Michigan Institute for Computational Discovery and Engineering

Modeling spatially correlated data
2019-06-28 13:00 - 16:00 (America/Detroit) | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover introductory concepts, tools, and techniques to model spatially referenced data observed over a regular or irregular grid. We will cover models such as spatial autoregressive that emphasizes the idea of spatial correlation via neighborhood.
The workshop will focus on conceptual aspects, diagnostics tests, implementation in R, and interpretation of results.

Introduction to Stata
2019-06-21 09:30 - 16:00 | Modern Languages Building (MLB), Room 2001A
Topics:

By the end of the workshop, participants will be able to:

Work with Stata, including using Do-files and using the help system.
Get data into Stata and manage your data files
Establish familiarity with your data
Clean the data to prepare it for analysis
Check for basic errors in the data
Generate new variables or manipulate existing variables
Merge or reshape the data.
Produce summary tables and descriptive statistics.

Note: This is a full day workshop. To get the most out of it, please plan to stay for the entire class.

(Topics subject to change)
If you have questions about this workshop, please send an email to jerrick@umich.edu

R package demo: gganimate and patchwork
2019-06-20 15:00 - 16:30 | Modern Languages Building (MLB), Room 2001A
This brief workshop will demonstrate the capabilities of two recent R packages, gganimate and patchwork. One package allows the data explorer to provide some lively enhancement to an otherwise static plot, without doing much more than standard plots with ggplot2. Likewise, the other package can seamlessly combine multiple ggplots of varying kinds into one cohesive visualization.
Prior knowledge of ggplot2 is required, but not much else. Note also that this is just a demo. No statistical or analytical concepts will be discussed, and while participation is certainly welcomed, one can simply watch the demonstration.

Advanced batch computing with Slurm on the Great Lakes cluster
2019-06-07 13:00 - 16:30 | Modern Languages Building (MLB), Room 2001A
OVERVIEW

This workshop will cover some more advanced topics in cluster computing on the U-M Great Lakes Cluster. Topics to be covered include a review of common parallel programming models and basic use of Great Lakes; dependent and array scheduling; troubleshooting and analysis; a brief introduction to workflow scripting using bash; parallel processing in one or more of Python, R, and MATLAB; and parallel profiling of C and Fortran code using Allinea Performance Reports and Allinea MAP of one or more of MPI and OpenMP programs.
PRE-REQUISITES
This course assumes familiarity with the Linux command line as might be got from the CSCAR/ARC-TS workshop Introduction to the Linux Command Line. In particular, participants should understand how files and folders work, be able to create text files using the nano editor, be able to create and remove files and folders, and understand what input and output redirection are and how to use them.
INSTRUCTORS
Dr. Charles J Antonelli
Research Computing Services
LSA Technology Services
Charles is a High Performance Computing Consultant in the Research Computing Services group of LSA TS at the University of Michigan, where he is responsible for high performance computing support and education, and was an Advocate to the Departments of History and Communications. Prior to this, he built a parallel data ingestion component of a novel earth science data assimilation system, a secure packet vault, and worked on the No. 5 ESS Switch at Bell Labs in the 80s. He has taught courses in operating systems, distributed file systems, C++ programming, security, and database application design.
John Thiels
Research Computing Services
LSA Technology Services
MATERIALS

Lecture Notes (TBD)
MPI Profiling with Allinea MAP
Great Lakes Slurm HPC cluster

COURSE PREPARATION
In order to participate successfully in the workshop exercises, you must have a Great Lakes user account, a Great Lakes job account (one is created for each workshop), and be enrolled in Duo. The user account allows you to log in to the cluster, create, compile, and test applications, and prepare jobs for submission. The job account allows you to submit those jobs, executing the applications in parallel on the cluster and charging their resource use against the account. Duo is required to help authenticate you to the cluster.
GREAT LAKES USER ACCOUNT
If you already have a Flux user account, you don’t need to do anything to obtain a Great Lakes user account. Otherwise, go to the Flux user account application page at: https://arc-ts.umich.edu/fluxform/ .
Please note that obtaining a user account requires human processing, so be sure to do this at least two business days before class begins.
GREAT LAKES JOB ACCOUNT
We create a job account for the workshop so you can run jobs on the cluster during the workshop and for one day after for those who would like additional practice. The workshop job account is quite limited and is intended only to run examples to help you cement the details of job submission and management. If you already have an existing Great Lakes job account, you can use that, though if there are any issues with that job account, we will ask you to use the workshop job account.
DUO AUTHENTICATION
Duo two-factor authentication is required to log in to the cluster. When logging in, you will need to type your UMICH (AKA Level 1) password as well as authenticate through Duo in order to access Great Lakes.
If you need to enroll in Duo, follow the instructions at Enroll a Smartphone or Tablet in Duo.
Please enroll in Duo before you come to class.

