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Understanding How the Brain Processes Music Through the Bach Trio Sonatas

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This event is open to public.

Daniel Forger, Professor of Mathematics and Computational Medicine and Bioinformatics
James Kibbie, Professor of Music and Chair of the Organ Department, University Organist
Caleb Mayer, Graduate Student Research Assistant (Mathematics)
Sarah Simko, Graduate Student Research Assistant (Organ Performance)

With support from the Data Science for Music Challenge Initiative through MIDAS, the team is taking a big data approach to understanding the patterns and principles of music. The project is developing and analyzing a library of digitized performances of the Trio Sonatas for organ by Johann Sebastian Bach, applying novel algorithms to study the music structure from a data science perspective. Organ students from the School of Music, Theatre & Dance will demonstrate how the Frieze Memorial Organ in Hill Auditorium is used to create big data files of live performances. The team will discuss how its analysis compares different performances to determine features that make performances artistic, as well as the common mistakes performers make. The digitized performances will be shared with researchers and will enable research and pedagogy in many disciplines, including data science, music performance, mathematics and music psychology.

Michigan Sports Analytics Symposium

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The Michigan Sports Analytics Society invites you to the Michigan Sports Analytics Symposium at the University of Michigan. The event will feature presentations from students currently participating in sports analytics research projects and faculty with significant interest in sports analytics. Come to learn about sports analytics and see how you can get involved in this rapidly growing topic at the University of Michigan. The Michigan Sports Analytics Symposium will consist of three parts: Poster Session, Presentation Session, and Panel Session. 






Poster Session (Space 2435)
5:00 to 6:00 PM

Speakers/Presentations (Ehrlicher Room)
6:15 – 6:20: Welcome & Intro
6:20 – 7:30: Presentations
     Ed Feng (Founder, The Power Rank)
     Tommy Gayfield (Strength & Conditioning Coach, Athletics, University of Michigan)
     Steven Broglio (Professor, Kinesiology, University of Michigan)
     Kyle Kumbier (Student, Biostatistics, University of Michigan)
7:40 – End:  Panel
     Moderator: Lisa Rabaut (Managing Director, Exercise Sports Science Initiative, University of Michigan)

NASEM Math & Statistics: Roundtable on Data Science Postsecondary Education

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The National Academies of Sciences, Engineering, and Medicine invites you to attend a one-day data science education meeting and webcast on December 10, 2018 in Washington, DC. The meeting will bring together data scientists and educators in academia, government, and industry to 1) learn about academic, government, non-profit, and private sector projects promoting data science for socially desirable outcomes and their intersection with education and hiring, and 2) explore how socially motivated projects and topics can engage and excite students. For more information, please visit the event website.


This event is part of an ongoing series of roundtable meetings that take place approximately four times per year. It is organized by the Committee on Applied and Theoretical Statistics in conjunction with the Board on Mathematical Sciences and Their Applications, the Computer Science and Telecommunications Board, and the Board on Science Education. Learn more about the roundtable and watch videos of past meetings on the roundtable website.

Learn more about the roundtable and watch past meetings at nas.edu/dsert.


Register to Attend In Person or Online

AIM Analytics Seminar – Dan Davis, PhD Candidate, TU Delft, the Netherlands.

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Improving Online Learning Outcomes Using Large-Scale Learning Analytics

Abstract: This talk will cover a holistic approach to improving learning outcomes and behavior in large-scale learning environments—namely MOOCs. I begin by sharing the results of a study exploring the extent to which learners follow (or deviate from) the designed learning path and the impact this behavior has on eventual learning outcomes. We next take a deeper dive into the design of online courses with a large-scale learning design approach, where I’ll present an automated method developed to categorize courses based on their design. With these trends in learning & teaching behavior in mind, the talk will conclude with the results of a series of randomized experiments (A/B tests) carried out in live MOOCs designed to provide additional support to learners. From these experiments we arrive at a better understanding of which instructional & design strategies are most effective for improving learning outcomes and behavior at scale.

Bio: Dan’s research uses and advances learning analytics techniques in open, online education at scale by pushing the boundaries towards personalized & adaptive learning environments. Dan develops methods to gain a deeper understanding about how the design of online learning environments affects learner success and engagement, often by implementing and testing instructional interventions at scale using randomized controlled experiments. Dan earned his BA in English, Writing & Mass Communication with a minor in Graphic Design from Assumption College in Worcester, Mass. His MA is from Georgetown University in Communication, Culture & Technology, and he is currently finishing his PhD in Computer Science, Learning Analytics from TU Delft in the Netherlands.

