Computer Science (MCS) Program Details
Degree Requirements
❱ Required coursework
❱ Qualifying or comprehensive examination
❱ Graduate School writing proficiency requirement
❱ Graduate School Responsible Conduct of Research (RCR) requirement
❱ Thesis (for MCS thesis option)
❱ Final oral examination (for MCS thesis option)
Research Specializations
❱ Software engineering
❱ Cybersecurity
❱ Algorithms and machine learning
❱ Data communications
❱ Computer systems
Research Opportunities
MCS students engage with faculty in research related to cybersecurity, data science, and other areas of contemporary research in the field.
Key Research Labs
Affective Biometrics Laboratory
Affective Biometrics Laboratory
Director: Dr. Gloria Washington
The Affective Biometrics Lab (ABL) at Howard seeks to investigate human emotion and identity using heart rate, skin temperature, brain waves, and body language gathered from images, video, and biological sensors. Research projects include an analysis of ear biometrics from 2-D images using advancements in deep learning; an investigation of the use of codeswitching to hide sentiment of social media data; and an NSF, Amazon, and Salesforce-funded project exploring empathy and microaggressions in person-to-person speech.
Data Science and Cybersecurity Center (DSC2)
Data Science and Cybersecurity Center
Director: Dr. Danda B. Rawat
The Data Science and Cybersecurity Center (DSC2) at Howard brings together a group of multidisciplinary faculty and student researchers to solve critical contemporary problems related to cybersecurity and data science, contributing new research insights on cyber-actor behavior, threat modeling, and intelligence, modeling and analysis, cyber-event forecasting, cyber resilience and defense, and data science.
Research Interests
Faculty Research Interests
A sampling of research interests
- Internet of Things and emerging networked systems
- Smart cities and intelligent transportation systems
- Design patterns for consensus algorithms in blockchain solutions
- Human-centered and affective computing
- Security engineering with machine learning for adversarial resiliency
- Design of smart spaces to support immersive learning
- Applying machine learning to data analysis in gene expression data, social media data, and biological data
- Real-time embedded systems
- Supply chain safety
- Bioinformatics and biometric-recognition
- Side-channel attacks and security instrumentation
- Energy-efficient computing and low-power design
- Use of codeswitching to hide sentiment of social media data
Program of Study*
CORE COURSES (18 CR)
CSCI 500 Socially Relevant Computing
CSCI 510 Computer Architecture
CSCI 551 Adv Software Engineering I
CSCI 570 Advanced Algorithms
CSCI 572 Computability & Complexity
CSCI 600 Research Methods
CSCI 680 Advanced Operating Systems
ELECTIVE COURSES
Thesis Track (6 CR) or Non-Thesis Track (12 CR)
A sampling of courses below (Students choose 3 electives for the thesis option or 4 electives for the non-thesis option)
CSCI 552 Advanced Software Engineering II
CSCI 653 Cybersecurity I
CSCI 652 Special Topics in Cybersecurity
CSCI 659 Capstone in Security
CSCI 676 Cybersecurity for Net CPS/IoT
CSCI 660 Artificial Intelligence
CSCI 675 Intro to Machine Learning
CSCI 673 Knowledge Engineering and Mmgt
CSCI 785 Advanced Topics in Artificial Intelligence
ECE 460 Wireless Communications
CSCI 548 Data Communications
CSCI 550 Network Modeling and Analysis
CSCI 786 Advanced Topics in Computer Networks
ECE 487 Telecommunications
CSCI 634 Advanced Modeling and Simulation
CSCI 682 Parallel Computing
CSCI 560 Performance Modeling
ECE 416 Microprocessors and Microcomputers
CSCI 632 Advanced Database Systems
CSCI 532 Advanced Operations Research
CSCI 574 Computational Biology
CSCI 788 Advanced Topics in Computational Systems
CSCI 652 Special Topics
THESIS or INDEPENDENT RESEARCH PROJECT
Thesis (6 CR) or non-thesis Independent Research (3 CR)
*Courses included in the sample program of study are subject to change. Students should consult with their programs regarding their required program of study.
Admission to Candidacy
Students are admitted to formal candidacy by the Graduate School when they have completed the required coursework, passed the qualifying or comprehensive examination, submitted an approved topic for research, and been recommended by the Department. Candidates must also have satisfied the Graduate School writing proficiency requirement and Responsible Conduct of Research (RCR) requirement.
Graduate Funding
Graduate research or teaching assistantships may be available at the department level that provide tuition remission and/or a stipend during the academic year. Research assistants and teaching assistants work no more than 20 hours a week under the program's direction, usually in support of faculty research (research assistants) or in support of assigned courses (teaching assistants).