LAPTOP PREPARATION
You do not need to bring your own laptop to class. The classroom contains Windows or Mac computers, which require your uniqname and UMICH (AKA Level 1) password to login, and that have all necessary software pre-loaded.
If you want to use a laptop for the course, you are welcome to do so: please see our web page on Preparing your laptop to use Flux. However, if there are problems connecting your laptop, you will be asked to switch to the provided computer for the class. We cannot stop to debug connection issues with personal or departmental laptops during the class.

If you are unable to attend the presentation in person we will be offering a link into the live course via BlueJeans. Please register as if attending in person. This will put you on the wait list but we will get your account setup for remote attendance.

Multiple testing and large-scale inference in Python
2019-06-04 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover techniques for conducting large-scale inference, using Python and its libraries. We will cover the principals of how large scale inference is different from classical inference, and why multiple comparisons should usually be accounted for in an analysis. We will then discuss the Bonferroni method, Benjamini and Hochberg’s False Discovery Rate (FDR), Efron’s local FDR, the Scheffe approach, and the knockoff filter. The motivation for each approach will be covered, and the process for carrying it out in a data analysis will be discussed in detail. Several case studies will be used to illustrate these analytic approaches.

Introduction to the Great Lakes cluster and batch computing with Slurm
2019-05-31 13:00 - 16:30 | Modern Languages Building (MLB), Room 2001A
OVERVIEW
This workshop will provide a brief overview of the components of the Great Lakes Cluster. The main body of the workshop will cover the resource manager and scheduler, creating submissions scripts to run jobs and the options available in them, and hands-on experience. By the end of the workshop, every participant should have created a submission script, submitted a job, tracked its progress, and collected its output. Participants will have several working examples from which to build their own submissions scripts in their own home directories.
PRE-REQUISITES
This course assumes familiarity with the Linux command line as might be got from the CSCAR/ARC-TS workshop Introduction to the Linux Command Line. In particular, participants should understand how files and folders work, be able to create text files using the nano editor, be able to create and remove files and folders, and understand what input and output redirection are and how to use them.
INSTRUCTORS
Dr. Charles J Antonelli
Research Computing Services
LSA Technology Services
Charles is a High Performance Computing Consultant in the Research Computing Services group of LSA TS at the University of Michigan, where he is responsible for high performance computing support and education, and was an Advocate to the Departments of History and Communications. Prior to this, he built a parallel data ingestion component of a novel earth science data assimilation system, a secure packet vault, and worked on the No. 5 ESS Switch at Bell Labs in the 80s. He has taught courses in operating systems, distributed file systems, C++ programming, security, and database application design.
John Thiels
Research Computing Services
LSA Technology Services
Mark Champe
Research Computing Services
LSA Technology Services
MATERIALS

Lecture notes (TBD)
Great Lakes Slurm HPC cluster

COURSE PREPARATION
In order to participate successfully in the workshop exercises, you must have a Great Lakes user account, a Great Lakes job account (one is created for each workshop), and be enrolled in Duo. The user account allows you to log in to the cluster, create, compile, and test applications, and prepare jobs for submission. The job account allows you to submit those jobs, executing the applications in parallel on the cluster and charging their resource use against the account. Duo is required to help authenticate you to the cluster.

GREAT LAKES USER ACCOUNT
If you already have a Flux user account, you don’t need to do anything to obtain a Great Lakes user account. Otherwise, go to the Flux user account application page at: https://arc-ts.umich.edu/fluxform/ .
Please note that obtaining a user account requires human processing, so be sure to do this at least two business days before class begins.

GREAT LAKES JOB ACCOUNT
We create a job account for the workshop so you can run jobs on the cluster during the workshop and for one day after for those who would like additional practice. The workshop job account is quite limited and is intended only to run examples to help you cement the details of job submission and management. If you already have an existing Great Lakes job account, you can use that, though if there are any issues with that job account, we will ask you to use the workshop job account.

