Kyle
The Northwest School, Seattle
The program helped me develop AI concepts through structured, research-driven project work. (... continue reading)

Admitted students pursue original research, advanced technical work, and formal capstone projects under rigorous academic guidance.
Admission is extremely selective. Adava University reviews only top-achieving applicants prepared for advanced academic work. Submit your email to begin the application process.
Adava programs are built around advanced academic performance: close reading, analytical writing, disciplined research, quantitative reasoning, and evidence-based defense of ideas.
Students explain their academic work through articles, videos, and project presentations.
The Northwest School, Seattle
The program helped me develop AI concepts through structured, research-driven project work. (... continue reading)
Clayton High School
The program helped me prepare for university-level study through sustained work on difficult topics. (... continue reading)
King's College, Auckland
My favourite part was learning about generative adversarial networks because the architecture is so clever. (... continue reading)
Every admitted student completes original research, technical development, and formal presentation work.
Completion requires depth, initiative, academic maturity, and sustained effort.
Completed work demonstrates original research, technical execution, and academic presentation.







Students receive formal recognition for completed academic work and distinguished project performance.


Students move beyond introductory exposure.
They build, research, write, revise, and defend original work.
Students pursue focused academic and technical concentrations.
Areas include:
Each concentration culminates in inquiry-driven work, revision, and a substantive final deliverable.
Students work with instructors who have studied, researched, or built technical projects in demanding academic environments.

Finance and data science background, with applied machine learning work on policy recommendations and transparency projects.

Computer Science and AI researcher and developer with experience in MIT research labs including Media Lab, CSAIL, and Space Systems Lab.

Oxford engineering background with computer vision and robotics research experience, plus student technology leadership.

Interdisciplinary scientist working across engineering, physics, and biology, with fellowship-backed PhD and research experience.

Stanford CS background, former Facebook and Apple engineer, and full-stack engineer at an early stage startup.

Built Knowledge Graph pipelines, improved document classification with Graph Neural Networks, and co-founded startups as CTO.

Engineering background with advanced mathematics and physics preparation, including Cambridge academic program experience.

Mathematics background with graduate work in simplicial homotopy theory and a later pivot into data science.
One ongoing weekly program with sustained academic expectations and regular review.
Submit your email to receive admissions criteria, required materials, and review steps for top-achieving applicants.
Application review includes: