A Data Scientist Plays Games: Nick Berry

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A Data Scientist Plays Games.  This is a presentation broken down into two parts.  The first is how to use mathematical techniques to analyze classic card and board games, and the second part is how data science techniques were applied in real life to support games on the Facebook platform.  This presentation is about 1.5 hours, with a target audience probably suited to CS/software engineering.  It’s light-hearted and fun.


This event will be hosted online via Zoom


Nick Berry, a native of the UK, has lived in Seattle for the last 25 years. He was educated as a rocket scientist and aircraft designer, graduating with a Masters Degree in Aeronautical and Astronautical Engineering.

Upon graduation, he joined a group of friends to form a software company, specializing in electronic mapping and route planning. This company was grown organically, and earned an unprecedented number of awards and accolades, including the British Design Award and The Queen’s Award for Technology, presented by Her Majesty in 1991. In 1994 Nick was recognized by the Sunday Times Magazine as “One of the top 50 entrepreneurs of the decade”. In 1994, after the company had grown to 50 people worldwide, it was sold to Microsoft.

Nick moved to America with the sale and spent 14 years working for Microsoft, the last ten of which were in the Microsoft Casual Game team. During his tenure, he filed a variety of patents for Microsoft, and represented Microsoft at various conferences and speaking engagements.

After leaving Microsoft, he joined RealNetworks to work as the GM of customer analytics for their games division, GameHouse.

After GameHouse, Nick spent five years as a Data Scientist, working for Facebook in their Seattle office.

In addition to his engineering expertise, Nick is passionate about data privacy and holds a CIPP qualification from the International Association of Privacy Professionals. He is an active member of the privacy community and speaks at various events about the legal and ethical aspects of data collection, use, and destruction.

In July 2013, Nick gave a TEDx talk about Passwords and the Internet, and in 2015 was nominated by GeekWire as Geek-of-the-week. In 2019 he was recognized as one of the 50 over 50 in the video games industry.

NSF Learning Analytics Workshop

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This NSF workshop brings together learning and data scientists with various backgrounds and prior expertise to collaboratively solve the research challenges of development of instruction, assessment of competence of current and would-be workers, and evaluation of learning tools.  Three specific questions will be addressed: how to define competence, measure competence and evaluate new approaches to learning.  Speakers are invited from across industry and academia to ensure a broad perspective and specifically to take into account employer’s perspectives.  Please join us for an exciting event and lively discussions.

Please register if you would like to attend.

March 18, 2019
8:00 a.m. – Registration
8:30 a.m.  – Welcome and Introductions

  • Stephanie Teasley, Research Professor, School of Information, University of Michigan
  • Rada Mihalcea, Professor, Computer Science and Engineering, University of Michigan
  • Henry Kelly, Senior Scientist, Michigan Institute for Data Science, University of Michigan

8:45 a.m. – Talks and discussion on defining competence

Marie Cini

President and CEO

The Council for Adult and Experiential Learning



David Blake





9:40 a.m. – Talks and discussion on measuring competence

Bror Saxberg

Vice President

Learning Science at Chan Zuckerberg Initiative



Tammy Wang

Vice President

Data Science and Analytics at Riviera Partners



10:35 a.m. – Talks and discussion on evaluating new approaches to learning

Norman Bier


Open Learning Initiative, DataLab, Carnegie Mellon University



Yun Jin Rho


Efficacy Analytics and Studies, Pearson



11:30 a.m. to 12:00 p.m. – Networking

March 19, 2019

9:45 a.m. to 12:50 p.m. –  Panel discussions on each of the three topics
12:50 p.m. – Concluding remarks and discussion of next steps
1:00 p.m. – Adjourn

ACNN Big Data Neuroscience Workshop

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Organized by Advanced Computational Neuroscience Network (ACNN)


Come join the ACNN Big Data Neuroscience Workshop and enjoy:

❖ Keynotes and Invited Talks
❖ Data Sharing Initiatives
❖ Demonstration of Neuroscience Computational Platforms
❖ Reproducibility Best Practices
❖ Learning Environment for Students and Early-Career Researchers

Students, trainees, fellows, junior investigators from the Midwest as well outside academic institutions and industry partners are invited.

Geospatial analysis with Google Earth Engine

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Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. This workshop will provide an introduction to GEE. We will cover data models in GEE, basic vector and raster operations, and classification in both feature and image space.

You should be familiar with vector and raster data, GIS and remote sensing. We will use the web-based IDE for the Earth Engine JavaScript API. You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine.

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 Working Group: Teaching Data Science

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The Michigan Institute for Data Science (MIDAS) continues to convene a working group on teaching data science. As we incorporate data science into almost every level of teaching, many issues need to be thoroughly thought out: How do we teach data science to students with various levels of preparation, from those with little quantitative training to STEM students? How do we build data science modules to incorporate into existing domain science courses? How do we raise awareness of ethics and social responsibility in data science teaching? How do we teach data science to independent researchers, including faculty, who want to build data science into their research? What teaching resources are available at UM? Our working group welcomes anyone interested in these topics. We are developing an interdisciplinary team to foster new ideas and collaborations in the development of data science teaching methods and materials.

Please RSVP.  