DUO AUTHENTICATION
Duo two-factor authentication is required to log in to the cluster. When logging in, you will need to type your UMICH (AKA Level 1) password as well as authenticate through Duo in order to access Great Lakes.
If you need to enroll in Duo, follow the instructions at Enroll a Smartphone or Tablet in Duo.
Please enroll in Duo before you come to class.

LAPTOP PREPARATION
You do not need to bring your own laptop to class. The classroom contains Windows or Mac computers, which require your uniqname and UMICH (AKA Level 1) password to login, and that have all necessary software pre-loaded.
If you want to use a laptop for the course, you are welcome to do so: please see our web page on Preparing your laptop to use Flux. However, if there are problems connecting your laptop, you will be asked to switch to the provided computer for the class. We cannot stop to debug connection issues with personal or departmental laptops during the class.
If you are unable to attend the presentation in person we will be offering a link into the live course via BlueJeans. Please register as if attending in person. This will put you on the wait list but we will get your account setup for remote attendance.

Introduction to GIS
2019-05-30 13:00 - 16:30 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover introductory GIS concepts, tools, and techniques. We will use QGIS (and ArcGIS) and learn the basics of vector- and network-data GIS by solving problems. You will also learn to generate maps. We will use the graphical user interface and no programming experience is required for this workshop.
The workshop is meant for students and researchers who want to have a quick and simple exposure to GIS concepts and tools.

Simulation studies in Python
2019-05-28 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
Simulation studies are used to understand the properties of statistical procedures. This technique is widely used when conducting power analysis for research studies, which is one focus for this workshop. Simulation studies can also be used to assess the performance of statistical procedures in non-standard settings, such as when there are concerns about missing data, selection bias, measurement errors, or model specification. Another role for simulation studies is to contrast the performance of alternative statistical methods, e.g. for prediction.
This workshop will emphasize practical aspects of conducting simulation studies using Python, including data simulation, automation, and interpretation and communication of simulation findings.

Introduction to the Twitter API
2019-05-23 16:00 - 17:00 | Room 1450 – Institute for Social Research, 426 Thompson Street, Ann Arbor, MI, 48109, United States
This workshop is designed for those who are interested in getting started with their own research and analytics of Twitter data. The workshop introduces workflows for using the Twitter API for programmers and non-programmers alike. For non-programmers, we’ll be using the Postman software. For programmers, we’ll demonstrate the same process in Python. We will also discuss some of the limitations of the Twitter API and cover how to parse the JSON data retrieved from it.

This workshop accompanies the #UMTweetCon2019 conference on the use of Twitter data in research and analytics at the University of Michigan on May 23, 2019. Participants interested in attending the workshop are invited to also attend the #UMTweetCon2019 panel and poster sessions to learn more about ongoing Twitter data research at U-M.

Introduction to Survey Design: Data Collection, Questionnaire Design and Response Processes-Lecture
2019-05-22 09:00 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This lecture-format workshop will present an overview of available modes and methods of survey data collection as well as an introduction to the survey response process and implications for questionnaire design. Participants will gain an appreciation of the tradeoffs inherent in survey design decisions and how design can affect data quality and survey errors. Topics will include:

Survey errors, in particular measurement, coverage, and nonresponse error.
What to consider when selecting a data collection method for a particular research question.
Measurement (response) error and how to reduce it through question wording/format and questionnaire structure.

The role of the interviewer and interviewer effects.

Dimension Reduction with Python
2019-05-21 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover dimension reduction techniques for data analysis in Python, focusing on Principal Component Analysis (PCA), factor analysis, and canonical correlation analysis (CCA). Depending on audience interest, dimension reduction regression (e.g. SIR), and kernel versions of the classical dimension reduction techniques can also be discussed.
The presentation will begin by covering the conceptual basis of these methods, then we will discuss several case studies. The Statsmodels, Sklearn, Numpy, and Scipy Python libraries will be used to conduct the analyses. Visualization of results using Matplotlib and Seaborn will also be covered.

