AI Forge Hero.

AI Forge

Purdue's AI Forge is both a principle and an initiative aimed at providing Purdue's undergraduate and master's students with advanced skills in state-of-the-art generative AI techniques, taught by internationally recognized AI experts. The key principle of AI Forge is giving students agency, knowledge, and resources to allow them to forge their own path in AI.

AI Forge Course

This semester-long course provides students with hands-on knowledge on state-of-the-art foundation models for AI.

Students will learn the basics of LLMs; in-context learning and RAG; and multi-modal generative AI models.

This course is open to students with an interest in AI who have Python programming experience and a foundational understanding of basic probability and statistics and linear algebra.

Students can enroll during open registration starting December 10. Contact your advisor for more information. 

  • Course number: CS 39000AIF (crn 41156)
  • Meets Monday, Wednesday, and Friday at 11:30a-12:20p

AI Forge Projects

AI Forge projects give students the chance to apply artificial intelligence to real-world challenges. From sustainability and healthcare to creative technologies and smart systems, each project is designed to be hands-on, collaborative and impact-driven. 

Students work in teams guided by faculty and industry mentors to move ideas from concept to prototype, gaining real experience in research, design and responsible AI development along the way.

Experience projects mirror a capstone-style learning experience. They are open to students and faculty across disciplines and emphasize hands-on learning, exploration, and skill development. The focus is on gaining experience with GenAI tools, not necessarily producing a polished or production-ready outcome.

Foundry projects are advanced, industry-style initiatives that focus on building high-impact, deployable GenAI solutions. They emphasize practical product development, scalability, and real-world value rather than research novelty.

 

Apply for AI Forge Projects

Project Titles

  • Factually Grounded and Human-Aligned RAG Systems for AI Governance and Policy
  • Auditing Bias in LLM-Generated Targeted Communication
  • GenAI-Driven Semantic Verification for Neural Math Theorem Proving
  • Computer Vision for Grocery Item Detection and Counting
  • AI Forge: Human-in-the-Loop Systems for Scalable Coursework Assistance
  • AI Forge: Building a Scalable LLM API Service
  • LLM-Agent Integration in Logistics Simulation
  • AI for Logistics: Neural Algorithmic Reasoning and Open-Source Simulator Development
  • Generative AI for Endowing Data-Driven Causal Discovery with World Knowledge
  • AI for Constrained Optimization
  • Educational Minecraft LLM Agent
  • AI for Materials Discovery
  • Autonomoua Data Exploration Agent
  • Wildlife Conservation Agent: Vision-Language Models for Habitat Monitoring
  • Developing and Benchmarking Graph Foundation Models
  • AI SHARE: Building the Global AI Attitudes Research Portal
  • Bridging the Sim-to-Real Gap: Generating Clinically Accurate Patient Videos
  • Improving Improvisational Reasoning in Vision-Language Models
  • Using Vision-Language Models to Provide Reward Signals for Reinforcement Learning
  • Flow-based Alignment of Graph Input Spaces in ALL-IN Foundation Models

Past AI Forge Projects

Person sitting in front of having a planning and analyzing with display screen and pointing on the data. (Adobe Stock)

Aligning Risk/Opportunity Preferences with Agent's Decision Making Behavior in the Financial Domain

This project explored whether large language models (LLMs) can reason about real investor behavior and connect it to declared risk preferences. Using a unique dataset that combines survey responses, trading history, and market data from European stock exchanges, students investigated whether AI models can identify investors preferences based on their investment actions. Initial experiments show that predicting an investor’s risk level from their trading behavior is surprisingly difficult, highlighting an opportunity for more advanced AI reasoning. The team proposed and implemented an LLM-based approach for identifying relevant risk-aligned behavioral patterns from raw data and showed that models learning over these patterns can significantly better predict investors’ preferences in real-world market conditions.
Chimpanzee sitting on a branch (Adobe Stock)

SimianSight: Multi-modal Vision-Language Model Framework to Monitor and Predict Ape Behaviors

Wildlife conservation efforts require constant monitoring of habitat conditions, which can be too costly to do at the needed scale. Recent advances in multimodal AI system have the potential to significantly lower that cost through automation. This project explored the first steps in this process - whether AI models can be used to better understand animal behavior in the wild. Unlike past work that relies on manually annotated data for training visual detection systems, this project uses a multi-modal foundation model, analyzing chimpanzee behavior in videos without additional data. Through careful analysis of the model’s capabilities, the students designed a multiple-step reasoning strategy for identifying complex social interactions, emotional cues, and behavioral patterns. Experiments comparing our reasoning strategies to traditional prompting show a more than 20% reduction in model prediction error.