Duke Applied Machine Learning
An open-membership student org dedicated to enhancing ML education at Duke.
What we do
An inclusive pre-professional ML community
We are a student-led organization that empowers education, research, and applied work in machine learning. From training and hands-on projects to community events, we help student engineers gain real ML experience, project leadership, and collaboration with partners across Duke and beyond.
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.
- 8-week AI Training Program — from regression and clustering through transformers and LLMs
Client Projects
Pairing Duke's talent with real experience
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.
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
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.
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.
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.
Build alongside Duke's ML talent
Work with DAML members on rigorous ML projects, ranging from explorations to real-world applications. Whether you're a student or a prospective partner, join a team focused on building and prototyping.