Introduction to the Flux cluster and batch computing
2019-05-21 13:00 - 17:00, 2019-05-15 13:00 - 17:00 | Modern Languages Building (MLB), Room 2001A
Overview
This workshop will provide a brief overview of the components of the Flux Cluster. The main body of the workshop will cover the resource manager and scheduler, creating submissions scripts to run jobs and the options available in them, and hands-on experience. By the end of the workshop, every participant should have created a submission script, submitted a job, tracked its progress, and collected its output. Participants will have several working examples from which to build their own submissions scripts in their own home directories.
Prerequisites
Obtain a user account on Flux. If you do not have a Flux user account, click here to go to the account application page at: https://arc-ts.umich.edu/fluxform/
Register for Duo authentication. See below for details.
This course assumes familiarity with the Linux command line as might be got from the CSCAR/ARC-TS workshop Introduction to the Linux Command Line. In particular, participants should understand how files and folders work, be able to create text files using the nano editor, be able to create and remove files and folders, and understand what input and output redirection are and how to use them.
Instructor
Bennet Fauber
Scientific Applications Analyst
Advanced Research Computing — Technology Services
Bennet Fauber is a member of the technical team that administers the Flux cluster.
Materials
In-class handouts

Output from qstat
Output from checkjob
Output from mdiag
Output from showq

Supplementary materials

Flux in Ten Easy Steps
Using PBS
Using software modules
Linking libraries with applications
Establishing and managing a Flux project allocation

Course Preparation
In order to participate successfully in the workshop exercises, you must have a Flux user account and be registered for the Duo authentication service. The user account allows you to log in to the cluster, create, compile, and test applications, and prepare Flux jobs for submission. Information about registering for Duo is at the ITS Two Factor Authentication web page.
Flux user account
A single Flux user account can be used to prepare and submit jobs using various allocations. If you already already possess a user account, you can use it for this course, you can skip to “Flux allocation” below. If not, please visit https://arc-ts.umich.edu/fluxform to obtain one. A user account is free to members of the University community. Please note that obtaining an account requires human processing, so be sure to do this at least two business days before class begins.
Flux allocation
We create an allocation for the workshop so you can run jobs on the cluster during the workshop and for one day after for those who would like additional practice. The workshop allocation is quite limited and is intended only to run examples to help you cement the details of job submission and management. If you already have an existing Flux allocation, you can use that, though if there are any issues with that allocation, we will ask you to use the workshop allocation.
Laptop Preparation
The classroom contains Windows computers, which require your uniqname and UMICH password to login, and which have all necessary software preloaded.
If you want to use a laptop for the course, you are welcome to do so. However, if there are problems connecting your laptop, you will be asked to switch to the provided computer for the class. We will try to help resolve issues after the workshop.
If you want to try to prepare your laptop for use in the class, please see our web page on Preparing your laptop to use Flux. If you have a Windows computer, we recommend that you install PuTTY/WinSCP from the Blue Disc web site. Macintosh computers typically have the minimum requirements installed as part of the system.
More help
Please use our class mailing list hpc101course@umich.edu for questions, comments, or to seek further assistance.

Advanced batch computing on the Flux cluster
2019-05-20 13:00 - 16:00 | Modern Languages Building (MLB), Room 2001A
This course will cover some more advanced topics in cluster computing on the U-M Flux Cluster. Topics to be covered include a review of common parallel programming models and basic use of Flux; dependent and array scheduling; advanced troubleshooting and analysis using checkjob, qstat, and other tools; and parallel debugging and profiling of C and Fortran code, including logging, gdb (line-oriented debugging), ddt (GUI-based debugging) and map (GUI-based profiling) of MPI and OpenMP programs. We will issue you a temporary allocation to use for the course, or you can use your existing Flux allocations, if any.
Course Prerequisites: Introduction to Batch Computing on Flux or equivalent. This course assumes familiarity with the Linux command line, text editing on Linux, and a basic understanding of Flux including how to submit and track jobs.
Please use our class mailing list hpc101course@umich.edu for questions, comments, or to seek further assistance.

