AI Research
Conduct data-driven AI research: build predictive models and produce a research-style paper and presentation to showcase your work.
AI Research is an advanced program where students learn how to conduct a rigorous AI investigation from start to finish, using research topics grounded in social impact and the UN Sustainable Development Goals. Students develop a focused research question, work with real datasets tied to community and global challenges, and build predictive models that connect data to meaningful conclusions.
Students learn the core building blocks of AI research by preparing training data, selecting appropriate machine learning approaches such as regression, decision trees, and random forests, and evaluating model performance through iteration and comparison. Students also learn how to use modern AI tools, including large language models, to accelerate research workflows through effective prompting, ideation, and debugging support.
By the end of the program, students produce a research-style paper and presentation that demonstrate technical depth, analytical thinking, and research communication, with a clear link to societal benefit. This outcome strengthens a student's academic portfolio and can be included in college applications, research showcases, and STEM opportunities. Because this is a research-intensive program, students should expect to complete a significant amount of independent work outside scheduled meeting hours to meet these expectations.
Program Details
Start Date
June 22, 2026
End Date
August 7, 2026
Duration
6 weeks
Program Times
5:00 - 7:00 PM PST (Mon, Thu)
Program Cost
$1000
Program Information
Prerequisites
- Completed Statistics and Math through Pre-Calculus (or equivalent)
- Prior programming experience (Python preferred), demonstrated via a certificate of completion
- Prior experience with data analysis, demonstrated via a short presentation or portfolio sample
Program Format
- Live online sessions 5:00 - 7:00 PM PST
- Lectures covering foundational AI and machine learning concepts
- Guided research skill-building (literature review, question formulation, evaluation)
- Week-by-week research plan with defined tasks and deliverables
- Hands-on work with real datasets and iterative predictive modeling
- Mentorship and feedback from domain-expert research advisors
Curriculum Highlights
- Research Foundations: Formulate a research question, review literature, and define a clear investigation plan
- Data Preparation: Collect, clean, and structure real-world datasets for modeling and analysis
- Predictive Modeling: Train and compare models using regression, decision trees, and random forests
- Model Evaluation & Iteration: Assess performance, diagnose errors, and refine features and approaches
- Research Communication: Write a research-style paper and present methods, results, and implications clearly
Capstone Project & Showcase
Students will complete an end-to-end AI research study that reflects advanced academic and technical growth by defining a focused research question with measurable success criteria and building and comparing predictive models.
Meet the Instructors
Dr. Suma Bhat is a Research Scholar in Computer Science at Princeton University and an AI researcher and educator specializing in natural language processing, human–AI interaction, and learning analytics. She previously served as an Assistant Professor in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, with affiliate roles in Computer Science and Educational Psychology. Her research develops AI methods that better understand nuanced human language and supports learning through intelligent, data-driven tools, including applications in STEM learning and medical education. In this program, Dr. Bhat guides students through the complete research process, helping them formulate meaningful questions, build rigorous models, and communicate findings with clarity and academic precision.
Program Registration
Register for AI Research
Complete the form below to apply for this program.
Testimonials
"DIYA showed me that data scientists can be changemakers, not just backseat workers. Applying machine learning to real social issues opened my eyes to the range of possible applications."
— Sandeep , Grade 11, 2023
"The DIYA program helped me learn so much about Machine Learning—Decision Trees, Random Forests, and Gradient Boosted Trees. I also learned when to use median, mean, or mode to fill data versus deleting null values entirely."
— Sudharsan , Grade 10, 2022
"DIYA made me realize the true power of data science. In an unpredictable world, using data for prediction can guide us in the right direction. The mathematical concepts behind ML algorithms, like Random Forest, really clicked for me."
— Madhavenshu , Grade 11, 2022
"The DIYA research program gave me a deeper understanding of the research process and allowed me to explore a new topic at a high level. The mentors' expertise and willingness to share knowledge were instrumental in my growth as a researcher."
— Vienna , Grade 11, 2023
"Through DIYA Research, I learned how to visualize, analyze, and use data to make predictions and validate hypotheses. Working in a team helped me improve my collaboration skills and learn from other students my age."
— Srinitha , Grade 11, 2023
"Working under DIYA mentors has been a rewarding experience. DIYA cultivated my interest and curiosity toward data science and machine learning, and I'm excited to continue this journey."
— Krishna , Grade 11, 2023
"This program helped me tremendously. I learned so much about data science that will aid me in my quest of becoming a doctor. Thank you to all the mentors and DIYA teachers for their amazing advice."
— Aaroh , Grade 10, 2022
"Participating in the DIYA Research program was an enlightening journey. Working on a climate change project, we used Python and a decision tree model to study how humidity, wind speed, and air pressure relate to rising temperatures. Despite limited data, we uncovered meaningful insights, strengthened my ML skills, and deepened my understanding of the importance of robust datasets. The mentorship and research experience inspired me to explore NLP and continue pursuing data science and machine learning in college."
— Sudharshan , Grade 10, 2021
Frequently Asked Questions
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