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

Lauren Zoe Baker awarded prestigious Goldwater Scholarship
Mon Apr 6, 2020

We’re very excited to announce that Lauren Zoe Baker, an undergraduate researcher with the MInDS@Mines team, has been awarded the prestigious Goldwater scholarship! Zoe has been working with our team for over a year and has shown tremendous promise. Receiving the Goldwater scholarship is further proof of her abilities and achievements.

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Eleven MInDS@Mines Students Present Research Posters at Annual C-MAPP Event
Fri Jan 17, 2020

Eleven MInDS@Mines students presented their research to industry leaders at the annual Computing-Mines Affiliates Partnership Program (C-MAPP) event in Golden, Colorado.

This year’s event was held in the newly renovated Friedhoff Hall in Green Center.

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New Method to Enrich Multi-Modal Longitudinal Data Accepted to AAAI 2020
Thu Jan 16, 2020

Lyujian Lu has recently had his paper, “Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments”, accepted into the proceedings of the 34th annual AAAI conference, AAAI 2020.

We’d also like to highlight Lauren Zoe Baker who is an undergraduate student who worked on this research. This is her second publication.

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Projects

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, FE 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. As a result, the SMART-CS initiative will help stakeholders and regulators overcome costly inefficiencies while increasing their confidence that the geologic carbon storage is secure.
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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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