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.



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.

Read more.

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.

Read more.

Journal Extension of 2018 MICCAI Conference Publication Accepted to IEEE Transactions on Medical Imaging
Thu Dec 12, 2019

Lodewijk Brand’s new journal publication, titled “Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction”, has recently been accepted into the IEEE Transactions on Medical Imaging. This work is an extension of the conference version presented at the Medical Image Computing and Computer Assisted Intervention (MICCAI 2018).

Read more.


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.
Learn more.

This project is funded by Universidad Nacional de San Agustin de Arequipa - UNSA, 2019/05 - present.

Artisanal and small-scale mining (ASM) has experienced explosive growth in recent years due to the rising value of mineral prices and the increasing difficulty of earning a living from agriculture and other rural activities. An estimated 40.5 million people were directly engaged in ASM in 2017, up from 30 million in 2014, 13 million in 1999 and 6 million in 1993. That compares with only 7 million people working in industrial mining in 2013.
Learn more.

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.
Learn more.