A Premier Opportunity for Future technology gurus

Program Highlights

  • PhD-Level Projects: Tackle advanced challenges like computer vision, generative models, and reinforcement learning.
  • Expert-Led Curriculum: Learn from Princeton PhDs and industry leaders in computer science technology and research.
  • Hands-On Training: Master research and problem-solving through immersive labs and team projects.
  • Industry-Leading Judges: Showcase your work to top experts and Princeton professors for valuable feedback.
  • Ongoing Support: Outstanding projects will receive mentorship, funding, and partnership opportunities.

Computer Science Bootcamp

Program Details

  • Early Registration Deadline: January 31, 2026
  • Location: Princeton University, NJ, USA
  • Duration: 28 Days (June 16 – July 14, 2026)
  • Tuition: $5,999
  • Student-to-Staff Ratio: 10:1
  • Total Internship Hours: 126
  • Food and Housing: Dining Hall & Princeton Dorms(Double Occupancy)
  • Requirements: Successful completion of the screening quiz exam

 

The Computer Science Bootcamp is an elite program designed for advanced high school and college students passionate about technology and innovation. This 28-day experience combines cutting-edge technical training with real-world applications, hosted at Princeton University under the guidance of renowned experts.

Advanced Technical Projects: Participants collaborate on groundbreaking topics such as computer vision, generative models, and reinforcement learning. Through hands-on lab work and project-based learning, students develop skills directly relevant to the evolving computer science field.

A Competitive Edge for Future Leaders: The internship provides unparalleled exposure to advanced research methodologies and teamwork, preparing participants for top-tier universities and careers in computer science. The program concludes with professional presentations to industry leaders, Princeton professors, and experts, offering opportunities for recognition, recommendations, and even funding.

Selective Admission Process: Only highly motivated students with exceptional credentials are admitted. Applicants must pass a rigorous selection process, including the AP Computer Science exam, an online assessment, and a technical interview to ensure readiness for this immersive experience.

Join the Computer Science Bootcamp to challenge yourself, innovate in technology, and take a decisive step toward a future in advenced technology.


Program Outcomes

  1. Stand Out from the Crowd
Gain a competitive edge over ordinary applicants with advanced project experience and technical skills.
    1. Real-World-Ready Mindset
    Learn how to conduct independent research, approach complex projects, and create innovative solutions. Develop the skills to find collaborators and resources to bring ideas to life.
      1. Elite Peer Network
      Connect with like-minded, high-achieving peers. Form strong teams and start working on impactful projects early in your academic and professional journey.
        1. Continuous Support from Xtatic Community
        Access ongoing mentorship, resources, and collaboration opportunities through the Xtatic alumni network.
          1. Personal Growth and Industry Insights
          Understand the world of computer science and high-tech industries, explore emerging trends, and prepare for future opportunities with a proactive mindset.
            1. Complementary College Counseling
            Receive personalized college counseling to refine your applications and align your achievements with your career aspirations.


              Who Should Apply

              Anyone with a strong interest in Computer Science and a passion for pursuing a future in technology. Scholarships are available for exceptional candidates and families needing financial assistance—please refer to the Apply for Financial Aid section for more information. To be eligible, students must meet the following criteria:

              1. Pass the Screening Quiz
              Demonstrate proficiency in Python or C++ through a rigorous screening quiz.
              1. Highly Self-Motivated
              Exhibit a strong drive to learn, innovate, and excel in a challenging, fast-paced environment.
              1. Team Player
              Showcase excellent collaboration skills and the ability to work effectively within a team.

                 

                What’s included?

                • Education Material: textbooks and other supplies
                • Lectures, lab, and excursion (20%)
                • Local Transportation: from airport to Princeton or vice versa
                • Housing: living in the Princeton University Dorms
                • Food: eating at the Princeton University Dining Halls
                • 24/7 Parent Support and In-Class Supervision Mode
                • Travel and Medical Insurance: Provided and included in the program fee. Please see Insurance Policy for more details.
                • Pre-departur Support: Comprehensive resources and assistance to prepare for the program.


