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). I am currently 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 am grateful to have my graduate research supported by the NSF CISE Graduate Fellowship.

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).

My research is at the intersection of robotics and artificial intelligence, where today’s policies fail to generalize and dexterous manipulation in human environments remains an open frontier. I build full-stack robotic systems—drawing on machine learning, kinematics and dynamics, and cognitive science—toward anthropomorphic intelligence that perceives, reasons, and acts under physical and social constraints in reactive human environments.

Concretely, my work spans scalable evaluation and representation of manipulation behaviors, human-grounded multimodal perception for action policies and world models, and sim-to-real transfer for dexterous manipulation.

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 cung1 [at] johnshopkins [dot] edu.

"We keep moving forward, opening new doors, and doing new things, because we're curious and curiosity keeps leading us down new paths." — Walt Disney

Advisors

Homanga Bharadhwaj Research Scientist, Meta Reality Labs

Greg Hager Director, NSF CISE & Amazon Robotics

Dieter Fox Founding Director, Seattle NVIDIA Robotics Lab

Siddhartha Srinivasa Founding Director, Amazon Robotics AI

Shishir Shah Chief AI Officer, University of Oklahoma

Mentors

Yi Ru (Helen) Wang PhD Student, University of Washington

Vuong (Dustin) Nguyen Senior AI Research Scientist, AutoDesk AI

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 Foundation Models Meet Embodied Agents Workshop at CVPR 2026 Workshop on Scalable Robot Learning Systems at CVPR 2026 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 ICRA 2026 Eval&Deploy Workshop at CoRL 2025 (Oral Spotlight) Bimanual Manipulation: Advancing Human-Humanoid Interaction and Collaboration Workshop at 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 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.