Faculty Research

Software Engineering focuses on improving and advancing methodologies that facilitate systematic development of quality software. As software becomes more complex and more prevalent, the field extends to include new research directions, e.g., apps and app store analysis, and leverages other research areas such as artificial intelligence and machine learning. Thus, the field of Software Engineering spans across a wide collection of topics ranging from human and social aspects of software engineering to formal methods, validation and verification of software; from empirical software engineering to software specification and modeling languages.

Faculty: Elena Sherman, Nasir Eisty, Jim Buffenbarger

Faculty: Min Long

Data Science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. In other words, by definition it is interdisciplinary, combining the fields of mathematics, statistics, information science, computer science, and other data-driven domains of scientific study. Like all sciences which require data to make scientific advancements, Data Science too advances with data. However, more focus in Data Science is put on the nature of the data itself: data analytics, “Big Data,” (i.e., structured and unstructured), visualization, and computation; i.e., the creation of novel data-driven software for real-world applications.

Faculty: Steven Cutchin, Amit Jain, Casey Kennington, Jianshu Liu, Edoardo Serra, Francesca Spezzano, Jun Zhuang

Faculty: Edoardo Serra, Gaby Dagher, Jun Zhuang

Social Media Mining deals with data science applied to social media. It is the process of representing, analyzing, and extracting predictive models form social media data (social network, micro blogs, wikis, etc.). It leverages many disciplines such as data mining, machine learning, social network analysis, sociology, optimization, etc.

Faculty: Francesca Spezzano

Natural Language Processing (NLP) aims to help computers understand, represent, and generate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. NLP draws inspiration from linguistic subfields such as phonology, syntax, and semantics, while also leveraging data using machine learning.

Faculty: Casey Kennington, Jun Zhuang

There is an increasing need in industry and academic research for students to graduate with an understanding of theory and practical skills related to data science and machine learning. Knowledge and experience in machine learning requires basic data science skills, knowledge of algorithms used for machine learning, as well as practical experience in common application areas.

Faculty: Edoardo Serra, Tim Andersen, Casey Kennington, Jun Zhuang

Faculty: Casey Kennington, Jun Zhuang, Tim Andersen, Edoardo Serra

Faculty: Gaby Dagher, Hoda Mehrpouyan (Cyber-Physical System Security), Jyh-Haw Yeh, Huadi Zhu (Cyber-Physical System Security)

Faculty: Gaby Dagher, Hoda Mehrpouyan, Edoardo Serra, Jyh-haw Yeh

Faculty: Jerry Fails

Faculty: Jerry Fails

Natural Language Processing (NLP) aims to help computers understand, represent, and generate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. NLP draws inspiration from linguistic subfields such as phonology, syntax, and semantics, while also leveraging data using machine learning.

Faculty: Casey Kennington

Faculty: Steven Cutchin

Faculty: Amit Jain, Tim Andersen

Faculty: Jun Zhuang, Min Long

Mobile computing, data collection and analysis in heterogeneous networks; edge and cloud computing on large data; coexistence of heterogeneous wireless mobile devices.

Faculty:

Faculty: Steven Cutchin, Amit Jain

Faculty: Gaby Dagher, Jianshu Liu, Jyh-haw Yeh