Research

The Department of Computer Science has a strong emphasis on research, both within the department and collaboratively with other departments, universities, government organizations and industry partners.

The departmental research areas include the following:

Algorithmic Robotics

Tom Williams with robotAn 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.

 

Active Groups and Centers

Affiliated Faculty

Sponsors

Recent collaborators

Applied Algorithms
Algorithms word cloudOur 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.

 

Active Groups and Centers

Affiliated Faculty

Sponsors

Augmented Reality
The augmented reality research group is directed by Dr. William Hoff. 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.

 

Affiliated Faculty

Sponsors

CS for All: Computer Science Education
Students teaching girls code.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.

 

Active Groups and Centers

Affiliated Faculty

Recent collaborators

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.

 

Some Examples of Research Projects in Dr. Yue’s CSP Group:

 

Affiliated Faculty

Sponsors

  • National Science Foundation
  • National Security Agency
  • Army Research Office
  • Amazon

Recent collaborators

High Performance Computing
ServersMines HPC (High Performance Computing) research group is directed by Dr. Bo Wu. The focus for this group lies in the broad field of compilers and programming systems, with an emphasis on program optimizations for heterogeneous computing and big data analytics.

  1. Emerging applications are not optimized enough to tap into the full potential of modern massively parallel machines.
    He aims at building a compilation and runtime system to efficiently support both regular and irregular applications on those machines by mitigating the memory and power bottlenecks.
  2. Due to the rapid increase of data volume, producing exact solutions for big data applications can be too time- and energy- consuming. Dr. Wu has been trying to build an optimization framework to systematically support approximate computing for big graph analytics. The goal is to produce a good enough analysis results with great resource and energy savings.

Affiliated Faculty

Sponsors

Recent collaborators

  • College of William and Mary
  • North Carolina State University
  • Rochester University
  • University of California
  • Pacific Northwest National Lab
Machine Learning
Hua Wang with computerMines 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, and so on. 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.

Affiliated Faculty

Sponsors

Recent collaborators

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.

Active Groups and Centers

Affiliated Faculty

Sponsors

  • LGS Innovations
  • National Renewable Energy Laboratory
  • National Science Foundation
  • Petroleum Institute, UAE

Recent collaborators

  • Petroleum Institute, UAE
  • University of Osnabruck
  • University of Toulouse
  • University of Amsterdam
  • Colorado State University
  • LGS Innovations
  • Chinese Academy of Sciences, China