Introduction to the Linux Command Line
2019-05-13 09:00 - 12:00, 2019-05-10 13:00 - 16:00 | Modern Languages Building (MLB), Room 2001A
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also generically referred to as “the command line”. Topics include: a brief overview of Linux, the Bash shell, navigating the file system, basic commands, shell redirection, permissions, processes, and the command environment. The workshop will also provide a quick introduction to nano a simple text editor that will be used in subsequent workshops to edit files.
INSTRUCTOR
Kenneth Weiss
IT Project Senior Manager
Medical School Information Services (MSIS)
Ken is a High Performance Computing Consultant in the Computational Research Consulting Division of MSIS at the University of Michigan. He works with a team of IT specialists to provide high performance computing support and training for the Medical School. Prior to this, he spent 21 years managing research computing, including an HPC cluster, for Dr. Charles Sing in the Human Genetics Department.
MATERIALS

Lecture Notes
Reference text: William E Shotts, Jr., “The Linux Command Line: A Complete Introduction,” No Starch Press, January 2012 .http://linuxcommand.org/tlcl.php
Download Creative Commons Licensed version at http://downloads.sourceforge.net/project/linuxcommand/TLCL/13.07/TLCL-13.07.pdf

COURSE PREPARATION
You must register at least three full days prior to the event so that we have time to insure you have proper UM credentials for the workshop. This allows enough time for you to get your account adjusted by ITS in case you do not have access to the Linux systems.
If you have questions about this workshop, please send an email to the instructor at kgweiss@umich.edu

Sliding into Slurm: An early look at U-M's new high-performance computing environment
2019-04-25 13:00 - 16:00, 2019-04-17 13:00 - 16:00, 2019-04-03 13:00 - 16:00 | Modern Languages Building (MLB), Room 2001A
This workshop will provide a brief overview of the the new HPC environment and is intended for current Flux and Armis users. We will use the temporary Beta HPC cluster to demonstrate how jobs will be submitted and managed under the new Great Lakes, Armis2, and Lighthouse clusters available later this year.
There are many differences between the familiar Flux environment and that of the new HPC clusters, including a new batch scheduling system, a new interactive batch job environment, a new HPC web portal, a new module environment, and a new on-demand-only job accounting system.
We will cover these differences in the workshop, and provide hands-on training in creating and running job submission scripts in the new HPC environment. Students are expected to be conversant with the Linux command line and have experience in creating, submitting, and troubleshooting PBS batch scripts.

Hands-on session with Python Statsmodels
2019-04-23 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This semester, a series of seven workshops was offered by CSCAR illustrating various approaches to statistical analysis using the Python Statsmodels package. In this session, we will provide an open forum for people to get hands-on assistance in this environment. Several data sets and suggested analysis goals will be provided, but participants are also welcome to come with their own data.
Attendance at previous workshops in this series is not required. The room for this meeting will not have computers, so participants must bring their own laptop. If desired, the instructor can assist with installation of the relevant packages (all of which are free and open source).

MICDE Seminar: Guy A. E. Vandenbosch, Electrical Engineering, KU Leuven
2019-04-17 16:00 - 17:00 | 1008 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Guy A. E. Vandenbosch is a Professor of Electrical Engineering at the Katholieke Universiteit Leuven in Leuven, Belgium. He received the M.S. and Ph.D. degrees in Electrical Engineering from KU Leuven in 1985 and 1991, respectively.
He was a research and teaching assistant from 1985 to 1991 with the Telecommunications and Microwaves section of the Katholieke Universiteit Leuven, where he worked on the modeling of microstrip antennas with the integral equation technique. From 1991 to 1993, he held a postdoctoral research position, in 1993 he became a Lecturer, and since 2005 he’s been a Full Professor at the same university. Guy Vandenbosch has taught or teaches courses on “Electromagnetic Waves”, “Antennas”, “Electromagnetic Compatibility”, “Fundamentals of Communication and Information Theory”, “Electrical Engineering, Electronics, and Electrical Energy”, and ”Digital Steer- and Measuring Techniques in Physics”.
Prof. Vandenbosch’s research interests are in the area of electromagnetic theory, computational electromagnetics, planar antennas and circuits, nano-electromagnetics, EM radiation, EMC, and bio-electromagnetics. His work has been published in over 300 papers in international journals and has led to over 370 papers at international conferences. He was a member of the Management Committees‘ of the consecutive European COST actions on antennas between 1993 and 2017, where he was leading the working group on modeling and software for antennas.
On a Boundary Integral Equation Approach Modeling the Interaction of Light with Nanostructured Metallic Objects
Computational Electromagnetics (CEM) is the technology modeling the interaction of electromagnetic waves (EM waves) with physical objects and their surroundings. This technology has been demonstrated to be a key element in the design of, e.g., modern antennas, waveguiding/shaping devices, etc..It has been playing a pivotal role in forging modern communication systems, and therefore was, is and will be greatly impacting peoples’ daily life. However, despite of all these successes, very recent experiments on the interaction of light (EM waves at optical frequencies) with deep-nanoscale metallic structures suggest the need of a paradigm shift in the classic CEM algorithms, where a more refined material model is required. As the very first step in this direction, we combine the dynamics of classical EM waves with the semi-classical hydrodynamic motion of free electrons in metals. The problem is formulated in the framework of Boundary Integral Equations (BIEs) and subsequently solved by the Method of Moments (MoM) algorithm. This research contributes to potentially bridging the computational gap between the classical macroscopic world and the quantum mechanical microscopic world, and provides an essential tool for chemists and physicists to understand new physics in the nanoscopic world.
Prof. Vandenbosch is being hosted by Prof. Michielssen (Electrical Engineering). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu. If you are a student or postdoc and would like to join him for lunch on Thurs., April 18, please RSVP here by April 16.

