Hu Ding

Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016).

Education:
• Ph.D. in Computer Science, State University of New York at Buffalo (2015)
• B.S. in Mathematics, Sun Yat-Sen University (2009)

Research Interests:
Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis.

Publication Trends:
His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers.

Awards and Honors:

• Young Investigator Award, Ministry of Science and Technology (2021)
• Simons-Berkeley Research Fellowship (2015-2016)
• CCF Committee Member for Theoretical CS and Big Data (2021)

Grants and Projects:

• USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021)
• MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021)

Research Group:
Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.