The Computer Science Department offer the degrees Master of Science and Doctor of Philosophy in Computer Science. These degree programs demand academic rigor and depth yet also address real-world problems.
The department has eight areas of research activity that stem from the core fields of Computer Science and in many cases, individual research projects encompass more than one research area.
An interdisciplinary research area drawing from traditional computer science, engineering and cognitive science. Research themes include artificial intelligence, human-robot interaction, and augmented reality, focusing on integrating computer vision and perception, learning and adaptation, natural language understanding and generation, and decision making into unified robot systems.
Research in Applied Algorithms and Data Structures combines classical algorithms research (characterized by the development of elegant algorithms and data structures accompanied by theory that provides mathematical guarantees about performance) and applications research (consisting of the actual development of software accompanied by empirical evaluations on appropriate benchmarks). Applications include cheminformatics and material science, crowdsourcing, data analytics, mobile computing, networking, security and privacy, the smart grid and VLSI design automation.
This area focuses on sensing information about the real world, augmenting visualization of reality by overlaying virtual information on the real world, and enabling user to interact with and digitally manipulate the information.
CS for All: Computer Science Education
This area encompasses research on STEM recruitment and diversity, K-12 computing education and computing/engineering education at the university level. Current projects include an on-campus computing outreach program tailored for girls across a broad age range; professional development opportunities for CS high school teachers; and incorporating ethics into core and elective computing courses.
Research includes usable security and privacy in web/mobile/cloud/cyber-physical systems, vulnerability measurement and analysis and security-privacy education.
Our high-performance computing research focuses on using compiler and runtime techniques to optimize Big Data and machine learning applications on heterogeneous systems.
Includes research in developing mathematical foundations and algorithm design needed for computers to learn. Focus areas include fundamental research in machine learning and numerical methods, as well as developing novel algorithms for bioinformatics, data mining, computer vision, biomedical image analysis, parallel computing, natural language processing and data privacy.
Research aims to enable emerging wireless applications via networks and systems support, ranging from hardware design to algorithms development and software integration, from credible simulations to actual system deployment and testing.
Additional program information is available at the Computer Science website.
Doctor of Philosophy
- Computer Science
Master of Science
- Computer Science
- A bachelor’s degree with a grade-point average of 3.0 on a 4.0 scale.
- Completion of two semesters of calculus, and computer science courses in programming concepts, data structures, computer organization, software engineering and discrete math.
- Graduate Record Examination (GRE) with quantitative section score of 151 or higher (or 650 on the old scale). Applicants who have graduated from Mines within the past five years are not required to submit GRE scores.
- For international applicants or applicants whose native language is not English, a TOEFL score of 79 or higher (or 550 for the paper-based test, 213 for the computer-based test) is required. In lieu of a TOEFL score, an IELTS score of 6.5 or higher will be accepted.
CSCI508. ADVANCED TOPICS IN PERCEPTION AND COMPUTER VISION. 3.0 Semester Hrs.
Equivalent with EENG508
CSCI522. INTRODUCTION TO USABILITY RESEARCH. 3.0 Semester Hrs.
CSCI542. SIMULATION. 3.0 Semester Hrs.
Equivalent with MACS542
CSCI544. ADVANCED COMPUTER GRAPHICS. 3.0 Semester Hrs.
Equivalent with MATH544
CSCI546. WEB PROGRAMMING II. 3.0 Semester Hrs.
CSCI547. SCIENTIFIC VISUALIZATION. 3.0 Semester Hrs.
Equivalent with MATH547
CSCI555. GAME THEORY AND NETWORKS. 3.0 Semester Hrs.
Equivalent with CSCI455
CSCI560. FUNDAMENTALS OF COMPUTER NETWORKS. 3.0 Semester Hrs.
CSCI561. THEORY OF COMPUTATION. 3.0 Semester Hrs.
CSCI562. APPLIED ALGORITHMS AND DATA STRUCTURES. 3.0 Semester Hrs.
CSCI563. PARALLEL COMPUTING FOR SCIENTISTS AND ENGINEERS. 3.0 Semester Hrs.
- CSCI564. ADVANCED COMPUTER ARCHITECTURE. 3.0 Semester Hrs.
- CSCI565. DISTRIBUTED COMPUTING SYSTEMS. 3.0 Semester Hrs.
- CSCI568. DATA MINING. 3.0 Semester Hrs.
- CSCI571. ARTIFICIAL INTELLIGENCE. 3.0 Semester Hrs.
- CSCI572. COMPUTER NETWORKS II. 3.0 Semester Hrs.
- CSCI573. HUMAN-CENTERED ROBOTICS. 3.0 Semester Hrs.
- CSCI574. THEORY OF CRYPTOGRAPHY. 3.0 Semester Hrs.
Equivalent with MATH574
- CSCI575. MACHINE LEARNING. 3.0 Semester Hrs.
Equivalent with MACS575
- CSCI576. WIRELESS SENSOR SYSTEMS. 3.0 Semester Hrs.
- CSCI580. ADVANCED HIGH PERFORMANCE COMPUTING. 3.0 Semester Hrs.
- CSCI585. INFORMATION SECURITY PRIVACY. 3.0 Semester Hrs.
- CSCI598. SPECIAL TOPICS. 6.0 Semester Hrs.
- CSCI599. INDEPENDENT STUDY. 0.5-6 Semester Hr.
- CSCI691. GRADUATE SEMINAR. 1.0 Semester Hr.
- CSCI692. GRADUATE SEMINAR. 1.0 Semester Hr.
Equivalent with MACS692, MATH692
- CSCI693. WAVE PHENOMENA SEMINAR. 1.0 Semester Hr.
- CSCI698. SPECIAL TOPICS. 6.0 Semester Hrs.
- CSCI699. INDEPENDENT STUDY. 0.5-6 Semester Hr.
- CSCI700. MASTERS PROJECT CREDITS. 1-6 Semester Hr.
- CSCI707. GRADUATE THESIS / DISSERTATION RESEARCH CREDIT. 1-15 Semester Hr.
Priority: Jan 5, 2019
International: March 1, 2019
Domestic: July 1, 2019