Computer Science (MCS) Program Details

Degree Requirements

   ❱  Required coursework
   ❱  Qualifying 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 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). Please see the Funding website for more detailed information.