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faculty
Ming Shao, PhD
Assistant Director of the Marine & UnderSea Technology Research Program (MUST)
Computer & Information Science
Associate Professor
Computer & Information Science
Contact
508-910-6893
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Dion 303A
Contact
508-910-6893
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Dion 303A
Education
| 2016 | Northeastern University | PhD in Electrical and Computer Engineering |
| 2010 | Beihang University | ME in Computer Science |
| 2007 | Beihang University | BS in Applied Mathematics |
| 2006 | Beihang University | BE in Computer Science |
Teaching
Programs
Programs
- Computer Science BS, BS/MS
- Computer Science Cybersecurity
- Computer Science Graduate Certificate
- Computer Science MS
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Engineering and Applied Science PhD
- Mobile Applications Development
- Software Engineering
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.
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.
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.
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.