                  Course Structure

                  Register Now For Our Info Sessions On

                  • 12/20/2025, 4:00 PM PDT – International Healthcare Internship
                  • 1/10/2026, 4:00 PM PDT – Computer Science Bootcamp (Princeton University, NJ)

                  Register for Info Session

                  Xtatic Exclusive Offerings

                  The program offers professional content in four key areas

                  All programs include direct engagement with real-world AI challenges. Under the guidance and mentorship of Princeton University’s PhD and post-PhD experts, students will tackle advanced AI projects in fields like healthcare and sustainability. This hands-on experience mirrors real-life applications, equipping participants with practical skills and authentic insights into cutting-edge AI solutions.

                  Apply now

                  Neural Networks and Deep Learning

                  Participants will gain an in-depth understanding of how neural networks work, including:

                  • Core Concepts: Learn the architecture of neural networks, covering neurons, layers, activation functions (e.g., ReLU, sigmoid), and loss functions. Explore how data flows through the network and how backpropagation adjusts weights during training.
                  • Practical Applications: Build feedforward neural networks for classification tasks and convolutional neural networks (CNNs) for image recognition. Real-world examples include medical imaging (e.g., classifying X-ray images) and handwritten digit recognition (MNIST dataset).
                  • Cloud-Based AI Solutions: Use TensorFlow/Keras in cloud-based environments like Google Colab with GPU acceleration to train models efficiently.

                  Cloud-Based AI Solutions

                  Participants will harness the power of cloud platforms for developing AI solutions:

                  • Cloud Platforms: Get hands-on experience with Google Colab, AWS SageMaker, and Azure ML Studio. Learn how to set up accounts, activate GPU runtimes, and configure essential libraries (e.g., TensorFlow, Scikit-learn).
                  • Advantages: Explore the benefits of scalability, accessibility, and cost-effectiveness that cloud computing offers for AI applications.
                  • Use Cases: Train machine learning models, preprocess datasets, and deploy AI solutions directly from the cloud. Projects include creating simple web apps for prediction tasks using Streamlit or Flask.

                  Generative AI and Transfer Learning

                  Participants will explore advanced AI methods that reduce the need for extensive training data:

                  • Generative AI: Understand the architecture and applications of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Hands-on labs include training a GAN to generate synthetic images, such as handwritten digits.
                  • Transfer Learning: Learn how to fine-tune pre-trained models like ResNet (for image classification) and BERT (for natural language processing) to solve domain-specific problems. Examples include classifying lung X-rays for pneumonia detection.
                  • Cloud Integration: Use TensorFlow Hub and cloud GPUs for efficient implementation and training of these advanced models.

                  Real-World AI Projects

                  The program emphasizes applying AI skills to impactful projects:

                  • Healthcare Focus: Work with datasets like UCI Heart Disease or Diabetes Prediction to build classification models that predict health outcomes. Learn preprocessing techniques such as handling missing data and scaling features, and deploy solutions as user-friendly web apps.
                  • Sustainability Focus: Analyze sustainability datasets, such as satellite images for deforestation or energy consumption data. Develop machine learning models for segmentation, forecasting, or other tasks, and deploy the solutions using cloud-hosted APIs.
                  • Comprehensive Lifecycle: Participants will experience the entire project lifecycle, including dataset preprocessing, model building, evaluation (using metrics like precision and recall), deployment, and presentation.

                  Why Choose Us

                  Advanced Program for High Achievers

                  Tailored for students with a strong foundation in computer science or programming, our program challenges participants to go beyond the basics and master advanced AI concepts.

                  World-Class Instructors

                  Learn from Princeton University’s PhD and post-PhD scholars specializing in AI and computer science. Our instructors provide expert guidance on advanced methodologies, instill a mastermind mindset for tackling complex projects, and mentor students on their academic journeys.

                  State-of-the-Art Resources and Facilities

                  Experience the world-class facilities of Princeton University, including high-tech classrooms, labs, and dormitories. Leverage cloud platforms like Google Colab, AWS SageMaker, and Azure ML Studio for scalable, GPU-accelerated AI development.

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