Visualize Complex data with the MIT CAVE Lab
Complex data can be difficult to visualize and analyze. Working in cross-functional teams also adds a layer of complexity to making decisions from big data analysis. The CAVE Lab uses interactive visualization to improve data visibility, data analysis, and decision making for supply chain and logistics challenges. View some of the lab’s software previews below.
Our Expertise & Research
The CAVE lab provides students, researchers, and decision-makers with a more intuitive understanding of and access to quantitative methods to support strategic design, tactical planning, and operational decision problems in the supply chain and logistics domain and related fields.
Based on a newly created physical lab space at MIT CTL equipped with state-of-the-art visualization technology, the lab is developing interactive visual interfaces to data and analytical tools, addressing complex supply chain and logistics problems.
Innovative Education
The lab provides a unique environment for students from all programs of MIT at all levels of seniority to understand and experiment with quantitative methods of supply chain, transportation, and logistics decision methods. From studying the principles of data science, optimization or simulation through interactive demo applications, to developing course projects or thesis projects that combine our domain with interactive visual interfaces, it provides students an intuitive, open source environment to understand the theory, apply it to real data, and implement usable demos and applications.
Smarter Decision Making
Together with MIT CTL’s corporate partners, the lab can be leveraged as an interactive decision-making space. By co-developing interactive visual analytics applications addressing specific, data-driven decision problems of our partners and presenting them in a way that optimally leverages human perception and cognition, we aim to promote the effective future use of data and analytics by corporate decision-makers at all levels.
Corporate engagement with the lab can be based on dedicated research and development projects and may entail interactive decision making and data analytics workshops with employees, customers, suppliers, or peers of the sponsoring partner at the lab.
Our Team
Dr. Matthias Winkenbach
Director
Tim Russell
Research Engineer
Connor Makowski
Research Associate
Luis Vazquez
Software Developer
Willem Guter
Research Assistant & Developer
Erin Y. Liu
Research Assistant & Developer
Jean G. Billa
Research Assistant & Developer
Research Alum
Yang Dai
Research Assistant & Developer
Samip Jain
Research Assistant & Developer
Margaret Sands
Research Assistant & Developer
Robert Tran
Research Assistant & Developer
Alan Yan
Research Assistant & Developer
Lab Demos
Last Mile Logistics at MIT Center for Transportation & Logistics
Last-mile logistics data can be difficult to visualize and analyze. The CAVE Lab https://cave.mit.edu uses interactive visualization to improve data visibility, data analysis, and decision making for last-mile logistics challenges.
Network Design in the MIT CTL Computational and Visual Education (CAVE) Lab
Together with MIT CTL’s corporate partners, the CAVE serves as an interactive decision-making space. By co-developing interactive visual analytics applications addressing specific, data-driven decision problems of our partners and presenting them in a way that optimally leverages human perception and cognition, we promote the effective future use of data and analytics by corporate and organizational decision-makers at all levels.
Supply Chain Simulation in the MIT CTL Computational and Visual Education (CAVE) Lab
Complex data can be difficult to visualize and analyze. Working in cross-functional teams also adds a layer of complexity to making decisions from big data analysis. The CAVE Lab uses interactive visualization to improve data visibility, data analysis, and decision making for supply chain and logistics challenges.
The MIT Computational and Visual Education (CAVE) Lab
The MIT CAVE Lab uses interactive visualization to improve data visibility, data analysis, and decision making for supply chain and logistics challenges.