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 Students Present at the 2021 Virtual Undergraduate Research Symposium
Thu May 20, 2021

This year four students (Lauren Zoe Baker, Kane Bruce, Lucia Saldana Barco, Braedon O’Callaghan) from the MInDS@Mines lab presented their research project during the 2021 Virtual Undergraduate Research Symposium. The presentations were open to the public for comment from April 19th-23rd, 2021 and have been archived online.

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Kane Bruce awarded Research Honor Distinction at Graduation
Wed May 19, 2021

Kane Bruce, an undergraduate student researcher in the MInDS@Mines team, has been awarded as Undergraduate Research Scholar Distinction in Spring 2021 for his research on Lofting: Estimating Mineral Distribution with Convolutional GAN Models. Kane has devoted himself to this research over the past year and has made significant research contributions to our team.

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High-School Student Wins Colorado Affiliate NCWIT Award for Aspirations in Computing for Summer Research
Sat Feb 27, 2021

Carla Ellefsen, a senior at Lakewood High School, won an Aspirations in Computing Award from the NCWIT Colorado Affiliate for her research in multiple-instance learning applied to natural-scene and biomedical imaging data during Summer 2020.

The award was presented at the annual NCWIT Colorado Aspirations in Computing Award Ceremony held virtually at the Colorado School of Mines on February 27, 2021. Congratulations, Carla!

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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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