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 Members Demonstrates Their Researches at 2023 C-MAPP Poster Event
Thu Jan 19, 2023

The 2023 annual Computing-Mines Affiliates Partnership Program (C-MAPP) event was held in Student Center at Colorado School of Mines. Four MInDS@Mines students presented their researches to the industry partners of Mines and communicated with them in the event.

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The poster presentations of MInDS@Mines Students at 2022 Annual C-MAPP Event
Thu Feb 10, 2022

Nine MInDS@Mines students presented their researches to industry leaders at the annual Computing-Mines Affiliates Partnership Program (C-MAPP) event held in Friedhoff Hall of Colorado School of Mines.

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Lauren Zoe Baker named a 2022 CRA Outstanding Undergraduate Researcher Award Finalist
Mon Jan 10, 2022

We’re very excited to announce that Lauren Zoe Baker, an undergraduate researcher with the MInDS@Mines team, has been named an Outstanding Undergraduate Researcher Award Finalist by the Computing Research Association (CRA). Zoe has been working with our team since Fall of 2018, and has contributed to several different machine learning projects in her time here. She has been a co-author in six different publications as a part of the team. Read more.


This project is funded by NSF Grant 2029543, 2020/05 - present.

How likely am I to have COVID-19 complications? Machine learning could help predict the answer. Covid-19 App Developed by Colorado School of Mines. Mines professors building app to predict likelihood of catching COVID-19. As of mid-April 2020, two million people are infected worldwide with the novel coronavirus. Now, the USA is at the epicenter of this pandemic, where it has already killed 20,000 people. Approaches to slow the progression are urgently needed.
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This project is funded by DOE - Office of Fossil Energy, 2020/05 - present.

Today’s technology can securely store captured carbon dioxide deep in the subsurface of the ground, but slow data processing can result in operational inefficiencies. To meet this challenge, the Office of Fossil Energy (FE) of DOE developed a Science-Informed Machine Learning to Accelerate Real Time Decisions (SMART-CS) initiative. Using science-based machine learning and AI, this initiative will enable better reservoir management through more rapid decision making. It will develop real-time visualization, forecasting capabilities, and virtual learning environments.
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This project is funded by NSF Grant 1932482, 2019/12 - present.

This project aims to radically transform traffic management, emergency response, and urban planning practices via predictive analytics on rich data streams from increasingly prevalent instrumented and connected vehicles, infrastructure, and people. Road safety and congestion are a formidable challenge for communities. Current incident management practices are largely reactive in response to road user reports. With the outcome of this project, cities could proactively deploy assets and manage traffic. This would reduce emergency response times, saving lives, and minimizing disruptions to traffic.
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