Computer Science

View the Mines Academic Catalog for more program-specific information.

Algorithmic Robotics

An interdisciplinary research area drawing from traditional computer science, artificial intelligence, cognitive science, philosophy and engineering.  Our laboratories leverage theories, methods and techniques from these fields to perform research on computer vision and perception, learning and adaptation, planning and manipulation, natural language understanding and generation, and decision making in the context of unified and/or networked robot systems, as well as into the social, cognitive and theoretical implications of algorithmic design choices.

Applied Algorithms

Our 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, blockchain, data analytics, edge computing, networking, Internet of Things and VLSI design automation.

Augmented Reality

Augmented reality is the process of augmenting the user’s view of the real world with computer-generated sensory information in the form of graphics (although sound can also be used). It is different from virtual reality, which completely replaces the user’s view of the real world with a simulated one. In AR, the user still sees the real world but the world is enhanced (or augmented) by virtual objects. For displaying the information, a head-mounted display can be used. More commonly, a hand-held device such as a smart phone or tablet is used. Virtual objects are overlaid on the live video images coming from the device’s camera.

A critical component in augmented reality is sensing the real world, in terms of recognizing objects and estimating the position and orientation (pose) of the user’s display relative to the real world. This information is critical in accurately rendering the virtual objects such that they are aligned with the real world. Another critical component is to automatically learn a model of the task being performed (such as a maintenance or assembly task) and recognize the state of the user’s progress through the task, in order to give guidance.

CS for All: Computer Science (CS) 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.
Cybersecurity

Mines CSP (Cyber Security and Privacy) research group is directed by Dr. Chuan Yue, and it consists of multiple PhD, master and undergraduate research assistants.

We focus on investigating the cyber security and privacy problems related to the web, mobile, cloud, cyber-physical and IoT systems as well as their users. We take four main approaches in our research:

1. Design novel systems and use techniques such as machine learning and program analysis to investigate security and privacy vulnerabilities
2. Design and perform novel user studies towards achieving usable security and privacy
3. Design novel mechanisms and software features to effectively strengthen the security and privacy protection capabilities of systems
4. Design and conduct novel security and privacy educational research

We actively collaborate with researchers in other areas and other disciplines, as well as with industry and government partners.

High-Performance Computing & Programming Languages

Mines High Performance Systems and Software (HypeSYs) research group is co-directed by Dr. Bo Wu, Dr. Jedidiah McClurg, and Dr. Mehmet Belviranli. The interests of this group lie within the broad fields of high performance computing (HPC), programming languages, compilers, and heterogeneous computer architectures.

Dr. Bo Wu’s research aims at building high-performance software systems for deep learning and graph applications. His focus is on leveraging domain knowledge to create novel compiler and runtime techniques to systematically optimize parallel computing efficiency, maximize memory bandwidth utilization and reduce computation redundancy.

Dr. Jedidiah McClurg’s research focuses on building new programming languages and tools to help programmers write better code. Formal methods such as verification (proving correctness) and synthesis (automatically generating correct code) are key components of this research. Dr. McClurg targets a broad spectrum of application areas, including networking, distributed systems, security and computer science education.

Dr. Mehmet Belviranli’s research is centered on improving the utilization of diversely heterogenous architectures. He develops runtimes, analytical models and programming solutions to increase the computing and energy efficiency of autonomous and embedded systems. The target application areas of Dr. Belviranli’s research include but are not limited to machine learning acceleration, object detection and tracking, and motion planning & kinematics computing for self-driving cars, autonomous drones and collaborative robots.

Machine Learning

Mines machine learning group is directed by Dr. Hua Wang, and consists of multiple PhD, master and undergraduate research assistants.
We focus on developing mathematical foundations and algorithms needed for computers to learn. Our research spans the areas of machine learning and data mining, as well as their applications in a number of practical areas, such as cheminformatics, bioinformatics, medical image analysis and computer vision. The goal of our research is to bridge the gap between computational solutions and real-world problems. Several of our current research topics include:

