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MIDAS Annual Symposium


"This annual symposium is the largest data research event on the University of Michigan campus."
Join top researchers, students, and companies in exploring the forefront of data science. During the event, join panel discussions hosted by Prof. Michael Traugott, and industry leaders including UBDI, Quicken Loans, and J.D. Power. Attend research talks from U of M data scientists. Mingle with researchers during several Poster Sessions, or present your own research.


Timings

08:30 AM - 05:00 PM (Nov 13) (General)
08:30 AM - 06:00 PM (Nov 14) (General)
08:30 AM - 05:00 PM ( Nov 15) (General)
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Entry Fees

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Participants

100 - 500
Delegates

10 - 50 Exhibitors Estimated Count

Category & Type

Conference
Science & Research
Education & Training

Editions

13 - 15 Nov 2019


Frequency One-time

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University of Michigan USA

96 events listed

User Community [ Users who have shown interest for this Event ]

VisitedOmar Malik

Omar Malik

Professor at GVSU

Grand Rapids, United States
VisitedRodrigo Erices

Rodrigo Erices

MADS Student at University of Michigan

Santiago, Chile
Gary Clemetson

Gary Clemetson

Economic Development at Lenawee Now

Ann Arbor, United States
Tyler

Tyler

Working at MSU

Lansing, United States
VisitedDennis Wiedbusch

Dennis Wiedbusch

Business Intelligence Manager at S&P Global

Canton, United States
Sha Chen

Sha Chen

Engineer at University of Michigan

Ann Arbor, United States
Brian Kelly

Brian Kelly

Ann Arbor, United States

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Schedule & Agenda

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Wed, 13 Nov08:00 AM - 12:00 PMDeep Learning Workshop - Amazon Session 1 Workshop
#Rackham East Conference

Pre-Registration REQUIRED.


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.

Wed, 13 Nov08:30 AM - 01:30 PMDeep Learning Workshop - Google Workshop
#Weiser 1010

Pre-Registration REQUIRED.


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.

Wed, 13 Nov01:00 PM - 05:00 PMDeep Learning Workshop - Amazon Session 2 Workshop
#Rackham East Conference

Pre-Registration REQUIRED.


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.

Thu, 14 Nov08:30 AM - 08:45 AMWelcome & Opening Remarks Opening Ceremony
#Rackham Amphitheatre
Thu, 14 Nov08:45 AM - 10:00 AMKeynote 1: Machine Learning for Social Good: Examples, Opportunities, and Challenges Keynote
#Rackham Amphitheatre

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.

Thu, 14 Nov10:00 AM - 11:00 AMPanel Discussion: Big Data and Political Science Roundtable
#Rackham Amphitheatre

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.

Thu, 14 Nov11:00 AM - 12:15 PMResearch Talks 1 Session
#Rackham Amphitheatre

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

Thu, 14 Nov12:15 PM - 02:15 PMPoster Session 1 Networking
#Rackham Assembly Hall

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. Overflow in the East and West conference rooms.

Thu, 14 Nov02:15 PM - 03:30 PMIndustry Discussion Panel: Data Science for the Next Ten Years in the Industry Roundtable
#Rackham Amphitheatre

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


Thu, 14 Nov03:30 PM - 04:30 PMKeynote 2: Just Machine Learning Keynote
#Rackham Amphitheatre

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.

Thu, 14 Nov04:30 PM - 05:30 PMResearch Talks Session 2 Session
#Rackham Amphitheatre

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

Thu, 14 Nov05:30 PM - 06:30 PMGeneral Reception Networking
#Rackham East Conference
Fri, 15 Nov08:30 AM - 09:45 AMKeynote 3: Computational Ecology and AI for Conservation Keynote
#Rackham Amphitheatre

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 – Wildbook.org) 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.

Fri, 15 Nov09:45 AM - 11:00 AMResearch Talks Session 3 Session
#Rackham Amphitheatre

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

Fri, 15 Nov11:00 AM - 12:30 PMPoster Session 2 & Student Poster Awards Session
#Rackham Assembly Hall

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.


Fri, 15 Nov12:30 PM - 01:00 PMClosing Remarks Closing Ceremony
#Rackham Amphitheatre
Fri, 15 Nov01:00 PM - 02:30 PMData Challenge Poster Session and Award Ceremony Competition
#Rackham Assembly Hall

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.

Fri, 15 Nov01:30 PM - 05:30 PMData Science for Music Mini-Symposium Session
#Rackham Amphitheatre

Featuring four projects funded by MIDAS.

  • “Understanding and Mining Patterns of Audience Engagement and Creative Collaboration in Largescale Crowdsourced Music Performances”
    Danai Koutra, Walter 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).

42.280808 -83.738258

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map of Rackham Graduate School - University of Michigan

Rackham Graduate School - University of Michigan

915 E Washington St Ann Arbor MI 48109
USA

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