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MDST Poster Wins Symposium Competition

By | MDSTPosts

Today, MDST participated in the student poster competition at the “Meeting the Challenges of Safe Transportation in an Aging Society Symposium”. The poster highlights the key findings from the Fatal Accident Reporting System (FARS) competition we held earlier this year. The Michigan Institute for Data Science (MIDAS) provided MDST members access to a dataset of fatal crashes in the US, with a labeled variable indicating whether alcohol was involved in the incident, and models were judged based on how well they could predict the value of this true/false variable.

The poster describes the winning model for the competition, an ensemble of a neural network and boosted decision tree, and identifies crash time, location, and the number of passengers involved, as the most predictive variables.

We want to thank MIDAS for funding the competition, Chengyu Dai and Guangsha Shi for representing MDST at the ATLAS Symposium, and the many members of MDST who participated in the FARS Challenge.

You can download the poster from the link below.

  • “Inferring Alcohol Involvement in Fatal Car Accidents with Ensembled Classifiers”,Guangsha Shi, Arya Farahi, Chengyu Dai, Cyrus Anderson, Jiachen Huang, Wenbo Shen, Kristjan Greenwald, and Johnathan Stroud.

Bloomberg Conference Accepts Both MDST Papers!

By | MDSTPosts

Earlier this summer, MDST submitted two papers to the Bloomberg Data For Good Exchange conference regarding our work on the Flint Water Crisis and with the University Musical Society respectively. It is my great pleasure to announce that the conference has elected both of our papers for presentation at the conference in New York on September 25th!

Needless to say, we’re all very excited. ?

MDST Faculty Advisor Jacob Abernethy Interviewed for Machine Learning Podcast!

By | MDSTPosts

Our very own Jacob Abernethy was recently interviewed on the popular machine learning podcast, Talking Machines. Among other things, Jake was asked about his experiences working with the trove of municipal data available in Flint, his path to research at the University of Michigan, and our work with Google and UM-Flint.

Fun Fact: Talking Machines is produced by Kathrine Goreman, a UM alumna!

MDST Submits Two Papers to Bloomberg Conference

By | MDSTPosts

While we are known for our participation in structured prediction challenges, MDST has picked up at least two community projects in the last year. MDST members of all experience levels got to participate in both our efforts in Flint and our work with UMS’s ticket purchase data. Around the time that we hit milestones in both projects, news of the Bloomberg Data 4 Good Exchange call for papers reached some members of MDST and we decided to take a shot.

The results of our foray into volunteer, remote, academic paper collaboration can be found below in the form of two successfully written MDST papers! We’re incredibly proud of the results and even prouder of our membership, who worked so hard to produce such quality work.

MDST Partners with UM-Flint & to Aid Locals in Flint Water Crisis

By | MDSTPosts

The Michigan Data Science Team is excited to have partnered with Google and the University of Michigan-Flint to engineer a data platform and accompanying app as a part of our continued efforts to help the community of Flint. This app will provide users with information regarding key public services, such as the locations of water bottle distribution centers and instructions to request new water testing kits. Users will also be able to report concerns about the water quality at their location, and access our predictive model, which flags homes that are potentially at high risk of lead contamination. is providing the University of Michigan-Flint a grant of $150,000 to build the platform and accompanying app. In addition, they are also providing access to several Google engineering consultants who will aid in producing interactive visualizations and oversee the app’s user interface design. MDST has created a multidisciplinary engineering team to oversee and manage the creation of our predictive model and data platform.

We will continue our efforts to ask and answer the data-related questions surrounding this crisis in order to provide as much value as we can to the people of Flint. We are incredibly grateful for the support from Google and for the chance to collaborate with our friends and fellow researchers at the University of Michigan-Flint campus.

FARS Visualization Challenge

By | MDSTPosts

Last week, we held the FARS Dataset Visualization Challenge, where teams were tasked with visualizing more than a decade of fatal traffic accident records to address the question – “What causes drunk driving accidents?”

First prize went to Team Bidiu (Chengyu Dai, Cyrus Anderson, Cupjin Huang, and Wenbo Shen) whose presentation addressed the questions: who is driving drunk, where are they driving, and when do fatal accidents occur? For their first-place finish, each member of Team Bidiu will receive a $25 gift card to! You can view Team Bidiu’s presentation and source code at the team’s Github page.

FARS Dataset Challenge Kickoff

By | MDSTPosts

It’s my great pleasure to be announcing the next MDST competition! We are very fortunate to be partnering with the Michigan Institute for Data Science (or ‘MIDAS’) for this event. We will be holding the kickoff meeting this Thursday at 5:00pm in 3150 DOW. Through this partnership, we’ve been able to obtain a particularly interesting dataset. We have compiled records of every fatal car accident reported in the United States between 2003 and 2014, a dataset known as the Fatal Accident Reporting System Dataset, or FARS. The challenge will be to predict whether or not a drunk driver was involved in the accident.

More information about logistics, prizes, and the dataset itself will be given at the kickoff ceremony this Thursday. Additionally, we will be awarding prizes to the winners of our last competition, the RateMyProfessor challenge, so if you won a prize, please show up to claim it.

If you have any further questions, feel free to email me at