Yingtian Tang

I am currently a PhD student at EPFL, working on NeuroAI with Prof. Martin Schrimpf.

I had my Master's degree at University of Pennsylvania, majoring in Computer and Information Science (CIS). I was also a research assistant at the GRASP Lab, supervised by Prof. Pratik Chaudhari.

I had my undergraduate at University of Electronic Science and Technology of China, majored in Computer Science. During my undergraduate, I also worked as research assistants in Nanyang Technological University (Rapid-Rich Object Search Lab), and later Tencent (Robotics X).

Contact

yingtian.tang [at] epfl dot ch

Curriculum Vitae (Transcript) [slightly outdated]

Research Interests

I am interested in artifitial intelligence, especially how current machine learning methods could approach active perception and learning in the real world. I think this line of research lies in the intersection between computational neuroscience, computational cognitive science, and machine learning. I then studied and was impressed by the research by people like James J. Gibson and Peter Dayan, and believe that machine learning will take them further (or conversely, they will guide a right way for ML research).

Currently, I am working on building models for dynamic perceptual representation, based on state-of-the-art representation learning methods, such as JEPA and MAE, and brain recordings of video watching.

Publications

Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective
Yingtian Tang, Abdulkadir Gokce, Khaled Jedoui Al-Karkari, Daniel Yamins, Martin Schrimpf
bioRxiv
Link, Tweets, Page

From Language to Cognition: How LLMs Outgrow the Human Language Network
Badr AlKhamissi, Greta Tuckute, Yingtian Tang, Taha Binhuraib, Antoine Bosselut*,Martin Schrimpf*
CCN 2025
Link, Tweets

Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data
Yingtian Tang*, Badr AlKhamissi*, Abdulkadir Gokce*, Johannes Mehrer, Martin Schrimpf
BabyLM Challenge, at CoNLL 2024
Link

Online Motion Style Transfer for Interactive Character Control
Yingtian Tang, Jiangtao Liu, Cheng Zhou, and Tingguang Li
Arxiv Preprint (2021)
Link

Learning-Aided Heuristics Design for Storage System
Yingtian Tang, Han Lu, Xijun Li, Lei Chen, Mingxuan Yuan, and Jia Zeng
The 2021 ACM SIGMOD/PODS International Conference on Management of Data
Link

Accurate probabilistic miss ratio curve approximation for adaptive cache allocation in block storage systems
Rongshang Li, Yingtian Tang, Qiquan Shi, Hui Mao, Lei Chen, Jikun Jin, Peng Lu, and Zhuo Cheng
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Link