(Click on Titles to expand.  Specific times subject to change)



Deep Learning Workshops by Amazon and Google

If interested, please apply (through this form ) to attend Google or Amazon deep learning workshops on November 13.  The workshops are free; however, there will be an application process as the expected number of attendees will be high.  Please note: if there are more applicants than the number of spots allows, students who have submitted abstracts to the MIDAS annual symposium and who signed up to volunteer at the symposium will be given priority.  Application deadline: Oct. 21.

Google Deep Learning Workshop
8:30 a.m. – 1:30 p.m.
Weiser Hall 10th floor

This workshop will offer practical instruction in deep learning (DL) through demos and hands-on labs. You will learn about machine learning (ML) and how to build a data strategy around it, along with feature engineering techniques. You will create ML/DL models in the cloud using Python notebooks. TensorFlow will be introduced, and you will learn how to write low-level TensorFlow programs. The workshop will also include a discussion of Google’s use of ML in practice.

Amazon Deep Learning Workshop (Only for University of Michigan faculty, staff and students), two identical sessions.
Session 1: 8:00 a.m. – noon.
Session 2: 1 pm to 5 pm.
Rackham Building 4th floor East Conference Room

This workshop will offer practical instruction in deep learning (DL) through demos and hands-on labs. You will explore the current trends powering artificial intelligence (AI)/DL adoption and algorithmic learning in neural networks, dive into how DL is applied in modern business practices, and leverage building blocks from the Amazon machine learning (ML) family of AI services from powerful new GPU instances, convenient Amazon SageMaker built-in algorithms, and to ready-to-use managed AI services. The workshop will include a discussion of Amazon’s use of ML in practice.


8:45 am Keynote 1: Rayid Ghani, University of Chicago

Title: Machine Learning for Social Good: Examples, Opportunities, and Challenges
Abstract: Can AI, ML and Data Science help help prevent children from getting lead poisoning? Can it reduce infant and maternal mortality? Can it reduce police violence and misconduct? Can it help cities better target limited resources to improve lives of citizens and achieve equity? We’re all aware of the potential of ML and AI but turning this potential into tangible social impact takes cross-disciplinary training, new methods, and scalable data and computational infrastructure. I’ll discuss lessons learned from working on 50+ projects over the past few years with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure. I’ll highlight opportunities as well as challenges around explainability and bias/fairness that need to tackled in order to have social and policy impact in a fair and equitable manner.

10:00 am Panel Discussion: Big Data and Political Science

Moderator: Dr. Rayid Ghani

Panelists: U-M Professors Michael Traugott, Ceren Budak, Joshua Pasek, Stuart Soroka.  They will discuss data-intensive research on the social media and the public’s political views and voting behavior in the context of the 2016 election and the upcoming election.

11:00 am Research Talks, Session 1

Featuring U-M data science research across methodology and application domains.  Abstract submission due Sept. 20.

12:15 pm Poster Session 1 and lunch

Featuring a large number of research posters from U-M data scientists.  New this year: poster presentation from students and postdocs from 30+ leading universities, including Columbia, Duke, Harvard, MIT, Morehouse College, Purdue, Rice, Stanford, University of California (Berkeley), University of Washington, Wayne State and more.

Lunch provided.

2:15 pm Industry discussion panel: Data Science for the Next Ten Years in the Industry

Dana Budzyn, Co-founder and CEO, UBDI

Richard Lindberg: Quicken Loans

Tony Qin, AI Lead, DiDi Chuxing

Kyle Schmitt: Managing Director, Global Insurance Practice, J. D. Power

Moderator: Ella Atkins, Professor of Aerospace Engineering, University of Michigan

3:30 pm Keynote 2: Tina Eliassi-Rad, Northeastern University

Title: Just Machine Learning

Abstract: Tom Mitchell in his 1997 Machine Learning textbook defined the well-posed learning problem as follows: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” In this talk, I will discuss current tasks, experiences, and performance measures as they pertain to fairness in machine learning. The most popular task thus far has been risk assessment. For example, Jack’s risk of defaulting on a loan is 8, Jill’s is 2; Ed’s risk of recidivism is 9, Peter’s is 1. We know this task definition comes with impossibility results (e.g., see Kleinberg et al. 2016, Chouldechova 2016). I will highlight new findings in terms of these impossibility results. In addition, most human decision-makers seem to use risk estimates for efficiency purposes and not to make fairer decisions. The task of risk assessment seems to enable efficiency instead of fairness. I will present an alternative task definition whose goal is to provide more context to the human decision-maker. The problems surrounding experience have received the most attention. Joy Buolamwini (MIT Media Lab) refers to these as the “under-sampled majority” problem. The majority of the population is non-white, non-male; however, white males are overrepresented in the training data. Not being properly represented in the training data comes at a cost to the under-sampled majority when machine learning algorithms are used to aid human decision-makers. There are many well-documented incidents here; for example, facial recognition systems have poor performance on dark-skinned people. In terms of performance measures, there are a variety of definitions here from group- to individual-fairness, from anti-classification, to classification parity, to calibration. I will discuss our null model for fairness and demonstrate how to use deviations from this null model to measure favoritism and prejudice in the data.
Speaker Bio: Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 80 peer-reviewed publications (including a few best paper and best paper runner-up awardees); and has given over 190 invited talks and 13 tutorials. Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, she served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy. For more details, visit

4:30 pm Research Talks, Session 2

Featuring U-M data science research across methodology and application domains.  Abstract submission due Sept. 20.


