I was truly amazed at how middle- and high-school students were learning this week. More than ever, I believe that every high-school student should be exposed to computational and data thinking in their freshman year. They may, or may not, like the material but they should have the opportunity to learn it. What we covered in one week was simply amazing: from no exposure to programming to decision trees. These students rocked and they came from all backgrounds and from many towns in Michigan.
The Seth Bonder Summer Camp in Computational and Data Science for middle- and high-school students is running from July 24-28, 2017 at the University of Michigan.
Terrence Mak was a runner-up for the INFORMS ICS Student Prize for his paper on “Efficient Dynamic Compressor Optimization in Natural Gas Transmission Systems“. Congratulations, Terrence!
Seth Blumsack (Penn State), Russell Bent (LANL), and I will be working on joint electricity and gas markets. This research is motivated by increasing interdependencies between the U.S. electric power and natural gas infrastructures, which may have some significant unintended consequences as in the case of the New England Polar vortex in 2014.
Discrete optimization is now available on the new Coursera platform: . First session in October.
The MIDAS projects on transportation were featured in the Urban Transportation Monitor.
See the following announcement for a senior faculty position at the university of Michigan. Areas of interest include but are not limited to: Climate Science, Energy, Humanitarian Logistics, Life Science, Manufacturing, Risk Analysis, Sharing Economy, Service Systems, Supply Chain Management, Sustainability, and Transportation Systems.
Follow this link for a programmer position on our data science project on mobility and transportation in Ann Arbor.
Here is an Xeconomy article on some of the transportation projects happening at the University of Michigan.
Our paper on Graphical Models for Optimal Power Flow won the best paper award at CP’2016. It unifies power flow, graphical models, and constraint programming for radial networks.