ARC-TS was kind enough to let MDST pick up the unused cores on their computing cluster, which are usually used for computationally intensive research simulations and big data algorithms. You can read more about it at this link!
Placement & Prizes
1st: DaBrain – Guangsha Shi, Sean Ma, Sheng Yang
$200 Amazon Gift Card + MDST T-shirts
2nd: The Data Miners – Alexander Zaitzeff, Ryan Sandberg
$100 Amazon Gift Card + MDST T-shirts
3rd: Arya Farahi
Congratulations are in order to DaBrain, who in the last week of the competition rocketed through the leaderboard, displacing the incumbent finalists and finishing out the competition in first place! DaBrain employed the only neural network algorithm in the competition, leveraging the power of the LSTM architecture for Recurrent Neural Networks against this document-based dataset. LSTM-RNN’s have seen great experimental results on NLP tasks in recent years, making this choice in algorithm particularly exciting for the Rate My Professor Challenge.
Our second place finalists, The Data Miners, lead the leaderboard for many weeks after the beginning of the challenge. The Data Miners managed an impressive number of submissions, more than double that of the next most frequently submitting team. In the end, an ensembling method combining ridge regression, random forest regression, and gradient boosting- as well as some hacky tricks beyond explanation- allowed The Data Miners to seize second place!
Our third place finisher was Arya Farahi, a PhD student of Physics. His final approach was well reasoned, relying on a small family of simple, easily interpreted predictors. Employing Ridge, Lasso, and logistic regressions, as well as a measure of ‘happiness’ described by an academic paper (Dodds, et al 2015), Arya was able to build out a highly transparent, robust model with very little additional tinkering.
The MDST administration would like to thank everyone who participated. We deeply appreciate all the time and energy our members put into these competitions. We’ve learned a lot from our first internal competition and we hope you all did as well!
As always, send us your questions, comments, concerns, and suggestions to email@example.com.
The Michigan Data Science Team will be kicking off our second data science competition of the year next week! At this meeting, we will be introducing the competition dataset and giving a live demo of a script to help get you started. You do not need to have participated in the last challenge to compete! Unlike the Springleaf challenge, this will be an entirely internal competition, and will be especially geared towards those new to data science.
- MDST RateMyProfessors Challenge Kickoff Meeting
- Date: Nov. 5th (Thursday)
- Time: 5 – 6 PM
- Location: Beyster 1690
This challenge will feature ratings of professors, written by university students. Your task is to infer the numerical rating assigned to a professor by their student from their rating’s text. To be successful, your solutions will need to extract interesting, useful features from this text and apply them effectively. The best solutions will draw inspiration from research in natural language processing, sentiment analysis, and deep learning.
- Big prizes!
- Weekly tutorials and starter scripts
- Teammate matching
- Data visualization challenge
- Flux allocations for all competitors
- Leaderboard hosted on Kaggle In Class
The Springleaf Marketing Response Challenge is now over! Thank you and congratulations to all the dedicated students who competed. In total, we had over 50 students and 20 teams participate in this competition.
1st Place: $200 Amazon Gift Card + T-Shirts
#34 – Cantseetherandomforestforthetrees
Alexander Zaitzeff and Jared Webb
2nd Place: $100 Amazon Gift Card + T-Shirts
#545 – Physteam
Arya Farahi and Anthony Kremin
3rd Place: T-Shirts
#552 – GGBrown
Xiang Li, Xinyu Tan, Tianpei Xie, and Jianming Sang
We graciously acknowledge Soartech for funding for the MDST Springleaf Challenge.
MDST will be participating in its first ever online data science competition, the Springleaf Marketing Response Challenge, during the first few weeks of the Fall semester. In this challenge, competitors are tasked with preemptively identifying customers who will respond to a direct mail offer for a personal or auto loan. For privacy reasons, features are completely anonymized, encouraging competitors to employ creative data-driven feature selection methods.
Deadline: Monday, 19 October 2015
1st Place: $200 Amazon Gift Card + T-Shirts 2nd Place: $100 Amazon Gift Card + T-Shirts 3rd Place: T-Shirts
1st: $40,000 2nd: $30,000 3rd: $15,000 4th: $10,000 5th: $5,000
We graciously acknowledge Soartech for funding for the MDST Springleaf Challenge.
FLINT—A partnership between Google and the University of Michigan’s Flint and Ann Arbor campuses aims to provide a smartphone app and other digital tools to Flint residents and officials to help them manage the ongoing water crisis.
The app and other tools will help predict where lead levels will be highest in the city’s water, and they’ll pull together information and resources to make the crisis easier to navigate for those affected. The project is made possible by a $150,000 grant from Google.
“This investment by Google is an outstanding commitment to our community. It creates an ideal combination of an industry powerhouse with faculty expertise. It will create new opportunities for students and continue building community partnerships—all so that we can provide quick and critically important information and analysis for our community as we move forward,” said Chancellor Susan E. Borrego of the University of Michigan-Flint.