8:30 am Keynote 3: Tanya Berger-Wolf, University of Illinois at Chicago

TitleComputational Ecology and AI for Conservation
Computation has fundamentally changed the way we study nature. New data collection technology, such as GPS, high definition cameras, UAVs, genotyping, and crowdsourcing, are generating data about wild populations that are orders of magnitude richer than any previously collected. Unfortunately, in this domain as in many others, our ability to analyze data lags substantially behind our ability to collect it. In this talk I will show how computational approaches can be part of every stage of the scientific process of understanding animal sociality, from intelligent data collection (crowdsourcing photographs and identifying individual animals from photographs by stripes and spots – to hypothesis formulation (by designing a novel computational framework for analysis of dynamic social networks), and provide scientific insight into collective behavior of zebras, baboons, and other social animals.
Dr. Tanya Berger-Wolf is currently a Professor of Computer Science at the University of Illinois at Chicago, where she heads the Computational Population Biology Lab. As a computational ecologist, her research is at the unique intersection of computer science, wildlife biology, and social sciences. She creates computational solutions to address questions such as how environmental factors affect the behavior of social animals (humans included). Berger-Wolf is also a director and co-founder of the conservation software non-profit Wild Me, home of the Wildbook project, which enabled the first ever full census of the entire species, the endangered Grevy’s zebra in Kenya, using photographs from ordinary citizens.
Berger-Wolf holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. She has received numerous awards for her research and mentoring, including University of Illinois Scholar, UIC Distinguished Researcher of the Year, US National Science Foundation CAREER, Association for Women in Science Chicago Innovator, and the UIC Mentor of the Year.

9:45 am Research Talks, Session 3

Featuring U-M data science research across methodology and application domains. Abstract submission due Sept. 20.

11:00 am Poster Session 2. Student Poster Award and Data Challenge Award Ceremony

Featuring a large number of research posters from U-M data scientists.  New this year: poster presentation from students and postdocs from 30+ leading universities, including Columbia, Duke, Harvard, MIT, Morehouse College, Purdue, Rice, Stanford, University of California (Berkeley), University of Washington, Wayne State and more.

Poster award winners (with cash awards) in multiple categories will be announced.

The MIDAS Data Challenge will run from mid-October to Nov. 15. Student teams will examine data provided by industry sponsors and come up with solutions to research questions provided by the sponsors or defined by the student teams. Interested students should contact MDST to ask how to participate. A brief presentation and award ceremony will be held during the symposium.

1:30 pm Data Science for Music mini-symposium

Featuring four projects funded by MIDAS.

  • “Understanding and Mining Patterns of Audience Engagement and Creative Collaboration in Largescale Crowdsourced Music Performances”
    Danai KoutraWalter Lasecki, Computer Science and Engineering
  • “Understanding How the Brain Processes Music through the Bach Trio Sonatas”
    Daniel Forger, Mathematics; James Kibbie, Organ,
  • “The Sound of Text”
    Rada Mihalcea, Electrical Engineering and Computer Science; Anıl Çamcı, Performing Arts Technology;
  • “A Computational Study of Patterned Melodic Structures across Musical Cultures”
    Somangshu Mukherji, Music Theory

Panel Discussion: Data Science and the Future of Arts Research

Moderator: Marvin Parnes, former Executive Director of Arts Alliance for Research Universities

Panelists: Daniel Forger (Professor of Mathematics, University of Michigan); Allie Lahnala (graduate student, Computer Science and Engineering); Sam Mukherji (Assistant Professor, Music Theory); Gregory Wakefield (Director of ArtsEngine, Professor of Electrical Engineering and Computer Science).

Program Highlight: Data Science Student Consortium  We are the first in the country to organize a student consortium for data science.  38 data science students and postdocs from 29 leading universities around the country will attend our symposium, including Columbia, Duke, Harvard, MIT, Clark Atlanta University, Purdue, Rice, Stanford, University of California (Berkeley), University of Washington, Wayne State.  Watch for the special section at our poster session.  Please be sure to visit their posters at the poster session. 

Featured Speakers

Tanya Berger-Wolf: Computational Ecology and AI for Conservation

Founding member of;  Board of Directors Member, Wild Me; Professor of Computer Science at University of Illinois at Chicago working at the intersection of computer science, wildlife biology, and social sciences

Tina Eliassi-Rad: Just Machine Learning

Core Faculty at the Network Science Institute, Associate Professor at the Khoury College of Computer Sciences, Northeastern University working in the areas of data mining, ethics of artificial intelligence, machine learning, network science, and computational social science

Rayid Ghani: Machine Learning for Social Good: Examples, Opportunities, and Challenges

Chief Scientist of 2012 Obama Campaign, Distinguished Career Professor at Carnegie Mellon with a joint appointment to the Heinz College of Information Systems and Public Policy and the School of Computer Science

Program Committee

  • Ceren Budak, School of Information
  • Yang Chen, Statistics
  • Danai Koutra, Computer Science and Engineering
  • Jing Liu, MIDAS
  • Sam Mukherji, Music Theory
  • Arvind Rao, Computational Medicine and Bioinformatics, and Radiation Oncology
  • Zhenke Wu, Biostatistics