The Paul English Applied Artificial Intelligence Institute's (PEAAII) 2025 High School Summer Camp

Time:
August 4th 2025 - August 22nd 2025
Monday - Friday, 9:00 AM - 4:00 PM

Elligibility:
Students rising to Grades 8-12 and rising college freshman

Work alongside leading AI professors doing cutting-edge research. In this 1–3 week camp, students will build real AI projects and explore the future of science and technology.

Tuition:
$800 (1 week) | $1500 (2 weeks) | $2100 (3 weeks)

Top projects may earn mentorship from Paul English and support from the Venture Development Center at UMass Boston.

Build boldly. Think deeply. Start your journey into AI.

Interested students can register for our summer camp in intervals of either one week, two weeks, or all three weeks. We are also offering a special AI event day for Friday, August 22nd, the last day of the camp, which you may choose to sign up for this singular day! You can register at the following links:

Summer Camp Schedule & Fees

1-Week Camp ($800)
Choose any one week:
August 4–8, August 11–15, or August 18–22

2-Week Camp ($1500)
Any two weeks within the three-week period

3-Week Full Camp ($2100)
August 4–22

Registration Link 👉 https://forms.office.com/r/a6tiJa63Da

Below, you will find biographical information of those working for and with the PEAAII for our 2025 August summer camp. Each Professor (with the exception of Professor Wei Ding) will have a project idea accompanied with their bio information. Students should read through each project and choose a Professor who they will work with for the duration of the summer camp period to bring their respective project to life.

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Professor Wei Ding, Executive Director of the PEAAII

Professor Ding's Bio:

Wei Ding is the Executive Director of the Paul English Applied AI Institute and a Distinguished Professor in the College of Science and Mathematics at UMass Boston. She holds a BS in Computer Science and Applications from Xi’an Jiaotong University, an MSc in Software Engineering from George Mason University, and a PhD in Computer Science from the University of Houston. Since joining UMass Boston in 2008, her research has focused on knowledge discovery and AI for science. From 2019 to 2023, she served as a Program Director in the Division of Information and Intelligent Systems at the NSF, receiving the NSF Director’s Award in 2022. Her honors include the 2019 WISAY Distinguished Woman in Science Award from Yale, the 2018 Outstanding Alumni Award, and the Best PhD Work Award from the University of Houston. Her research has been funded by NSF, NIH, NASA, and DOE. She is a Fellow of IEEE and AAIA, and a Senior Member of ACM.

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Professor Ping Chen, Professor of Computer Engineering and Director of AI Lab at UMass Boston

Professor Chen's Bio:

Dr. Ping Chen is a Full Professor of Computer Engineering and the Director of Artificial Intelligence Lab at the University of Massachusetts Boston. His research interests include Artificial Intelligence, Natural Language Processing, and Machine Learning. Dr. Chen has published over 100 papers in major Data Mining, Artificial Intelligence, and Machine Learning conferences and journals.

Professor Chen's Project: AI For Nature: Building a Wildlife Image Classifier

In this 3-week summer camp project, students will explore how artificial intelligence can help protect wildlife through image classification. Participants will learn the basics of machine learning and computer vision by building a simple AI model that can identify different species of animals from photos taken in nature reserves or camera traps. The project starts with an introduction to Python and AI fundamentals, including data labeling, neural networks, and model training. Students will work in teams to collect and clean datasets, train image classifiers using platforms like TensorFlow, and evaluate model performance. They will also explore real-world applications of AI in conservation and reflect on ethical considerations, such as bias in data and the importance of preserving biodiversity. By the end of the camp, students will present their models and findings in a final showcase, demonstrating how AI can be used for positive environmental impact. No prior coding experience is required—just curiosity and a willingness to learn.

Expected Student Outcomes:

Gain hands-on experience in machine learning and image classification using real-world tools.

Understand the role of AI in environmental conservation and ethical implications of technology.

Develop teamwork, problem-solving, and presentation skills through a collaborative final showcase.

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Professor Xiaohui Liang, Associate Professor, Computer Science

Professor Liang's Bio:

Dr. Xiaohui Liang is an Associate Professor of the Computer Science Department at the University of Massachusetts Boston, where he leads the Mobile Computing and Privacy (MobCP) Lab. His research focuses on mobile healthcare, voice AI, wearable computing, and security/privacy. He has published over 100 peer-reviewed papers and received numerous awards. His research has been funded by both NIH and NSF.

Professor Liang's Project: AI Agents for Usable and Accurate Dietary Assessment (UADA)

Are you interested in AI, mobile apps, and making a real impact on older adults' health? Join our exciting 3-week summer camp project to help build the next-generation UADA app—a smart dietary assessment tool that combines Voice AI and Visual AI to help seniors easily track what they eat. Traditional tools like the ASA-24 are long, complicated, and not friendly to older users. Our UADA app is different: it uses natural voice conversations powered by large language models and realistic food visuals generated by cutting-edge AI to make food tracking simple, accurate, and even fun. You’ll get hands-on experience with automatic speech recognition (ASR), LLM customization (like OpenAI’s GPT or Whisper), and image generation models, all while contributing to a meaningful healthcare project. You’ll also learn how AI can be applied to solve real-world problems in geriatric health and human-centered design. Whether you’re into coding, AI, UX/UI design, or just want to explore healthcare technology, this project welcomes students of all backgrounds.

