Furong Huang is an Associate Professor in the Department of Computer Science at the University of Maryland. She is also affiliated with the Center for Machine Learning at Institute for Advanced Computer Studies, the Maryland Robotics Center, the Applied Mathematics, Statistics, and Scientific Computation Program, and the Department of Electrical and Computer Engineering.
Furong’s lab studies the next generation of AI systems: foundation models as decision-making systems. We are interested in models that do more than predict the next token — models that can understand physical and digital worlds, reason under uncertainty, plan with limited computation, act through tools or bodies, and improve from their own failures. Our research centers on three pillars: world models for learning what can happen, reasoning control for deciding how to think, and trustworthy self-improvement for detecting, recovering from, and learning from failure. Together, these directions aim to make AI systems more reliable, efficient, adaptive, and trustworthy in complex real-world environments.
She has received multiple best paper awards and research grants from organizations such as DARPA, ONR, AFOSR, NASA, NSF, Open Philanthropy, as well as from companies including Apple, Capital One, JP Morgan, Microsoft, Adobe, Peraton, and more. Her publications appear in leading venues including NeurIPS, ICML, ICLR, and CVPR, and her work has been recognized in the media for advancing the safety and integrity of modern AI systems.
Student Placement Update:
Huge congratulations to all the Spring/Summer 2025 graduates!
- Bang An joined OpenAI
- Sicheng Zhu joined OpenAI
- Xiaoyu Liu joined Google
- Yuhang Zhou joined Meta
- Yuancheng Xu joined Netflix
- Dehao Yuan joined Capital One
- Marco Bornstein joined a Formula 1 technology company APQX
Their work spans some of the most important areas in modern AI, including AI Security, Agentic AI, Alignment, Ethics, Fairness, and Responsible AI. With deep experience in Generative AI, LLMs, VLMs, and VLAs, they are driving advances in Weak-to-Strong Generalization and AI safety.
We’re incredibly proud of their achievements and can’t wait to see what they accomplish next. Don’t miss the chance to connect with these future leaders. Learn more about them here:
Graduating Lab Members
Academic Positions
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2024 - present Tenured Associate Professor
University of Maryland
Department of Computer Science -
2017 - 2024 TTK Assistant Professor
University of Maryland
Department of Computer Science -
2016-2017 Postdoctoral Researcher
Microsoft Research NYC
Mentors: John Langford, Robert Schapire -
2010-2016 Doctoral Researcher
University of California, Irvine
Advisor: Anima Anandkumar
Selected Awards
MIT TR35
MIT Technology Review Innovators Under 35 Asia Pacific 2022
Visionaries
She makes AI more trustworthy by developing models that can perform tasks safely and efficiently in unseen environments without human oversight.
AI Researcher of the Year
Finalist of AI in Research – AI researcher of the year, 2022 Women in AI Awards North America.
Special Jury Recognition – United States, 2022 Women in AI Awards North America.
National Science Foundation Awards
National Artificial Intelligence Research Resource (NAIRR) Pilot Awardee.
NSF Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII).
NSF Div Of Information & Intelligent Systems (IIS) Direct For CISE, “FAI: Toward Fair Decision Making and Resource Allocation with Application to AI-Assisted Graduate Admission and Degree Completion.”
Industrial Faculty Research Awards
Microsoft Accelerate Foundation Models Research Award 2023.
JP Morgan Faculty Research Award 2022.
JP Morgan Faculty Research Award 2020.
JP Morgan Faculty Research Award 2019.
Adobe Faculty Research Award 2017.
Research Projects
My research stands at the forefront, focusing on robustness, efficiency, and fairness in AI/ML models, vital in fostering an era of Trustworthy AI that society can rely on. My research fortifies models against spurious features, adversarial perturbations, and distribution shifts, enhances model, data, and learning efficiency, and ensures long-term fairness under distribution shifts.
With academic and industrial collaborators, my research has been used for cataloguing brain cell types, learning human disease hierarchy, designing non-addictive pain killers, controlling power-grid for resiliency, defending against adversarial entities in financial markets, updating/finetuning industrial-scale model efficiently and etc.
Specific Area of Research
Click Below
Contact Me
4124 The Brendan Iribe Center
Department of Computer Science
Center for Machine Learning
University of Maryland
College Park, MD 20740