Ming Shao, PhD

Assistant Professor

Computer & Information Science

Machine Intelligence and Data analytics (MIND) Lab

508-910-6893

ntibpAvnbtte/fev

Dion 303A

Education

2016Northeastern UniversityPhD in Electrical and Computer Engineering
2010Beihang UniversityME in Computer Science
2007Beihang UniversityBS in Applied Mathematics
2006Beihang UniversityBE in Computer Science

Teaching

Programs

Teaching

Courses

Advanced coverage of machine learning algorithms and applications to computer vision, data mining and social media. Specifically, the course focuses on data pre-processing, feature extraction, supervised/unsupervised learning, graphical model, deep learning, and other advanced machine learning topics. The course will also explore several real-world problems, e.g., visual detection/recognition, topics discovery, social media analytics using machine learning approaches.

An introduction to data analysis with a focus on visualization. Topics include: visualization of scalar, vector and tensor data; software tools for image, volume and information visualization and analysis; descriptive statistics; time dependent data; data patterns; analyzing propositions, correlations, and spatial relationships. Application of these topics to natural sciences and engineering are discussed. This course will also introduce programming basics including data types, variable declarations, arithmetic expressions, conditional statements, function prototypes, standard libraries, stacks, queues, file processing, structures, unions, unix systems, file systems, and some I/0.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.

Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.

Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.

Research

Research Interests

  • Transfer learning, and multi-modality recognition
  • Social media analytics: kinship verification, occupation recognition
  • Auto-encoder and deep feature learning
  • Graph approximation and clustering
  • Outlier detection and analysis

Ming Shao received B.E. degree in Computer Science, B.S. degree in Applied Mathematics, and M.E. degree in Computer Science from Beihang University, Beijing, China, in 2006, 2007, and 2010, respectively. He received Ph.D. degree in Computer Engineering from Northeastern University, Boston MA, 2016. He is a tenure-track Assistant Professor affiliated with College of Engineering at the University of Massachusetts Dartmouth since 2016 Fall. His current research interests include predictive modeling, adversarial machine learning, robust visual representation learning, and health informatics. He was the recipient of the Presidential Fellowship of State University of New York at Buffalo from 2010 to 2012, and the best paper award winner of IEEE ICDM 2011 Workshop on Large Scale Visual Analytics, and best paper award candidate of ICME 2014. He has served as the reviewers for many IEEE Transactions journals including TPAMI, TKDE, TNNLS, TIP, and TMM. He has also served on the program committee for the conferences including AAAI, IJCAI, ECAI, CVPR, ICCV, ICLR, ICDM, IEEE BigData, CIKM, ACM-MM, etc. He is the Associate Editor of SPIE Journal of Electronic Imaging, and IEEE Computational Intelligence Magazine. He is a member of IEEE.

Latest from Ming

Request edits to your profile