DAPLab is a group of Columbia faculty and their PhD students at the forefront of applied research on Data, Agents and Processes (DAP). Agents have the potential to transform how humans, companies, and economies function, yet also introduce systemic risks at every level of technology and society.

The goal of the DAPLab is to pursue research to make agent-based automation accountable, reliable, and efficient, and thus trusted within organizations.

The lab pursues this vision by combining expertise across systems (OS, data, cloud), AI (ML, RL, NLP, computer vision, robotics), human-computer interaction, and operations research.

For more information about the lab, please contact ewu@cs.columbia.edu

Events and Education

Workshop 2025: “AI Agents for Work” On March 12, 2025, DAPLab ran the first annual workshop at the Columbia Business School. The one-day workshop to brought together over 200 industry leaders, Columbia faculty and students, and technologists who are interested in the concept of AI agents.

Speakers and panelists come from enterprises that are deploying agentic solutions, technologists and infrastructure leaders, and researchers at leading AI labs as well as Columbia. These include Jason Wei from OpenAI who led their chain-of-thought and agentic work, Danielle Perszyk from Amazon AGI, Jonathan Frankle from Databricks, Deepak Dastrala from Intellect, Cong Yu who leads AI at Celonis, and more.

Spring 2025 class: Agentic System Made Real LLMs have opened new possibilities of automated agents that plan and complete tasks on the user’s behalf. Such agents have the potential to usher in a new industrial revolution by automating organizational processes. This graduate-level course will cut across the technology stack to examine the research questions that need to be answered for agents to be possible in real tasks that matter. Each session will review 1-3 papers or systems, and discuss research opportunities that arise from the gap between existing research and enterprise requirements. Topics will span systems (data systems and ML systems), AI (LLMs, agent-based planning), HCI, and theory (reinforcement learning, markets).