Ming Shao

faculty

Ming Shao, PhD

Associate Professor

Computer & Information Science

Machine Intelligence and Data analytics (MIND) Lab

508-910-6893

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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

Models of sequential, parallel, and distributed computations. The Chomsky hierarchy of formal languages and their accepting machines are studied in detail. The relationship of these languages and machines to computer programs is presented. Influence of a Turing machine and related formalisms on modern computing are studied. Decidability of decision problems is explained. Several models of parallel and distributed computations are introduced and compared.

Study under the supervision of a faculty member in an area covered in a regular course not currently being offered. Conditions and hours to be arranged.

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

Prerequisite: Completion of three core courses. Research leading to submission of a formal thesis. This course provides a thesis experience, which offers a student the opportunity to work on a comprehensive research topic in the area of computer science in a scientific manner. Topic to be agreed in consultation with a supervisor. A written thesis must be completed in accordance with the rules of the Graduate School and the College of Engineering. Graded A-F.

Prerequisite: Completion of three core courses. Research leading to submission of a formal thesis. This course provides a thesis experience, which offers a student the opportunity to work on a comprehensive research topic in the area of computer science in a scientific manner. Topic to be agreed in consultation with a supervisor. A written thesis must be completed in accordance with the rules of the Graduate School and the College of Engineering. Graded A-F.

Prerequisite: Completion of three core courses. Research leading to submission of a formal thesis. This course provides a thesis experience, which offers a student the opportunity to work on a comprehensive research topic in the area of computer science in a scientific manner. Topic to be agreed in consultation with a supervisor. A written thesis must be completed in accordance with the rules of the Graduate School and the College of Engineering. Graded A-F.

Prerequisite: Completion of three core courses. Research leading to submission of a formal thesis. This course provides a thesis experience, which offers a student the opportunity to work on a comprehensive research topic in the area of computer science in a scientific manner. Topic to be agreed in consultation with a supervisor. A written thesis must be completed in accordance with the rules of the Graduate School and the College of Engineering. Graded A-F.

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.

Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.

Teaching

Online and Continuing Education Courses

Coverage of advanced topics of data mining and its applications. The course will review related mathematics and then focus on data mining core algorithms and advanced modeling including regression, dimensionality reduction, support vector machines, clustering, graph theory, and frequent pattern mining. The course will also explore several real-world problems and discuss strategies for large-scale data. Requires pre-knowledge from an undergraduate course on algorithms and data structures.

Research

Research awards

  • $ 499,999 awarded by Commonwealth of Massachusetts for Mass Skills - Intelligent Industrial Robotics and Cyber Security Test Bed
  • $ 207,695 awarded by National Fish And Wildlife Foundation | AIS, INC for Integrating an Intelligent Discard Chute into New England Groundfish Electronic Monitoring (MA)
  • $ 512,105 awarded by National Science Foundation for Collaborative Research: CPS: Medium: AI-Boosted Precision Medicine through Continual in situ Monitoring of Micro-tissue Behaviors on Organs-on-Chips
  • $ 498,970 awarded by National Science Foundation for CAREER: Enabling Continual Multi-view Representation Learning: An Adversarial Perspective
  • $ 92,404 awarded by Massachusetts Institute of Technology for Incorporating Image Recognition and Machine Learning into the NE Multispecies Groundfish Electronic Programs to Quantify Species and Sizes of Discards

Research

Research interests

  • Predictive modeling
  • Adversarial machine learning
  • Continual and incremental machine learning
  • Robust and multi-view representation learning
  • Health informatics

Ming Shao received BE degree in Computer Science, BS degree in Applied Mathematics, and ME degree in Computer Science from Beihang University, Beijing, China, in 2006, 2007, and 2010, respectively. He received PhD degree in Computer Engineering from Northeastern University, Boston, MA, in 2016. He is currently an Associate Professor affiliated with the College of Engineering at the University of Massachusetts Dartmouth. His current research interests include predictive modeling, adversarial machine learning, continual and incremental machine learning, robust and multi-view representation learning, and health informatics. He published over 90 articles in prestigious journals and conference proceedings. Since joining UMass Dartmouth, Dr. Shao has secured funding from several sources for $3.17M. He received the NSF CAREER Award in 2022 and 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 was the reviewer for many IEEE Transactions journals, including TPAMI, TKDE, TNNLS, TIP, and TMM. He also served on the (senior) program committee for top-tier conferences such as AAAI, IJCAI, CVPR, ICCV, NeurIPS, ICML, ICLR, KDD, ICDM, CIKM, ACM-MM, etc. He is the Associate Editor of IEEE Transactions on Image Processing, IEEE Computational Intelligence Magazine, and SPIE Journal of Electronic Imaging. He is a member of IEEE.