Undergraduate Student

Carnegie Mellon University

Pittsburgh, PA 15213

I am an undergraduate student pursuing a Bachelor of Science in Mathematics and Computer Science at Carnegie Mellon University (CMU), with an expected graduation in May 2026. My research interests span Embodied AI, Spatial Intelligence, 3D Reconstruction and Understanding and Spatial Data Infrastrutre, with additional focus on Machine Learning for Healthcare.

I am currently conducting research at both the Computer Science Department and the Robotics Institute at CMU. Within the Computer Science Department, under the supervision of Prof. Srinivasan Seshan and Prof. Anthony Rowe, I serve as a research assistant at WiSELab focuses on large-scale spatial data infrastructure and spatial data understanding using multimodal large language models (MLLMs). At the Robotics Institute, I am jointly supervised by Prof. Jean Oh and Prof. Jeffrey Ichnowski, conducting research on 3D reconstruction, world models, and soft robotics.

Previously, I was a research assistant at DropLab at CMU , supervised by Prof. Matthew Johnson-Roberson and Dr. Weiming Zhi, where I focused on 3D reconstruction from single-view images and path planning in 3D environments. I also worked at the DIG Lab in the Human-Computer Interaction Institute (HCII) at CMU, supervised by Prof. Adam Perer, conducting research on machine learning systems for healthcare.

Besides research, I’m a big basketball enthusiast 🏀 and an avid fan of travel 🌏 and photography 📷!

I am actively seeking opportunities for internships and graduate school. Feel free to contact me at email.

news

Nov 08, 2025	My personal website is released!

selected publications

RA-L
DOSE3: Diffusion-based Unified Out-Of-Distribution Detection on SE(3) Trajectories

Hongzhe Cheng^*, Tianyou Zhang^*, Ziyong Ma^*, Tianyi Zhang, Matthew Johnson-Roberson, and Weiming Zhi

IEEE Robotics and Automation Letters (RA-L), 2025

Abs Bib

A diffusion-based approach for unified out-of-distribution detection in robotic trajectory planning, improving safety and reliability in uncertain environments.
@article{ma2024pose3, title = {DOSE3: Diffusion-based Unified Out-Of-Distribution Detection on SE(3) Trajectories}, author = {Cheng, Hongzhe and Zhang, Tianyou and Ma, Ziyong and Zhang, Tianyi and Johnson-Roberson, Matthew and Zhi, Weiming}, journal = {IEEE Robotics and Automation Letters (RA-L)}, year = {2025}, tags = {robotics} }
ML4H
TempoQL: A Readable, Precise, and Portable Query System for Electronic Health Record Data

Ziyong Ma, Venkatesh Sivaraman, Richard D Boyce, and Adam Perer

In Proceedings of the Machine Learning for Healthcare (ML4H), 2025

Also Presented at TS4H @ NeurIPS 2025

Abs Bib Code

A readable, precise, and portable query system designed for electronic health record data analysis, enabling healthcare professionals to perform complex temporal queries with ease.
@inproceedings{ma2025tempoql, title = {TempoQL: A Readable, Precise, and Portable Query System for Electronic Health Record Data}, author = {Ma, Ziyong and Sivaraman, Venkatesh and Boyce, Richard D and Perer, Adam}, booktitle = {Proceedings of the Machine Learning for Healthcare (ML4H)}, year = {2025}, note = {Also Presented at TS4H @ NeurIPS 2025}, tags = {healthcare} }

NeurIPS

Building 3D Representations and Generating Motions From a Single Image via Video-Generation

Weiming Zhi, Ziyong Ma, Tianyi Zhang, and Matthew Johnson-Roberson

In Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS 2025), 2025

Abs arXiv Bib

@inproceedings{ma2024single,
  title = {Building 3D Representations and Generating Motions From a Single Image via Video-Generation},
  author = {Zhi, Weiming and Ma, Ziyong and Zhang, Tianyi and Johnson-Roberson, Matthew},
  booktitle = {Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS 2025)},
  year = {2025},
  publisher = {NeurIPS,},
  tags = {robotics}
}

arXiv
OpenFLAME: A Federated Spatial Naming Infrastructure

Sagar Bharadwaj, Ziyong Ma, Ivan Liang, Michael Farb, Anthony Rowe, and Srinivasan Seshan

In arXiv, 2025

Abs arXiv Bib Website

A moderated heterogeneous federated learning framework that enables data scientists to collaboratively specify predictive modeling tasks while maintaining data privacy and security.
@inproceedings{ma2025openflame, title = {OpenFLAME: A Federated Spatial Naming Infrastructure}, author = {Bharadwaj, Sagar and Ma, Ziyong and Liang, Ivan and Farb, Michael and Rowe, Anthony and Seshan, Srinivasan}, booktitle = {arXiv}, year = {2025}, publisher = {arXiv,}, tags = {system} }