Lunch will be provided.

AIM Analytics is a bi-weekly seminar series for researchers across U-M who are interested in learning analytics. The field of learning analytics is a multi and interdisciplinary field that brings together researchers from education, learning sciences, computational sciences and statistics, and all discipline-specific forms of educational inquiry.

Women in Big Data at Michigan Symposium

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Please join us for the Women in Big Data at Michigan symposium. This day-long symposium will highlight women data science researchers at U-M, provide resources and support for women pursuing careers in data science, a poster session, lunch time round table discussions, a faculty panel, and ample time for networking.

Please fill out the registration form if you plan to attend and consider submitting a poster. 

For more information, see the event page at https://midas.umich.edu/2018-wbdm/.

Keynote Speaker:

Xihong Lin
Henry Pickering Walcott Professor of Biostatistics
Harvard T.H. Chan School of Public Health

Dr. Lin’s research focuses on the development and application of statistical and computational methods to analyze high-throughput genetic and genomic data in epidemiological, environmental and clinical studies, and to analyze complex exposure and phenotype data in observational studies.

U-M Speakers:

Presenters Panel Participants
“Charting a Career in Data Science”
Jenna Wiens, Computer Science and Engineering Moderator: Liza Levina, Statistics
Snigdha Panagrahi, Statistics Bhramar Mukherjee, Biostatistics
Heather Mayes, Chemical Engineering Rada Mihalcea, Computer Science and Engineering
Danai Koutra, Computer Science and Engineering Amy Cohn, Industrial and Operations Engineering
Veronica Berrocal, Biostatistics Rocio Titiunik, Political Science
Maureen Sartor, DCMB Jennifer Linderman, Chemical Engineering

Pushing Mobile Inventions Forward Seminar: Fjola Helgadottir, PhD – Director of AI Therapy

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Fjola Helgadottir, PhD

Director of AI Therapy

Vancouver CBT Centre


Translating Clinical Psychology Treatments into Algorithms: Successes and Challenges

Abstract: Computerized therapy has the potential to revolutionize how evidence based psychological interventions are delivered to those who need them. Many of the recent advances in AI, from computer vision to natural language processing, will doubtlessly be integral components of future treatment systems.

There is a wide range of approaches to computerized therapy. Many research projects aim to replicate the face-to-face therapy experience. This seems like a natural approach, given that this is a longstanding and proven model of therapy. For example, these systems make use of avatars and chatbots. However, this approach may be misguided. Computer-based approaches and human therapists are fundamentally different, and designing one to mimic the other may not be optimal. The goal should be to find the most effective methods of targeting the key mechanisms that are paramount to change in mental health.

In this talk Dr. Helgadottir will take a look at computerized therapy from the perspective of a practicing clinical psychologist. She will review some of the advantages that computers have over human therapists, as well as considering limitations of these systems. As a practical example, she will explain how her online “Overcome social anxiety” program works and discuss promising results from recent clinical trials.

Bio: Dr Fjola Dogg Helgadottir is a Director at AI-Therapy, a registered psychologist at the Vancouver CBT Centre and previously a Senior Research Clinician at Department of Psychiatry, University of Oxford in the UK. She is a Chartered clinical psychologist within the British Psychological Society, and a registered psychologist with the UK Health and Care Professions Council and with the British Columbia College of Psychologists. Fjola has completed four degrees in psychology (see more professional qualifications). AI-Therapy grew out of her doctoral research, which was focused on innovative computer-based treatments for anxiety disorders.

Fjola has written extensively about online therapy, both in peer reviewed academic journals and conferences. She is an expert writer for Psychology Today with her open access blog Man vs Machine and is featured frequently in the Icelandic media. See Fjola’s publications for more details.

Fjola has received several major awards for her internationally recognized research, including Australia’s prestigious Tracy Goodall Early Career Award for Research Achievement. In addition, she has trained to the highest level as a clinical psychologist in Australia, and ran a successful private practice in Sydney. She currently provides consulting services on the topic of online psychology and psychiatry for her company AICBT Ltd, which has clients in Sydney, Australia; Oxford and London, UK; and Denver and New York in the USA.

