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Faculty Research

Explore the various research specialties our faculty bring to the department and our students!

Faculty research areas are listed in the following “searchable” list below.  Feel free to contact the faculty to discuss their research and opportunities available to students.

Faculty Research Areas

Software & Systems

Computational Science and Engineering

Software Engineering

Software Engineering focuses on improving and advancing methodologies that facilitate systematic development of quality software. As software become more complex and more prevalent, the field extends to include new research directions, e.g., apps and app store analysis, and leverage 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: Bogdan DitElena ShermanJim Buffenbarger

Configuration Management

This research focuses on an intersection of Software Engineering and Programming Languages: Software Configuration, Version Control, and Build Systems.

Faculty: Jim Buffenbarger

Cloud Computing

Distributed Systems

Faculty: Casey Kennington

Parallel Computing

High Performance Computing

Operating Systems

Faculty: Jidong Xiao

Computer and Wireless Networks

Works are in the areas of mobile computing, which includes: data collection and analysis in heterogeneous networks; edge and cloud computing on large data; coexistence of heterogeneous wireless mobile devices.

Faculty: Yantian Hou

Data Science

Informational Retrieval

Big Data and Data Science

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: Amit JainCasey KenningtonEdoardo SerraFrancesca SpezzanoSteven Cutchin

Data Mining

Social Analysis and Mining

Social Analysis and 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

User Interaction

Human Computer Interaction

Human-Robot Interaction

Human-Robot Interaction (HRI) is a field of study dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. Interaction, by definition, requires communication between robots and humans.

Faculty: Casey Kennington

Natural Language Processing

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

Graphics and Visualization

Faculty: Steven Cutchin

Machine Intelligence

Natural Language Processing

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

Machine Learning

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: Casey Kennington, Sole PeraMichael EkstrandTim Andersen

Artificial Intelligence

Faculty: Casey KenningtonTim Andersen (Artificial Neural Networks)

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