Statistical analysis with missing data in Python
2019-04-16 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
Missing data arise in many fields of research, and a large body of statistical tools has been developed to facilitate statistical analysis in the presence of missing data. Here we focus mainly on multiple imputation, which is a broadly-applicable approach for working with missing data. We will illustrate through several case studies how multiple imputation allows certain types of missing data to be rigorously accounted for, while preserving the flexibility to use a variety of familiar statistical tools to account for other aspects of the data.
The analyses presented in this workshop will be performed in Python using the Statsmodels package. All software tools covered in this workshop are free and open source.

Web Scraping with Python
2019-04-16 09:30 - 12:00 | Modern Languages Building (MLB), Room 2001A
This workshop will provide an overview of how to scrape data from html pages and website APIs using Python. This will mostly be accomplished using the requests, beautifulsoup, retry modules and the browser developer tools. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

Open Source GIS and Geometric Network Analysis
2019-04-12 13:30 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This workshop will cover GIS concepts and techniques for analyzing geometric networks embedded in geographical space. We will mainly focus on road network, but the ideas and techniques apply to similar network such as water and electric distribution and gas pipelines. We will primarily use open source tools in R and QGIS, but will also touch upon the functionalities in ArcGIS.
You should know the introductory concepts and tools in GIS and should be familiar with R and QGIS.

The 2019 MICDE Symposium
2019-04-10 08:00 - 17:00 | Michigan League, 911 N. University, Ann Arbor, MI, 48104, United States
The Michigan Institute for Computational Discovery and Engineering 2019 Symposium will feature eminent scientists from around the world and the U-M campus.

SPEAKERS

Marsha Berger
Professor, Computer Science and Mathematics
New York University Courant Institute of Mathematical Sciences

Marisa Eisenberg
Associate Professor, Epidemiology and Mathematics
University of Michigan

Carla Gomes
Professor and Director, Institute for Computational Sustainability
Cornell University

Jan Hesthaven
Dean, School of Basic Sciences
EPFL, Switzerland

Necmiye Ozay
Assistant Professor, Electrical Engineering and Computer Science
University of Michigan

Stephen Wolfram
Founder and CEO, Wolfram Research
Creator of Mathematica

A poster competition will be held, open to post-docs and graduate students.
More information will be posted here as it becomes available. Also see micde.umich.edu/symposium19[/vc_column_text][/vc_column][/vc_row]

Mediation analysis in Python
2019-04-09 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
Mediation analysis is a set of tools for exploring hypotheses about causal pathways, with a special focus on differentiating “direct” from “mediated” associations between an exposure and an outcome. Many approaches to mediation analysis are based on regression analysis.
In this workshop, we will cover some of the basic ideas behind regression-based mediation analysis, and show how this type of analysis can be performed in Python using the Statsmodels package. All software tools covered in this workshop are free and open source.

