Trustworthy foundation models · University of Maryland

AI systems that reason, act, recover, and improve.

Furong Huang's lab studies foundation models as decision-making systems—models that understand physical and digital worlds, reason under uncertainty, act through tools or bodies, and learn from failure.

Associate Professor of Computer Science · University of Maryland

Furong Huang
Research visionTrust should be designed as a closed loop.
235Scholarly works
13Current Ph.D. students
19Doctoral alumni
15Alumni destinations

About Furong

Research leadership across machine learning, robotics, and trustworthy AI.

Furong Huang is a tenured associate professor of computer science at the University of Maryland, building learning systems that connect mathematical foundations with consequential real-world behavior.

Research support

Public and industrial partners.

Research has been supported by NSF, DARPA, ONR, AFOSR, Good Ventures through Open Philanthropy, NVIDIA, Microsoft, Adobe, Apple, JP Morgan, Capital One, Amazon, and Peraton.

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Where talent goes

The lab’s strongest record is its people.

Furong Lab alumni carry their research into leading AI organizations, technology companies, and academic institutions. Their trajectories are part of the lab’s impact—not a footnote to it.

OpenAIMIT CSAILGoogleMetaAppleNetflixYaleAmazonCapital OneAPQX
Meet the extended Furong Lab community

Research architecture

Three connected loops for capable and trustworthy AI.

The lab connects models of the world, policies for allocating reasoning, and mechanisms that turn failures into safer future behavior.

Selected recognition

Recognition for research that connects rigor with impact.

A concise selection from the complete awards and grants record maintained in the CV.

2026

NVIDIA Academic Grant

Support for frontier academic research in artificial intelligence.

2024

NeurIPS workshop Best Paper Award

New Frontiers in Adversarial Machine Learning (AdvML Frontier).

2024

NSF NAIRR Pilot Award

Guardians of Integrity in AI: Establishing Trust, Originality, and Ethical Standards.

2023

Microsoft Accelerate Foundation Models Research Award

Recognition and support for foundation-model research.

2022

MIT Technology Review Innovators Under 35

Asia Pacific honoree for work on trustworthy artificial intelligence.

2019–2022

Three JP Morgan Faculty Research Awards

AI security, robust and fair financial models, and learning over financial data streams.

Selected work

A small, deliberate portfolio of signature and emerging work.

Twelve papers show the arc from foundational contributions and widely used benchmarks to the lab’s current frontier. This is an editorial selection, not a second publication list.

Latest signals

News from the lab.

Talks, new research, student milestones, and recognitions—kept concise and connected to the underlying work.

Teaching

Generative AI Agents at DeepLearn 2026

An advanced summer-school course on agent architectures, reasoning, alignment, safety, and world models.

Talk

Building Self-Improving Foundation Models

Auditors, actuators, and amplifiers for trustworthy AI.

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Talk

Reasoning as Control

Adaptive test-time compute for planning agents.

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Research with us

Build foundation models that can earn trust through their behavior.

Explore current work, meet the research group, or get in touch about collaboration and student opportunities.

Meet the group