AI Training Program
A 10-week program taking members from ML fundamentals through transformers, generative models, and agents, ending in a judged project showcase.
Est. 2019
Duke's largest AI/ML community
An open-membership student org building Duke's next ML engineers through hands-on training, real client projects, and a strong alumni network.
250+Members
70+Data scientists
40+Projects
Where DAML members go
What we do
An inclusive pre‑professional ML community
A student-led org where members learn ML, lead projects, and work with partners across Duke and beyond.
Client Projects
Dedicated engineering teams paired with partner organizations to deliver ML prototypes across the semester.
Internal Projects
Our students lead personal projects that explore new ideas and research-inspired experiments.
Research
Initiatives in diffusion, medical imaging, and vision-language-action models, run with faculty-adjacent rigor.
Community & Events
Events, alumni connections, and recruiting touchpoints that extend the DAML network.
A large thank you to the teams that have presented at our 2026 showcase.
Education
The AI Fundamentals Training Program
DAML's AI Training Program (AITP) is the structured entry point for members to become engineers within the organization. The program takes members from ML fundamentals through modern deep learning systems, with a focus on real deliverables and project readiness.
- A 10-week program, from classification and clustering through transformers, generative models, and agents
- Weekly workshops followed by mentored project work sessions
- A final showcase judged by our Data Science Directors
Client projects
Student teams on real client problems
We match dedicated engineering teams with partner organizations to deliver ML prototypes throughout the semester, with dedicated PMs and leads.
ML Consulting
Our teams scope ML projects and define clear, measurable outcomes & action plans.
Eng. Teams
We pair dedicated engineering teams with partner organizations, bringing Duke's CS talent to tackle real-world ML problems.
Deliverables
Partners receive project plans, EDA reports, and working model prototypes at defined milestones.
Companies we've built projects with
From early-stage startups to big tech.
Internal Projects
Our students lead personal projects that explore new ideas and research-inspired experiments.
Community & Events
Events, alumni connections, and recruiting touchpoints that extend the DAML network.
Portfolio
Featured projects
A selection of what our engineering teams shipped last semester (Spring 2026), from diffusion research to deployed client tools.
Unifying Optimal Transport Frameworks in Diffusion
This project focuses on unifying diffusion and optimal transport frameworks, including Flow Matching, Schrödinger Bridges, and DDPMs, to simplify diffusion model training and inference. It explores DiT architectures and optimized sampling methods to improve performance.
Duke AI Tour Guide
AI-enabled tour guide web app that allows users to take pictures of Duke campus buildings and receive short summaries with up-to-date information. Uses CLIP for building recognition, LLMs for chatbot responses, and real-time location tracking for campus navigation.
Taylor Allen, Erica Zhang, Natalie Lai, Reese Pagtalunan, Uzair Chaudhry, Veronica GuoView slides – Duke AI Tour Guide
Multimodal NCDE for Medical Image Forecasting
Develops a multimodal neural controlled differential equation (NCDE) model to perform optical flow, interpolation, and extrapolation of medical image sequences to forecast disease progression in Alzheimer's patients using PET and MRI data.
Aashish Cheruvu, James Wright, Matthew Xie, Tristan Carter, Alan YeView slides – Multimodal NCDE for Medical Image Forecasting