Ming Shao, PhD
Computer & Information Science
|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|
- Computer Science BS, BS/MS
- Computer Science MS
- Computer Science Graduate Certificate
- Computer Science Cybersecurity Concentration
- Data Science BS, BS/MS
- Master's in Data Science MS
- Engineering and Applied Science PhD
- Mobile Applications Development
- Software Engineering Option
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.
Foundations of computer vision. Image formats, projection models, regions, filters, edge detection, segmentation, shape description and representation, object recognition and understanding, and stereo-vision are discussed.
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.
Offered as needed to present advanced material to graduate students.
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.
- 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.