The agenda for the meeting includes:

  • Introduction
  • Short presentations
    • Kerby Shedden (Professor, Statistics, and CSCAR director) will share insight from his experience teaching “capstone” style courses for undergraduate and MS students, based around case studies and focus on methods, formulating good questions, and writing.
    • Heather Mayes (Assistant Professor, Chemical Engineering) will talk about the design of a Data Science ramp-up course for engineering students and how to integrate it with existing course offerings.
    • Aaron Keys (data scientist, Airbnb) will give the industry perspective on the various training paths that students can take for a career in data science.
  • Open discussion of ideas and collaboration, and sharing resources

For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (ljing@umich.edu734-764-2750).

Mini-course: Introduction to Python — Sept. 11-14

By | Data, Educational, Events, General Interest, News

Asst. Prof. Emanuel Gull, Physics, is offering a mini-course introducing the Python programming language in a four-lecture series. Beginners without any programming experience as well as programmers who usually use other languages (C, C++, Fortran, Java, …) are encouraged to come; no prior knowledge of programming languages is required!

For the first two lectures we will mostly follow the book Learning Python. This book is available at our library. An earlier edition (with small differences, equivalent for all practical purposes) is available as an e-book. The second week will introduce some useful python libraries: numpyscipymatplotlib.

At the end of the first two weeks you will know enough about Python to use it for your grad class homework and your research.

Special meeting place: we will meet in 340 West Hall on Monday September 11 at 5 PM.

Please bring a laptop computer along to follow the exercises!

Syllabus (Dates & Location for Fall 2017)

  1. Monday September 11 5:00 – 6:30 PM: Welcome & Getting Started (hello.py). Location: 340 West Hall
  2. Tuesday September 12 5:00 – 6:30 PM: Numbers, Strings, Lists, Dictionaries, Tuples, Functions, Modules, Control flow. Location: 335 West Hall
  3. Wednesday September 13 5:00 – 6:30 PM: Useful Python libraries (part I): numpy, scipy, matplotlib. Location: 335 West Hall
  4. Thursday September 14 5:00 – 6:30 PM: Useful Python libraries (part 2): 3d plotting in matplotlib and exercises. Location: 335 West Hall

For more information: https://sites.lsa.umich.edu/gull-lab/teaching/physics-514-fall-2017/introduction-to-python/


Midwest Big Data Hub Transportation and Mobility Conference, Ann Arbor, MI

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signature-informal-verticalMBDH logo

The Midwest Big Data Hub






“Big Data for Transportation and Mobility”


Please Register

Details are on the main conference webpage.


The NSF supported Midwest Big Data Hub has made data for transportation a priority.  Its goal is to bring together experts in the increasingly powerful tools of Big Data (including visualization, machine learning, statistical models, integration of heterogeneous data, data scrubbing, privacy and security) with domain experts.  The Midwest has been a center of innovation in transportation for generations and data-related research has become a major focus of interest for corporate, academic, and governmental organizations.  This conference includes a series of presentations providing an overview of research underway in the Midwest hat will help us understand the scope of this work, encourage cross-fertilization, and possibly nucleate future collaborations.

One of the highlights of the meeting will be a series of talks by faculty from the schools that are the core of the Midwest Big Data Hub.  Additional activities include tours of research sites on the Ann Arbor Campus, short tutorials and breakout sessions on pertinent topics. 

Students are especially welcome and a poster session, part of the conference reception on June 22, will provide an opportunity for them to share their research. There is some limited travel and hosting funding available for students; restrictions apply and requests for travel/ hosting support should be submitted while registering. 

The Midwest Big Data Hub is supported by the National Science Foundation through award #1550320. 

MBDH-NSF Attribution MBDH logo

Frontiers in Data Science and Computing, Michigan State University

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The Frontiers in Data Science and Computing workshop brings together visionaries, scientists, practitioners and other stakeholders in computational science that includes both algorithmic aspects of data science and scientific computing.  Our model for the Frontiers workshop series is based on the successful format commonly seen in Silicon Valley, which combines “big picture” motivation talks with activities designed to foster ideas, create communities, and allow significant interactions between application/domain scientists, algorithm designers and end users.  Based on the successful outcomes of our pilot Frontiers workshop in 2015, we anticipate that this annual workshop series will result in fruitful scientific exchanges and new interdisciplinary collaborations across academia, industry and government agencies.  In 2016, the Frontiers workshop will focus on data-infused computing, which is a new area that merges data science with scientific computing.  “Frontiers in Data Science and Computing 2016” will be one of the first venues to explore this topic and its high-impact potential in areas as diverse as medicine, astronomy, physics, biology, computer architecture, smart grids, and climate change.

Workshop: Refining the Concept of Scientific Inference When Working with Big Data — June 8-9 (webcast)

By | General Interest, News

The National Academies of Sciences, Engineering and Medicine Committee on Applied and Theoretical Statistics is holding a workshop titled “Refining the Concept of Scientific Inference When Working With Big Data” in Washington DC, June 8-9.

The workshop will bring together statisticians, data scientists and domain researchers from different biomedical disciplines to explore four key issues of scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models

Prof. Alfred Hero, co-director of the Michigan Institute for Data Science (MIDAS) and Professor of Electrical Engineering and Computer Science, is a co-chair of the event.

More information: