Shuran Song

She is a prominent figure in the field of robotics and perception, known for her significant contributions to 3D modeling and algorithm development. Her work has focused on recognizing and reasoning about 3D shapes, which has earned her multiple best paper awards. She has also showcased impressive capabilities in tackling challenging robotic tasks.

Tatsunori Hashimoto

He has made substantial contributions to artificial intelligence, specifically in the areas of distributional robustness, sequence modeling, and bioinformatics. He has authored several foundational papers on robust natural language processing (NLP) and large language models (LLMs). His recent research, which focuses on human feedback and evaluation in LLMs, exemplifies both innovation and impact in the field.

He He

She has demonstrated her expertise at the crossroads of machine learning, natural language processing, and artificial intelligence. Her pioneering research on coaching as a model for imitation learning has been particularly notable. Her work encompasses both the algorithmic facets of AI and its broader societal implications, including safety and consequences.

Aviral Kumar

He has made substantial strides in the field of offline and safe reinforcement learning, tackling the constraints of online learning in delicate real-world settings. His recent contributions to contemporary large-scale language models illustrate how progress in offline reinforcement learning can optimize training procedures.

Jun-Yan Zhu

He has made significant contributions to the fields of vision and graphics, with a particular focus on generative image models. Notably, he has demonstrated the potential of Generative Adversarial Networks (GANs) to rival the performance of diffusion models, provided that sufficient data is available. This has been achieved through the implementation of innovative strategies aimed at enhancing the capabilities of GANs. His work addresses fundamental empirical questions and has resulted in substantial advancements within the field.