Siyuan Huang, a PhD candidate in computer engineering at Johns Hopkins University, studies AI, computer vision, large language models (LLMs), multimodal learning, and person re-identification in the Artificial Intelligence for Engineering and Medicine (AIEM) Lab, under the supervision of Bloomberg Distinguished Professor Rama Chellappa.

Currently, Huang also is an applied scientist intern at Amazon, where he leads research on large language models (LLMs) for understanding and evaluation. He previously was an associate researcher at the National Institute of Standards and Technology and was a graduate researcher at Tsinghua University in China. Huang received his MS in computer engineering from Johns Hopkins and his MS in computer science from George Washington University. He has received multiple awards, including the IJCB Best Student Paper Award.

Yen-Ju Lu, a PhD candidate in Johns Hopkins Department of Electrical and Computer Engineering, focuses on bridging speech and text through multimodal large language models (LLMs) that leverage speech representations, with the goal of developing more reliable, scalable, and efficient spoken language intelligence. His work spans speech-text pre-training, representation learning, and efficient data synthesis to advance multimodal AI. Yen-Ju received his MS in electrical engineering and computer science from National Taiwan University in 2017.

Yiqing Shen, a computer science PhD candidate, is developing visual foundation models that can address real-world challenges. His aim is to develop models that can understand and interpret visual information in ways that can be applied across multiple fields, including healthcare. Prior to his doctoral studies, Shen earned his BS degree in mathematics and applied mathematics from Shanghai Jiao Tong University in 2018.