Expected Student Outcomes:

Hands-on Experience with Cutting-Edge AI Tools: Students will gain practical skills in applying Voice AI (automatic speech recognition, large language models) and Visual AI (text-to-image generation) in real-world applications.

Interdisciplinary Collaboration and Problem-Solving: Through teamwork and mentorship, students will tackle real challenges in health tech, combining computer science, human-centered design, and geriatric care.

Portfolio-Ready Project with Social Impact: By the end of the camp, students will have contributed to a working prototype of the UADA app—a project that can be showcased in their resumes, portfolios, or college applications.

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Professor Shichao Pei, Assistant Professor, Computer Science

Professor Pei's Bio:

Dr. Shichao Pei is an Assistant Professor in the Computer Science Department at the University of Massachusetts Boston. His research interests include machine learning, large language models, trustworthy AI systems. His work has been published in top-tier conferences and journals.

Professor Pei's Project: Smart Connections: Building a Friend Recommendation AI

Have you ever wondered how apps like Snapchat or Instagram suggest people you might know? In this 3-week summer camp project, you’ll build your own mini version of that system—powered by AI. By modeling friendships as a network of connections, you’ll train an AI to spot hidden patterns and recommend new friends, just like real-world social platforms do. You’ll begin with a beginner-friendly introduction to Python and AI concepts, including how to work with networks, analyze connections, and build simple predictive models. Then, using real-world data, you’ll design and train your own AI-powered friend recommender. Along the way, you’ll explore powerful tools like NetworkX for network analysis and scikit-learn for model training. Beyond coding, this project also invites you to think critically: What happens when AI influences who we talk to online? Can friend suggestions reinforce echo chambers? You’ll learn not only how these systems work, but also how to build them responsibly. At the end of the camp, you’ll present your personalized system and share what you’ve learned about the intersection of technology, society, and human connection.

Expected Student Outcomes:

Learn how to represent social networks as data and understand how AI can analyze connections to make predictions.

Build their own friend recommendation system and evaluate how well it works.

Explain both the technical process and the social impact of AI-driven recommendations.

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Professor Yinxin Wan, Assistant Professor, Computer Science

Professor Wan's Bio:

Dr. Yinxin Wan is an Assistant Professor of Computer Science at the University of Massachusetts Boston. His research includes secure AI systems, network security, and cyber-physical systems. His research has been published in top-tier journals and conferences.

Professor Wan's Project: AI Model Hub: Building a Universal Gateway for Multiple AI Systems

In this hands-on project, students will design and build an AI gateway system that serves as a central hub for accessing multiple AI models through a single, unified interface. Students will learn to integrate various AI services including OpenAI, Claude, and Google's Gemini through their APIs, while also implementing support for locally-run open-source models like Llama. Through this project, students will gain practical experience in API integration, software architecture, user interface design, and the deployment of AI systems, while developing a deeper understanding of how different AI models compare in capabilities and use cases. By the end of the project, students will built a functional AI gateway that demonstrates real-world software engineering skills and showcases their ability to work with cutting-edge AI technologies.

Expected Student Outcomes:

Technical Proficiency in AI Integration: Students will successfully integrate multiple AI models (OpenAI, Claude, Gemini, and local models) into a unified gateway system with proper API management and error handling.

Research Skills Development: Students will learn to evaluate and compare different AI models through systematic testing, analyze performance metrics, and document their findings using scientific methodology.

Professional Development: Participants will gain experience in project planning, version control, technical documentation, and presenting complex technical work to diverse audiences.

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Chengjie Zheng, PhD Computer Science Student, Summer Camp Director

Chengjie's Bio:

Chengjie Zheng is a PhD candidate in Computer Science at the University of Massachusetts Boston, where he currently serves as a Graduate Assistant and a core member of the Student Advisory Council (SAC) at the Paul English Applied Artificial Intelligence Institute (PEAAII). He leads the planning and execution of AI workshops, speaker events, and cross-disciplinary initiatives that make AI more accessible and practical for students. Passionate about democratizing access to AI, he is dedicated to helping students across disciplines streamline their academic and professional work, while fostering a university culture that values innovation, efficiency, and continuous improvement.

Steven Carr, Graduate Computer Science Student, Summer Camp Director

Steven's Bio:

Steven Carr is a Graduate (Master's) Computer Science student at the University of Massachusetts Boston. Former core member of the Student Advisory Council (SAC) at the Paul English Applied Artificial Intelligence Institute (PEAAII) and United States Air Force veteran, he has a passion for the technological advancement of AI and video game development. He fervently shares the ideals of the PEAAII's core mission: to democratize the use of AI for everyone.