About me and my work

I am fascinated by the natural phenomenon of intelligence, and I work on understanding and advancing the limits of artificial intelligence (AI).

I am a co-founder and the Chief Scientist of Yutori. I have been a professor, led research teams in industry, and built open-source communities.

I was a Senior Director at Meta leading FAIR Embodied AI (AI for robotics and smart glasses). My teams:

• Developed the multimodal AI assistant that shipped in the Ray-Ban Meta SmartGlasses.
• Built Habitat, the fastest 3D simulator for training virtual robots to navigate, pick and place objects, and operate around humans, and follow language instructions.
• Solved PointNav, the task of navigation to goal coordinates in unfamiliar environments without a map, both in simulation and with Boston Dynamics' Spot robot.
• Demonstrated a robotic assistant to CBS and at the White House Correspondents' Dinner.
• Built the world's first aritifical superhuman fingertip.

I was a tenured Associate Professor in the School of Interactive Computing at Georgia Tech, where:

• I recived the PECASE award, the highest honor bestowed by the U.S. government for early career scientists and engineers.
• I created Georgia Tech's Deep Learning class in 2017 and taught it till 2021.
• My PhD students won university-level dissertation awards in 4 out of the 8 years I spent at Georgia Tech.
• Three of those students (Aishwarya Agrawal, Abhishek Das, and Erik Wijmans) were recipients/honorable-mentions of the ACM SIGAI Doctoral Dissertation Awards.

My work has received best paper awards/nominations/honorable mentions in every area of AI

• computer vision (Ego4D at CVPR 2022, AI Habitat at ICCV 2019),
• machine learning (Emergence of Maps at ICLR 2023),
• natural language processing (Lack of emergence of language at EMNLP 2017),
• robotics (Combining foundation models and mapping (System 1 + System 2) at ICRA 2024).

• Summarizing beliefs of AI agents via diverse plausible predictions/hypotheses:
  Diverse Beam Search, Multiple Choice Learning, Tutorial on Diversity at CVPR '13 and CVPR '16,
• Vision-and-language (or multimodal AI):
  My colleagues and I developed the foundations of a new AI sub-field — new tasks, benchmarks, techniques, and models.
  
  If the phrases Visual Question Answering (VQA), Text VQA, Visual Dialog, Audio-Video Dialog, image-text cross-modal attention, visuolinguistic pre-training, or training visual chatbots with RL sound familiar, you have heard of the titles of our papers.
• Embodied AI and robotics:
  Habitat: A Platform for Embodied AI, Decentralized Distributed PPO, Embodied Question Answering, Sim2Real Predictivity, ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation, LSC: Language-guided Skill Coordination for Open-Vocabulary Mobile Pick-and-Place
• Explainable, Unbiased, Trustworthy AI:
  Grad-CAM (Visual Explanations), Human-vs-machine attention, Counterfactual Visual Explanations
• Platforms for reproducible AI research:
  EvalAI, a platform for evaluating AI algorithms.

Essays

• Dec 2024: The term "LLM" is a misnomer
• Nov 2024: The repeated saturation of scaling
• Jan 2024: 6 Years of Questions