Welcome to the MInDS@Mines lab led by Dr. Hua Wang at the Colorado School of Mines. The MInDS lab focuses on research of Machine learning, Informatics and Data Science, with their applications in, but not limited to, medical image computing, health informatics, bioinformatics, additive manufacturing, material informatics, cheminformatics, and computer vision. The central theme of our lab is developing robust, adaptive and efficient machine learning, data mining and optimization algorithms with provable guarantee to understand large-scale, dynamic, complex, and heterogeneous data. The goal of our research is twofold:

  1. advance computer science and machine learning by producing novel algorithms for analyzing large scale heterogeneous data sets;
  2. provide important new insights into real-world data via new informatics and data science technologies.

We are looking for highly motivated students with backgrounds in computer science, statistics, or engineering to join our lab! Positions for undergraduate and graduate students are available. If you are interested, please email Dr. Wang for more information.

MInDS @ MINES

News

MInDS@Mines Undergraduate Researchers Present at the 2019 Undergraduate Research Symposium
Sun Apr 28, 2019

Zoe Baker and Madeline McKune, two undergraduate student researchers on the MInDS team, presented their work on effective visualization of machine learning results at the annual Undergraduate Research Symposium at CoorsTek.

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Announcing Machine Learning in Practice - a short course
Wed Apr 3, 2019

The MInDS@Mines team is excited to announce that we will be offering a hands-on short course focused on applications in machine learning: Machine Learning in Practice. The course will be held on June 10th-14th at the Catalyst Tech Center located in the RiNo neighborhood in Denver. We designed Machine Learning in Practice for folks in industry and research who are looking to apply machine learning to their work.

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Projects

This project is funded by DOD-OEA, Phase I: 2017/06 – 2018/12 (completed), Phase II: 2019/02 - Present.

Additive manufacturing (AM) and data-driven design are becoming increasingly common in a wide range of industries. Unlike plastics 3D printing, which can be done by hobbyists in their homes, AM of metals requires a high-level of skill, expertise, and specialized know-how to adjust and tune the manufacturing process for a specific part geometry, material, or desired functional outcome. As such, building a new part or changing to a new material with AM processing is currently time consuming, tedious, and expensive (on the order of 5 years and $5M for a single “A-Basis Allowable” part).
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This project is funded by NSF Grant 1849359, 2019/04 - present.

Imagine that in the near future a patient needing surgery will swallow a small mobile robot that can autonomously perform the procedure without any external incisions or pain. Such robots have the potential to make state-of-the-art surgical concepts a reality by providing an unconstrained mobile platform to visualize, manipulate and surgically treat tissue. The project’s strategy will also harness the excitement surrounding robotics and computer science, and leverage it with the Investigators’ exceptional infrastructure for education innovation and outreach to provide new, inspirational educational experiences for students.
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This project is funded by Danone, 2018/12 - 2019/05 (completed).

Although communities of microorganisms, originally referred to as microbiota, have been studied for a long time, the field has taken off in 2002 with the advent of metagenomics, which for the first time equipped us with a way to “see” the incredible diversity of species around us — too small to see with or own eyes. Now, the microbiome is not only of interest in environmental samples, but also within our own bodies.
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