1. Robust learning models: Data collected from real-world problems are inevitably compromised by noises and outliers, which makes conventional learning models hard to achieve satisfactory performance. As a result, designing learning models that are insensitive to noises, particularly to outlying data points, is of great value for practice. We utilized state-of-the-art numerical methods and redesigned a number of most broadly used learning models to improve their robustness against noises.
2. Data fusion models: In many real-world applications, data is usually collected from different sources. For example, in disease diagnosis physicians can collect different types of data to evaluate different aspects of a patient, such as the physical data, imaging data, genetic data, etc. How to effectively integrate this data is playing a critical role in diagnosis. We have built many mixed-norm induced learning models, which can elegantly extract the most relevant information from massive raw data collected by various instruments.
3. Learning models for big data: With the recent development in technologies, we have accumulated vast amount of data, such as those in biology, information technology, to name a few. This data provides a wealth of information that is valuable for our lives. However, how to efficiently process the data with either a big number of samples or a big number features is a very challenging problem, which has aroused a lot of interests in both academia and industries. We are actively engaged in this area and have designed learning models with low computational complexity to deal with big data problems extracted from a variety of real-world domains.

Networked Systems

Continuing advances in the computational power, radio components, and memory elements have led to the proliferation of portable devices (e.g., intelligent sensors, actuators, RFID readers, PDAs, smartphone) with substantial processing capabilities and various networking interfaces. These devices are rapidly permeating a variety of application domains such as monitoring and remediation of an oil spill, underground mine safety, earth dam failure detection, and climate forecasting. These application areas align very well with Colorado School of Mines’ strategic areas: earth, energy and environment.

We conduct research and development within the realm of wireless networking and mobile computing, addressing challenges raised by the emerging pervasive computing environments and Internet of Things (IoT). Our overall research style involves the design of algorithms and protocols, the evaluation of concepts and ideas via both simulations and actual system building, and the development of applications using the algorithms and systems built. In addition to focusing on basic computer science research, we also actively conduct interdisciplinary research. Example contributions include using wireless robotic networks for oil refinery inspection and using wireless sensor networks for intelligent geosystems, subsurface contaminant monitoring and building monitoring and control.

With a graduate degree in computer science, students can pursue a variety of careers in fields such as: 

• Algorithms 
• Human-Centered Computing 
• Machine Learning 
• Data Science 
• Robotics 
• Security and Privacy 
• Systems

• Apple0
• Amazon
• Google
• Microsoft
• Salesforce
• Chevron
• Numerical Algorithms Group (NAG)
• Outrider
• BlackSky
• The Trade Desk
• Plus One Robotics
• Raytheon
• Emerson
• CACI
• RARE Petro

Mines Career Center

The Mines Career Center helps students chart their career paths and prepare for job searches, holds networking events and brings hundreds of employers to campus, among a host of other services.

Job Boards

• CrunchBoard
• GitHub
• IEEE Computer Society
• StackOverflow
• TechCareers
• Uncubed
• Y Combinator

Research and Trade Publications

• Artificial Intelligence
• Communications of the ACM
• ComputingEdge
• IEEE Transactions on Pattern Analysis and Machine Intelligence
• Journal of Computer Science
• Open Journal of the Computer Society

Professional Organizations

• American Society for Information Science and Technology
• Association for Computing Machinery
• Association for Women in Computing
• Association of Information Technology Professionals
• Computing Research Association
• Institute of Electrical and Electronics Engineers Computer Society
• International Association of Computer Science and Information Technology

Overall, computer and information technology jobs are expected to grow 12 percent between 2018 and 2028, much faster than the average for all occupations. The median annual wage in the field is also higher than the average of all other occupations—$88,240 compared to $39,810, as of May 2019.

In fact, of the 20 occupations that are projected to add the most jobs between 2018 and 2028, applications software developer ranked sixth, adding 241,500 jobs in that span—a growth rate of 26 percent. With high demand comes higher salaries; median pay in 2018 was $103,620.

Information security analyst jobs are projected to grow 32 percent over the same time period, adding 35,500 positions.
Below is the BLS outlook for computer and information technology jobs:

Computer and information systems managers, with an average salary of $142,530, are among the highest-paying occupations overall—16th in the country, according to the BLS, in a list dominated by medical professionals.

Colorado School of Mines awarded 24 master’s degrees in computer science in 2019. The average salary offer for that group was $92,625, with a high of $112,000 (view the Career Center Annual Report). Mines has consistently been highly ranked for return on investment.