Carter Ung

Incoming CS PhD at Johns Hopkins · Embodied AI & Robotics at University of Washington

I am an incoming CS PhD student at The Johns Hopkins University, advised by Homanga Bharadhwaj (Research Scientist, Meta Reality Labs) and Greg Hager (Director, NSF CISE and Amazon Robotics). My research is at the intersection of robotics and artificial intelligence: building full-stack robotic systems that can reason in clutter and operate in messy human environments. I am supported by the NSF CISE Graduate Fellowship.

Currently, I am a predoctoral researcher at the University of Washington, advised by Dieter Fox (Founding Director, Seattle NVIDIA Robotics Lab) and Siddhartha Srinivasa (Founding Director, Amazon Robotics AI) in the Robotics and State Estimation Lab and Personal Robotics Lab.

I also work as a software engineer at Microsoft, building Copilot into OneNote. Previously, I studied Computer Science and Biomedical Engineering at the University of Houston, where I spent over two years with Dr. Shishir Shah on pose-invariant face recognition (VISAPP 2025).

I’ve come across many kind people in my curious journey toward academia and industry. I am always open to chat and talk about perspectives in research, career, and life aspirations. Reach me at carterung [at] gmail [dot] com.

I work toward general-purpose robots that safely reason and manipulate alongside people in everyday environments. I am interested in a) building full-stack robotic systems that continuously collect, train on, and evaluate diverse manipulation data with minimal expert involvement, b) learning generalizable representations from human data that capture how people reason, plan, and grasp across diverse tasks, and c) closing the loop between simulation and the real world for dexterous manipulation that continually grows skills through interaction and deployment.

News

Apr 2026 We release RoboPlayground and partner with the BitRobot Network to unveil TeleArms!
Jan 2026 RoboEval accepted to IEEE International Conference on Robotics and Automation (ICRA) 2026!
Aug 2025 Selected as a 2025 National Science Foundation Computer and Information Science and Engineering Graduate Fellow!

Publications

	RoboPlayground: Democratizing Robotic Evaluation through Structured Physical Domains Yi Ru Wang^, Carter Ung^, Evan Gubarev, Christopher Tan, Siddhartha Srinivasa^†, Dieter Fox^† ^*Equal contribution ^†Equal advising Under Review Website PDF
	RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation Yi Ru Wang, Carter Ung, Christopher Tan, Grant Tannert, Jiafei Duan, Josephine Li, Amy Le, Rishabh Oswal, Markus Grotz, Wilbert Pumacay, Yuquan Deng, Ranjay Krishna, Dieter Fox^†, Siddhartha Srinivasa^† ^†Equal advising IEEE Conference on Robotics and Automation 2026 (ICRA), Eval&Deploy Workshop Paper at CoRL (Oral Spotlight), Bimanual Manipulation: Advancing Human-Humanoid Interaction and Collaboration Workshop at IEEE International Conference on Intelligent Robots and Systems (IROS 2025) Website PDF Code RoboEval introduces a unified evaluation suite covering bimanual manipulation tasks with fine-grained metrics so researchers can compare models beyond binary success/failure.
	Minimizing Number of Poses for Pose-Invariant Face Recognition Carter Ung, Pranav Mantini, Shishir Shah International Conference on Computer Vision Theory and Applications (VISAPP) 2025 PDF We study how many viewpoint images are truly needed for robust, pose-invariant face recognition and show that a compact pose set dramatically reduces data-collection overhead.
	Dual-VXM: A Prior-Aware Dual-Encoder UNet for Watch-and-Wait Monitoring with Low-Field MRI Mohammad Javadi, Panagiotis Tsiamyrtzis, Carter Ung, Ernst Leiss, Andrew Webb, Nikolaos Tsekos Under Review We present a novel dual-encoder UNet architecture that leverages prior high-field MRI scans to enhance low-field MRI image quality for improved monitoring of Low-Grade Gliomas (LGG).
	Minimizing the Number of Poses for Pose-Invariant Face Recognition Carter Ung Undergraduate Dissertation, University of Houston 2024 PDF My honors thesis extends the VISAPP study and details the full experimental pipeline for low-redundancy face-data acquisition.