The Android app is slated for roll-out this summer. It could help residents determine whether their homes are at high risk of having lead-contaminated water. It could also help them locate day-to-day resources for lead testing, water distribution, water bottle recycling, water filters, and volunteer opportunities. A website will offer similar resources and will be accessible on any computer, including those in public libraries.
Additional web-based tools for researchers and government officials could provide detailed insight on how to deploy repairs and resources. For example, they could help identify and prioritize the water service line replacements.
A student team at UM-Flint has already developed a prototype smartphone app for Flint residents. Google and U-M Ann Arbor will work with them through the spring and summer to add mapping features that use predictive analytics from U-M Ann Arbor’s Michigan Data Science Team. The team will also develop an improved user interface with assistance from Google.
Google has pledged a variety of resources to the project including a grant and remote and on-site assistance from its user experience and app development team. The company will also donate data resources to the Michigan Data Science Team including mapping, satellite imagery, and geo-location data.
Initial work by the data science team has already shown some success at predicting which homes and neighborhoods have a high risk of lead contamination. In the coming months, they’ll continue to apply predictive algorithms and machine learning techniques to data from a wide variety of sources including Google, the State of Michigan and the City of Flint. The data includes existing lead testing data; detailed information on the type and location of water infrastructure; and information on the size, age, type, and condition of every parcel of property in the city.
“There’s a lot of data on the water crisis, but it’s scattered over many different agencies and places,” said Jacob Abernethy, an assistant professor of computer science and engineering at U-M Ann Arbor and faculty advisor to the Michigan Data Science Team. “By organizing it in one place and analyzing it, we can predict which areas are likely to be at risk. We can help planners determine which infrastructure repairs will benefit the most residents, and how to allocate resources like bottled water most efficiently.”
Google and U-M also plan to create a separate set of web tools for city planners and other officials. They will include extensive mapping and predictive analytics, with details on waterline type and location and other infrastructure data.
Mark Allison is an assistant professor of computer science at UM-Flint and the faculty leader of the Flint student team. He says the project will be an opportunity for students to make a difference in the water crisis and pick up valuable real-world development experience along the way.
“Finding the best way to put resources close to where high lead levels are is a big part of managing this crisis, and it’s the kind of problem that analytics can solve. We also want to give residents more transparency by making it easier for anyone to get access to the most up-to-date information,” Allison said. “I think this project will be transformative. And for all of us here in Flint, it’s about much more than grades.”
Allison said the team is working to keep the tools they develop flexible, enabling them to be used by other cities that face similar crises. His team is developing the tools as part of UM-Flint Computer Science’s community-based learning program, which puts students to work on real-world challenges in and around Flint.
The Michigan Data Science Team is a competitive extra-curricular team at U-M Ann Arbor. Founded by Abernethy, the team builds and applies advanced computer algorithms that can analyze and “learn” from large sets of data. By finding connections and patterns within that data, they can make predictions about future events. The techniques are already widely used in areas like online retailing and advertising.
“Access to clean drinking water is a concern all over the world, but in the United States it’s often a foregone conclusion. That is not the case recently for the residents of Flint, Michigan,” said Mike Miller, head of Google Michigan. “I am proud that we can contribute to help with the recovery of and we hope we can help to support a resolution to this crisis and get the residents of Flint the resources and respect they so rightly deserve.”
The Flint Water crisis began after April of 2014, when the city’s drinking water source was changed from Lake Huron via Detroit’s water system to the Flint River. The water supply was not properly monitored for corrosion control and it caused lead to leach from service lines into the city’s drinking water. While the city has since switched its water supply back to the Detroit system, residents are still being advised not to drink unfiltered tap water.
Written by Gabe Cherry
Five research projects — three in health and two in social science — have been awarded funding in the second round of the Michigan Institute for Data Science Challenge Initiative program.
The projects will receive funding from MIDAS as part of the Data Science Initiative announced in fall 2015.
The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science. For more information, visit midas.umich.edu/challenges.
The projects, determined by a competitive submission process, are:
- Title: Michigan Center for Single-Cell Genomic Data Analysis
Description: The center will establish methodologies to analyze sparse data collected from single-cell genome sequencing technologies. The center will bring together experts in mathematics, statistics and computer science with biomedical researchers.
Lead researchers: Jun Li, Department of Human Genetics; Anna Gilbert, Mathematics
Research team: Laura Balzano, Electrical Engineering and Computer Science; Justin Colacino, Environmental Health Sciences; Johann Gagnon-Bartsch, Statistics; Yuanfang Guan, Computational Medicine and Bioinformatics; Sue Hammoud, Human Genetics; Gil Omenn, Computational Medicine and Bioinformatics; Clay Scott, Electrical Engineering and Computer Science; Roman Vershynin, Mathematics; Max Wicha, Oncology.
- Title: From Big Data to Vital Insights: Michigan Center for Health Analytics and Medical Prediction (M-CHAMP)
Description: The center will house a multidisciplinary team that will confront a core methodological problem that currently limits health research — exploiting temporal patterns in longitudinal data for novel discovery and prediction.