MIDAS Learning Analytics Challenge Symposium

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Learning analytics is one of the research focus areas that MIDAS supports with its Challenge Awards.  Our long-term goal is to support this research area more broadly, using the Challenge Award projects as the starting point to build a critical mass.  This symposium offers a platform for all participants to explore collaboration opportunities and aims to attract more researchers to our hub.  It will feature in-depth presentations from two Challenge Award teams, and all participants are encouraged to submit posters on research related to Learning Analytics.


9 am to 11:30 am: Welcome and Challenge Award presentations

11:30 am to 1 pm: Lunch, Poster Session, Networking [poster dimensions: up to 6ft wide X 4ft height]

1 to 2 pm: Panel discussion: The Future of Data Science for Learning Analytics at U-M


  • Steve DesJardins, Education, Public Policy
  • Cynthia Finelli, Engineering Education Research Program
  • Al Hero (Moderator), MIDAS, Electrical Engineering and Computer Science
  • Rada Mihalcea, Computer Science Engineering
  • Stephanie Teasley, Information


Please register online.  Please submit poster abstracts (< 300 words).  Submission Deadline: May 15.

For questions: midas-research@umich.edu.

Recommended Visitor Parking:  Palmer Parking StructurePalmer Drive, Ann Arbor

ASA Symposium on Data Science & Statistics

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Beyond Big Data: Leading the Way

The ASA’s newest conference, the Symposium on Data Science & Statistics, will take place in Reston, Virginia, May 16-19, 2018. The symposium is designed for data scientistscomputer scientists, and statisticians analyzing and visualizing complex data.

The annual SDSS will combine data science and statistical machine learning with the historical strengths of the Interface Foundation of North America (IFNA) in computational statistics, computing science, and data visualization. It will continue the IFNA’s tradition of excellence by providing an opportunity for researchers and practitioners to share knowledge and establish new collaborations.

Offering sessions centered on the following six topic areas:
Data Science                                            Data Visualization
Machine Learning                                  Computing Science
Computational Statistics                      Applications

Key Dates:
December 5, 2017 – Contributed and E-Poster Online Abstract Submission Opens
January 18, 2018 – Contributed and E-Poster Online Abstract Submission Closes
February 1, 2018 – Conference Registration Opens

MIDAS Data Science for Transportation Research Challenge Symposium

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Data Science for Transportation is one of the research focus areas that MIDAS supports with its Challenge Awards.  Our long-term goal is to support this research area more broadly, using the Challenge Award projects as the starting point to build a critical mass.  This symposium offers a platform for all participants to explore collaboration opportunities and aims to attract more researchers to our hub.  It will feature in-depth presentations from two Challenge Award teams, and all participants are encouraged to submit posters on research related to Data Science for Transportation.


10:30 am to 10:45 am: Welcome and Introductions, MIDAS Co-Director Al Hero

10:45 am to 12:00 pm: Building a Transportation Data Ecosystem, Carol Flannagan and team

12:00 to 1 pm: Lunch, Poster Session, Networking

1 to 2:00 pm: Reinventing Public Urban Transportation and Mobility, Pascal Van Hentenryck

2 to 3 pm: Panel discussion: The Future of Data Science for Transportation at U-M.


  • Clive D’Souza, Industrial and Operations Engineering
  • Carol Flannagan, U-M Transportation Research Institute
  • Al Hero (Moderator), MIDAS, Electrical Engineering and Computer Science
  • Dave LeBlanc, U-M Transportation Research Institute
  • Pascal Van Hentenryck, Industrial and Operations Engineering


Please register online.  Please submit poster abstracts (< 300 words).  Submission Deadline: April 30.

For questions: midas-research@umich.edu.

Recommended Visitor Parking:  Palmer Parking Structure, Palmer Drive, Ann Arbor


NASEM Webinar: Data Science for Undergraduates – Opportunities & Options

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Data Science for Undergraduates: Report Release Webinar

As our economy, society, and daily life become increasingly dependent on data, new college graduates entering the workforce need to have the skills to analyze data effectively. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine organized a study to explore what data science skills are essential for undergraduates and how academic institutions should structure their data science education programs. We invite you to join us for a report release webinar on May 2, 2018 at 11am ET. During this webinar, study co-chairs Laura Haas and Alfred Hero will discuss the report’s findings and recommendations, followed by a question and answer session with webinar participants.

Learn more about the study, download the interim and final reports, and watch past webinars on the study webpage at nas.edu/EnvisioningDS.

Register for the Webinar.


Click here to join the webinar

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