Welcome to the Artificial Intelligence and Robotics Lab (AIRLab) at Cornell Tech. Our mission is to design intelligent systems (e.g., robots) that work alongside human teams in real-world, safety-critical environments (i.e., healthcare). Our research lies at the intersection of robotics, computer vision, and artificial intelligence. Through community engagement and human-centered design, we develop systems that improve teamwork and collaboration between multiple users with multi-robot systems, robot vision, AI, and AR/VR.

We are an interdisciplinary group of computer scientists, engineers, and designers in the Computer and Information Science Departments at Cornell Tech, Cornell University.

We are grateful to Google for supporting our research.

Teaching

[Spring 2023, Spring 2024] Practical Applications in Machine Learning, Cornell Tech. This course provides hands-on experience developing and deploying foundational machine learning algorithms on real-world datasets for practical applications (e.g., predicting housing prices, document retrieval, and product recommendation among others). Students will learn about the machine learning pipeline end-to-end including dataset creation, pre- and post-processing, annotation, annotation validation, preparation for machine learning, training and testing a model, and evaluation. Students will focus on real-world challenges at each stage of the ML pipeline while handling bias in models and datasets. Lastly, students will analyze the strengths and weaknesses of regression, classification, clustering, and deep learning algorithms.

 

[Fall 2022] Introduction to Human-Robot Interaction, Cornell Tech. Robots are making their way into our everyday lives, working across many applications including in people’s home, healthcare, and retail settings to name a few.  As these systems become more integrated into our lives, it is important that they are designed to be useful, functional, and socially acceptable; however, this remains a key challenge for the field of human-robot interaction (HRI).  This course covers core computational, engineering, social challenges, and approaches for effective HRI in human-centered environments.  Topics include research methods, robot design and anthromorphization, perception of people, groups and teams, spatial interaction, emotion and intent design in HRI, social signal processing – recognition and synthesis, and augmented and virtual reality (AR/VR) for HRI. Students should expect to learn about seminal research in HRI, gain hands-on experience with physical mobile robots, and implement systems for real-time interaction with users.

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