Lead researchers: Brahmajee Nallamothu, Internal Medicine; Ji Zhu, Statistics; Jenna Wiens, Electrical Engineering and Computer Science; Marcelline Harris, Nursing.
Research team: T. Jack Iwashyna, Internal Medicine; Jeffrey McCullough, Health Management and Policy (SPH); Kayvan Najarian, Computational Medicine and Bioinformatics; Hallie Prescott, Internal Medicine; Andrew Ryan, Health Management and Policy (SPH); Michael Sjoding, Internal Medicine; Karandeep Singh, Learning Health Sciences (Medical School); Kerby Shedden, Statistics; Jeremy Sussman, Internal Medicine; Vinod Vydiswaran, Learning Health Sciences (Medical School); Akbar Waljee, Internal Medicine.
- Title: Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology
Description: Using an app platform that integrates signals from both mobile phones and wearable sensors, the project will collect data from over 1,000 medical interns to identify the dynamic relationships between mood, sleep and circadian rhythms. These relationships will be utilized to inform the type and timing of personalized data feedback for a mobile micro-randomized intervention trial for depression under stress.
- Lead researchers: Srijan Sen, Psychiatry; Margit Burmeister, Molecular and Behavioral Neuroscience.
Research team: Lawrence An, Internal Medicine; Amy Cochran, Mathematics; Elena Frank, Molecular and Behavioral Neuroscience; Daniel Forger, Mathematics; Thomas Insel (Verily Life Sciences); Susan Murphy, Statistics; Maureen Walton, Psychiatry; Zhou Zhao, Molecular and Behavioral Neuroscience.
- Title: Computational Approaches for the Construction of Novel Macroeconomic Data
Description: This project will develop an economic dataset construction system that takes as input economic expertise as well as social media data; will deploy a data construction service that hosts this construction tool; and will use this tool and service to build an “economic datapedia,” a compendium of user-curated economic datasets that are collectively published online.
Lead researcher: Matthew Shapiro, Department of Economics
Research team: Michael Cafarella, Computer Science and Engineering; Jia Deng, Electrical Engineering and Computer Science; Margaret Levenstein, Inter-university Consortium for Political and Social Research.
- Title: A Social Science Collaboration for Research on Communication and Learning based upon Big Data
Description: This project is a multidisciplinary collaboration meant to introduce social scientists, computer scientists and statisticians to the methods and theories of engaging observational data and the results of structured data collections in two pilot projects in the area of political communication and one investigating parenting issues. The projects involve the integration of geospatial, social media and longitudinal data.
Lead researchers: Michael Traugott, Center for Political Studies, ISR; Trivellore Raghunathan, Biostatistics
Research team: Leticia Bode, Communications, Georgetown University; Ceren Budak, U-M School of Information; Pamela Davis-Keane, U-M Psychology, ISR; Jonathan Ladd, Public Policy, Georgetown; Zeina Mneimneh, U-M Survey Research Center; Josh Pasek, U-M Communications; Rebecca Ryan, Public Policy, Georgetown; Lisa Singh, Public Policy, Georgetown; Stuart Soroka, U-M Communications.
The Michigan Institute for Data Science (MIDAS) will hold a faculty meeting at noon on Thursday, January 19 (Suite 7625, School of Public Health I, 1415 Washington Heights) for the NSF 17-534 “Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA)” solicitation.
The meeting will include an overview of the NSF solicitation, U-M Data Science Resources (MIDAS, CSCAR, ARC-TS) available to faculty responding to the NSF call, and an opportunity to network with other faculty.
MIDAS has also arranged for Sylvia Spengler, NSF CISE Program Director, to be available at 1:30 pm to answer questions regarding the BIGDATA solicitation.
We invite you to participate in the faculty meeting to share your ideas and interest in responding to this BIGDATA solicitation as well as interact with other faculty looking to respond to this funding mechanism.
For those unable to participate in person, you can join virtually using GoToMeeting:
- You can also dial in using your phone.
United States: +1 (872) 240-3212
Access Code: 795-426-685
A box lunch will be provided at the faculty meeting. Your RSVP (https://goo.gl/forms/OYAuB8mWCOlx3fw73) is appreciated.
Sharon Broude Geva, the Director of Advanced Research Computing at the University of Michigan, has been elected vice-chair of the Coalition for Academic Scientific Computation (CASC).
Founded in 1989, CASC advocates for the use of advanced computing technology to accelerate scientific discovery for national competitiveness, global security, and economic success. The organization’s members represent 83 institutions of higher education and national labs.
The vice-chair position is one of four elected CASC executive officers. The officers work closely as a team with the director of CASC. The vice-chair also leads CASC meeting program committees, is responsible for recruitment of new members, substitutes for the chair in his or her absences, and assists with moderating CASC meetings.
Geva served as CASC secretary in 2015 and 2016. Her term as vice-chair is effective for the 2017 calendar year.
The other executive officers for 2017 are are Rajendra Bose, Chair, Columbia University; Neil Bright, Secretary, Georgia Institute of Technology; and Andrew Sherman, Treasurer, Yale University. Curt Hillegas of Princeton University is immediate past chair.