Furong Huang is an Assistant Professor in the Department of Computer Science at the University of Maryland. Specializing in trustworthy machine learning, AI for sequential decision-making, and high-dimensional statistics, Dr. Huang focuses on applying theoretical principles to solve practical challenges in contemporary computing.
Her research centers on creating reliable and interpretable machine learning models that operate effectively in real-world settings. She has also made significant strides in the realm of sequential decision-making, aiming to develop algorithms that not only optimize performance but also adhere to ethical and safety standards.
Academic Positions
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2017 - present 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 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 2022
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
NSF Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII), “Principled Methods for Learning and Understanding of Neural Networks.”
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.”
JP Morgan Faculty Research Award 2022, 2020 & 2019
JP Morgan Faculty Research Award 2022, “Repelling Security Vulnerabilities in AI-augmented Financial Decision-Making Systems”.
JP Morgan Faculty Research Award 2020, “Robust, Private and Fair ML for Financial Models”.
JP Morgan Faculty Research Award 2019, “Methods to Identify Communities and Trading Behavior over Financial Data Streams”.
Adobe Faculty Research Award 2017
Adobe Faculty Research Award 2017, “Understanding User Features with Text, Social and Behavior Data through Deep Tensor Neural Network Learning”.
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
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Contact Me
4124 The Brendan Iribe Center
Department of Computer Science
Center for Machine Learning
University of Maryland
College Park, MD 20740