Zhongfei Zhang
Also known as Zhongfei Mark Zhang, Mark Zhang
Zhongfei “Mark” Zhang is a computer scientist at Binghamton University whose work on machine learning and data mining centers on multimedia and multimodal data. He earned degrees at Zhejiang University and a PhD at the University of Massachusetts Amherst, worked at the University at Buffalo, and joined Binghamton in 1999. His 2009 book Multimedia Data Mining and his 2010 book Relational Data Clustering came from CRC Press, and he coauthored a 2013 survey of appearance models for visual object tracking and a 2016 ICML paper on energy-based anomaly detection. IEEE made him a Fellow in 2020, and the International Association for Pattern Recognition followed in 2022. At Binghamton, he is a professor in the School of Computing and directs the Multimedia Research Laboratory.
- Now at
- Binghamton University and School of Computing
Timeline
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By 1999
Joining the Binghamton faculty
Zhang earns a BS in electronics engineering and an MS in information science at Zhejiang University, then a PhD in computer science at the University of Massachusetts at Amherst. At the University at Buffalo he works as a research scientist at the Center of Excellence for Document Analysis and Recognition and on the computer science and engineering faculty, before joining the Binghamton faculty in fall 1999, where his primary research areas have been machine learning and data mining.
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2009
Multimedia Data Mining monograph
In 2009, he and Ruofei Zhang publish Multimedia Data Mining: A Systematic Introduction to Concepts and Theory with CRC Press, which covers the theory and applications of multimedia data mining and is the first monograph on the subject.
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2010
Relational Data Clustering monograph
With Bo Long and Philip S. Yu, he publishes the CRC Press monograph Relational Data Clustering: Models, Algorithms, and Applications in 2010, presenting models, algorithms and applications for relational data.
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2013
A survey of appearance models
In 2013, he coauthors a survey of 2D appearance models for visual object tracking with Xi Li, Weiming Hu, Chunhua Shen, Anthony Dick and Anton van den Hengel. The survey splits appearance modeling into visual representation and statistical modeling.
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2016
Energy models for anomaly detection
For ICML 2016, he coauthors Deep Structured Energy Based Models for Anomaly Detection with Shuangfei Zhai, Yu Cheng and Weining Lu. The models use the output of a deterministic deep neural network as the energy function and detect anomalies with the energy score or the reconstruction error.
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2020
Named an IEEE Fellow
He is named an IEEE Fellow in the class of 2020 for contributions to multimodal data content understanding and mining.
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2022
An IAPR Fellowship
The International Association for Pattern Recognition names him a Fellow in 2022 for contributions to data understanding and mining.
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As of October 2026
Professor in the School of Computing
He is a professor in Binghamton's School of Computing and directs its Multimedia Research Laboratory. In 2025, he gives a keynote at the International Conference on Image, Vision and Intelligent Systems on out-of-distribution learning, addressing high-confidence incorrect predictions by deep neural networks on inputs from unfamiliar distributions.
Career
- NowBinghamton UniversityProfessor
- NowSchool of ComputingDirector
- University at Buffalo, State University of New YorkResearch Scientist
Education
Sources and credits
Photo: We have no published photo of Zhongfei Zhang yet. Send us a free-licensed photo.
- Our Faculty | School of Computing | Binghamton University
- Zhang named IEEE fellow for computer science research - Binghamton News
- Multimedia Data Mining: A Systematic Introduction to Concepts and Theory - 1st Edition
- Relational Data Clustering: Models, Algorithms, and Applications - 1st Edition
- A Survey of Appearance Models in Visual Object Tracking
- Deep Structured Energy Based Models for Anomaly Detection
- Chronological List of IAPR Fellows
- ICIVIS | Keynote Speakers 2025

