معرفی
Jacob Yeung is a Ph.D. student in the Neural Computation & Machine Learning joint program at Carnegie Mellon University, supported by an NSF Graduate Research Fellowship. He is advised by Prof. Michael J. Tarr and Prof. Deva Ramanan.
- Current Institution: Carnegie Mellon University
- Prior Education: B.S. in Electrical Engineering and Computer Science (EECS) from University of California, Berkeley
His primary research focuses on understanding human visual processing of dynamic stimuli and enhancing machine visual reasoning through fMRI decoding and video diffusion models. Key contributions include developing BrainNRDS for motion decoding from neural activity and eDCA for dimensionality reduction in neural-behavioral data.
Recent publications highlight advancements in dynamic visual stimuli reanimation (CVPR 2025 Oral) and cortical semantic mapping with Vision Transformers (ICLR 2025). He has also contributed to datasets like Hangul Fonts and analytical methods such as eDCA.
Scientific Awards:
- NSF Graduate Research Fellowship
He has served as a teaching assistant for Advanced Machine Learning (10716) at CMU (Spring 2025) and Introduction to Network Communications (EE 122) at Berkeley (Spring 2022). Additionally, he tutored foundational computer science courses at Berkeley in 2021.
