hsapp23
Need-based scholarships are avaialble for eligible students living in D.C., Virginia, and Maryland: go.umd.edu/need23