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Theodore Zhao

Zhengde Zhao 赵正德

Research Scientist
Apple
Seattle, WA, USA

About Me

I am a Research Scientist, working on vision and multimodal foundation models at Apple. Previously, I worked at at Microsoft, where I worked on vision and multimodal learning across a range of research projects. I received my PhD and Master degrees in Applied Mathematics from University of Washington with research focus on probabilistic and multiscale temporal modelling, advised by Prof. Tim Leung. During PhD, I interned at Microsoft Research, Harborview Medical Center, Rotella Capital, and Parametric. I received my Bachelor degree in Physics from Fudan University in Shanghai, China.

My research interest broadly covers:
  • Multimodal Intelligence: Vision-Language Models, Multimodal Foundation Models, Grounding, and Reasoning.
  • Visual Representation Learning: Self-Supervised Learning, Sparse and Efficient Representations, Video Understanding, and World Modeling.
  • Learning and Reasoning: Reinforcement Learning, Verifier-based Learning, and Multi-objective Learning.

I am particularly interested in building generalizable intelligence grounded in visual and multimodal representations, and I am always happy to discuss research and potential collaborations.!

News

  • [May 2026] STELLAR is accepted by ICML 2026. See you in Seoul!
  • [Feb. 2026] We released STELLAR, a unified vision representation learning framework supporting both rich semantics and high-quality reconstruction from only 16 tokens.
  • [Jun. 2025] The extension of BiomedParse to 3D images won the first place in CVPR 2025: Foundation Models for Text-guided 3D Biomedical Image Segmentation Challenge!
  • [Mar. 2025] BoltzFormer is accepted at CVPR 2025! See you in Nashville!
  • [Nov. 2024] BiomedParse is in Nature Methods! We are also releasing the code, model, and a million scale text promptable medical image segmentation dataset.
  • [May. 2024] Proud to lead the BiomedPrase project. We presented a foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities. Various medical image analysis tasks are unified with natural language prompting.
  • [May. 2024] Our paper on Error Detection is accepted by ACL 2024. See you in Bangkok!
  • [April. 2024] I am giving a talk at 2024 Joint Statistical Meetings on 08/05/2024 in Portland.

Selected Publications

  1. Publication Teaser Image
    Theodore Zhengde Zhao, Sid Kiblawi, Jianwei Yang, Naoto Usuyama, Reuben Tan, Noel C. Codella, Tristan Naumann, Hoifung Poon, Mu Wei
    ICML, 2026
  2. Publication Teaser Image
    Enshu Liu, Theodore Zhengde Zhao, Xuefei Ning, Zinan Lin, Yu Wang
    COLM, 2026
  3. Publication Teaser Image
    Jiachen Tu, Guanghui Qin, Theodore Zhengde Zhao, Jeya Maria Jose Valanarasu, Sheng Zhang, Tristan Naumann, Fan Lam, Sheng Wang, Hoifung Poon
    CVPR, 2026
  4. Publication Teaser Image
    Theodore Zhao*, Sid Kiblawi*, Naoto Usuyama, Hohin Lee, J Samuel Preston, Hoifung Poon, Mu Wei^
    CVPR, 2025
  5. Publication Teaser Image
    Theodore Zhao, Ho Hin Lee, Alberto Santamaria-Pang, Noel C. Codella, Sid Kiblawi, Yu Gu, Yu Fang, Wen Xuan Teng, Naiteek Sangani, Ivan Tarapov, Matthew P. Lungren, Matthias Blondeel, Tristan Naumann, Naoto Usuyama, Sheng Wang, Paul Vozila, Hoifung Poon, Mu Wei^
    MedSegFM, 2025
  6. Publication Teaser Image
    Theodore Zhao*, Yu Gu*, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Sid Kiblawi, Tristan Naumann, Jianfeng Gao, Angela Crabtree, Jacob Abel, Christine Moung-Wen, Brian Piening, Carlo Bifulco, Mu Wei^, Hoifung Poon^, Sheng Wang^
    Nature Methods, 2024
  7. Publication Teaser Image
    Theodore Zhao, Mu Wei, J Samuel Preston, Hoifung Poon
    ACL, 2024

Previous Work

  1. Publication Teaser Image
    Ruanne V Barnabas, Adam A Szpiro, Xolani Ntinga, Melissa Latigo Mugambi, Heidi van Rooyen, Andrew Bruce, Philip Joseph, Thulani Ngubane, Meighan L Krows, Torin T Schaafsma, Theodore Zhao, Frank Tanser, Jared M Baeten, Connie Celum, Alastair van Heerden, Siyabonga Nkala
    The Lancet HIV, 2022
    We conducted a randomised trial, the Deliver Health Study, of a fee for home delivery of ART compared d with free clinic ART delivery in South Africa.
  2. Publication Teaser Image
    Financial time series analysis and forecasting with Hilbert–Huang transform feature generation and machine learning
    Tim Leung*, Theodore Zhao*
    Applied Stochastic Models in Business and Industry, 2021
    We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstation financial time series.

Misc

In my free time, I play keyboard, and also started making my own music.

Here are some of my music — Just a heads-up, you'll hear real SOUND when you hit “play”! 🎵 🎧

  • Entanglement

  • Pearl Waltz

  • À La Recherche Du Temps Perdu

  • Sunshine in the Winter

If my research/music interests you, send me an email! I'm always open to collaborations and discussions.🍺