MICDE Seminar: Prith Banerjee, Chief Technology Officer, ANSYS, Inc.
2019-04-04 12:00 - 13:00 | 1005 EECS, 1301 Beal Ave, Ann Arbor, MI, 48109, United States
Bio: Prith Banerjee is the Chief Technology Officer of ANSYS where he is responsible for leading the evolution of ANSYS’ Technology strategy and champion the company’s next phase of innovation and growth. He also serves on the Board of Directors of Cray, Inc. and Cubic Corporation. Previously he used to be Senior Client Partner at Korn Ferry where he was responsible for IOT and Digital Transformation in the Global Industrial Practice. Formerly, he was Executive Vice President, Chief Technology Officer of Schneider Electric. Previously, he was Managing Director of Global Technology Research and Development at Accenture. Formerly, he was Chief Technology Officer and Executive Vice President of ABB. Earlier, he was Senior Vice President of Research at HP and Director of HP Labs. Formerly, he was Dean of the College of Engineering at the University of Illinois at Chicago. Formerly, he was the Walter P. Murphy Professor and Chairman of Electrical and Computer Engineering at Northwestern University. Prior to that, he was Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. In 2000, he founded AccelChip, a developer of products for electronic design automation, which was acquired by Xilinx Inc. in 2006. During 2005-2011, he was Founder, Chairman and Chief Scientist of BINACHIP Inc., a developer of products in electronic design automation. He was listed in the FastCompany list of 100 top business leaders in 2009. He is a Fellow of the AAAS, ACM and IEEE, and a recipient of the 1996 ASEE Terman Award and the 1987 NSF Presidential Young Investigator Award. He received a B.Tech. in electronics engineering from the Indian Institute of Technology, Kharagpur, and an M.S. and Ph.D. in electrical engineering from the University of Illinois, Urbana.
FUTURE OF SIMULATION-BASED PRODUCT INNOVATION IN THE DIGITAL WORLD
Digital transformation refers to the use of digital technologies such as cloud, IOT, AI/ML, to transform the way business is executed. Digital transformation is impacting every industry – automotive, agriculture, logistics, healthcare and manufacturing. In this talk we will discuss how Digital Transformation is disrupting the manufacturing industry. In the past, engineered products were designed with mechanical and electrical CAD tools, simulated and validated for correctness with CAE tools, prototypes were fabricated and tested, and products were then manufactured at scale in factories. This process required long product cycles often requiring years to build a new product. Today, one can use unlimited computing and storage available from the cloud to do generative design to explore 10,000 design choices in near real-time, verify these products accurately through simulation (eliminating the need to build physical prototypes) and manufacture the products using additive manufacturing and factory automation (Industrie 4.0). In the past, simulation tools were used to model specific physics such as mechanical structures, or fluid dynamics, or electromagnetic interactions by solving second order partial differential equations using numerical methods. Today the simulation tools are being used to solve multi-physics problems (fluid-structure-electromagnetics interactions) at scale using the most complex solvers. These products once built are connected using IOT so that manufacturers have 24/7 connectivity to all these products, and can monitor how customers are using these product; this helps the manufacturers design future generations of products even faster. The connectivity also allows them to monitor the products for failures using predictive analytics, and service these products remotely. In this talk I will discuss how the ANSYS Pervasive Simulation Platform allows hardware and software developers to work together in all phases of a product development lifecycle including Ideation, Design Manufacturing, and Operations. Simulation tools are increasingly being used in the ideation phase by designers to get real-time simulation of the parts as soon as they are being conceptualized. This has resulted in shorter, agile product cycles even for hardware products allowing innovative products to be designed and produced in months and days. Companies are increasingly using model-based systems engineering concepts to take high level requirements of products, and manage the complexity of product design using concepts of Digital Threads, Digital Twins, and Digital Continuity. We will touch upon some future directions of simulation-based product innovation around AI/Machine Learning, Multi-physics Platforms, Hyperscale Simulation, and the convergence of the Digital and Physical worlds using IOT and Augmented Reality/Virtual Reality.

Go for data processing 1/2/3
2019-04-02 14:00 - 16:00, 2019-03-26 14:00 - 16:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This is a three-session workshop on the use of Go for data processing. Go is an open source language developed for general-purpose programming. It is not more difficult to learn and use than a high-level scripting language like Python, but it is strongly typed, statically compiled, and provides native support for concurrency, leading to much better performance for many common tasks. In this series of workshops, we introduce Go as a tool for data processing. No prior exposure to Go is expected, but participants should have some programming background. Free and open source tools for Go are available for all common platforms.
Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.
Session 1: March 19, 2:00pm – 4:00pm
Session 2: March 26, 2:00pm – 4:00pm
Session 3: April 2, 2:00pm – 4:00pm
Note: Interested participants only need to register with the 1st session.

Understanding How the Brain Processes Music Through the Bach Trio Sonatas
2019-04-01 15:30 - 17:00 | Hill Auditorium, 825 N University Ave, Ann Arbor, MI 48109, Ann Arbor, MI, 48104, United States
This event is open to the 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.
