Wed, Nov 23, 2022 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
PhD. candidate: Yunhao(Andy) Ge
Title: Towards trustworthy, effortless, and human-like AI in computer vision
Time: Nov. 23, Wednesday 10:00AM-12:00AM (PST)
Location: Room B15 (basement), Hedco Neurosciences Building, 3641 Watt Way, Los Angeles, CA 90089, USA.
Artificial Intelligence (AI) has achieved great success in various domains, such as self-driving, medical diagnosis, and mobile robotics. Model and Data, two foundations of the current AI system, play significant roles in ensuring the success of AI. However, there are still challenges that remain to be addressed:
On the model side: how to make AI models be trustworthy and reliable? How to empower AI models with the learning and reasoning ability of the human brain? (1) Lack of trustworthiness is a big challenge: The bad transparency of model decisions hinders the understanding of errors and prevents saving more lives.
(2) Filling the gap between the human brain and AI models is challenging. \"How do we humans get so much (ability) from so little (supervision)?\" How can we build more powerful learning machines based on the same principles as the human brain?
On the data side: How can we minimize the human effort in labeling data and learn from increasingly weak forms of supervision? How to use synthetic data to substitute real-world data to avoid privacy and scalability issues?
To conquer the above mentioned challenges, my research focuses on three different but highly connected and mutually supported dimensions: 1) Human-centric and trustworthy AI: Understand the Human-centric properties of AI models. Such as Causal Explainability, Robustness, Steerability, and Domain Adaptation. 2) Humanoid AI: Simulate human cognitive learning ability. Such as Imagination, Visual Reasoning, and Multi-modal learning (CLIP). 3) Data-centric (human-effortless) AI: Use synthetic data and neural renderer (NeRF, DALL-E, GAN, VAE) to solve real-world computer vision problems (classification, detection, segmentation) with minimal supervision.
Committee members: Laurent Itti (Chair), Ram Nevatia, Greg Ver Steeg, Yan Liu, Nicolas Schweighofer.
Zoom link (hybrid):
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Meeting ID: 222 662 0525
Audiences: Everyone Is Invited
Contact: Lizsl De Leon