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:

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

Register for the Webinar.


Click here to join the webinar

Password: data

ARC-TS joins Cloud Native Computing Foundation

By | General Interest, Happenings, News

Advanced Research Computing – Technology Services (ARC-TS) at the University of Michigan has become the first U.S. academic institution to join the Cloud Native Computing Foundation (CNCF), a foundation that advances the development and use of cloud native applications and services. Founded in 2015, CNCF is part of the Linux Foundation.

CNCF announced ARC-TS’s membership at the KubeCon and CloudNativeCon event in Copenhagen. A video of the opening remarks by CNCF Executive Director Dan Kohn can be viewed on the event website.

“Our membership in the CNCF signals our commitment to bringing cloud computing and containers technology to researchers across campus,” said Brock Palen, Director of ARC-TS. “Kubernetes and other CNCF platforms are becoming crucial tools for advanced machine learning, pipelining, and other research methods. We also look forward to bring an academic perspective to the foundation.”

ARC-TS’s membership and participation in the group signals its adoption and commitment to cloud-native technologies and practices. Users of containers and other CNCF services will have access to experts in the field.

Membership gives the U-M research community input into in the continuing development of cloud-native applications, and within CNCF-managed and ancillary projects. U-M is the second academic institution to join the foundation, and the only one in the U.S.

U-M, MIDAS researchers supported by Chan Zuckerberg Initiative

By | General Interest, Happenings, News, Research

Several University of Michigan researchers, including faculty affiliated with MIDAS, recently received support from the Chan Zuckerberg Initiative under its Human Cell Atlas project.

The project seeks to create a shared, open reference atlas of all cells in the healthy human body as a resource for studies of health and disease. The project is funding a variety of software tools and analytic methods. The U-M projects are listed below:

Identifying genetic markers: dimension reduction and feature selection for sparse data
Investigator: Anna Gilbert, Department of Mathematics, MIDAS Core Faculty Member
Description: One of the modalities that scientists participating in the Human Cell Atlas will use to gather data is single cell RNA sequencing (scRNA-seq). The analysis, however, of scRNA-seq data poses novel biological and algorithmic challenges. The data are high dimensional and not necessarily in distinct clusters (indeed, some cell types are exist along a continuum or developmental trajectory). In addition, data values are missing. To analyze this data, we must adjust our dimension reduction algorithms accordingly and either fill in the values or determine quantitatively the impact of the missing values. Furthermore, none of these steps is performed in isolation; they are part of a principled data analysis pipeline. This work will leverage over a decade of modern, sparsity-based machine learning methods and apply them to dimension reduction, marker selection, and data imputation for scRNA-seq data. In one of our two feature selection methods, we adapt a 1-bit compressed sensing algorithm (1CS) introduced by Genzel and Conrad. In order to select markers, the algorithm finds optimal hyperplanes that separate the given clusters of cells and that depend only on a small number of genes. The second method is based on the mutual information (MI) framework developed in. This algorithm greedily builds a set of markers out of a set of statistically significant genes that maximizes information about the target clusters and minimizes redundancy between markers. The imputation algorithms use sparse data models to impute missing values and are tailored to integer counts.

Computational tools for integrating single-cell RNA sequencing studies with genome-wide association studies
Investigator: Xiang Zhou, Biostatistics
Description: Single cell RNA sequencing (scRNAseq) has emerged as a powerful tool in genomics. Unlike previous bulk RNAseq that measures average expression levels across many cells, scRNAseq can measure gene expression at the single cell level. The high resolution of scRNAseq has thus far transformed genomics: scRNAseq has been applied to classify novel cell-subpopulations and states, quantify progressive gene expression, perform spatial mapping, identify differentially expressed genes, and investigate the genetic basis of expression variation. While many computational tools have been developed for analyzing scRNAseq data, tools for effective integrative analysis of scRNAseq with other existing genetic/genomic data types are underdeveloped. Here, we propose to extend our previous integrative methods and develop novel computational tools for integrating scRNAseq data with genome-wide association studies (GWASs). Our proposed tools will identify cell-subpopulations relevant to GWAS diseases or traits, facilitate the interpretation of association results, catalyze more powerful future association studies, and help understand disease etiology and the genetic basis of phenotypic variation. The proposed tools will be applied to integrate summary statistics from various GWASs with fine-scale cell-subpopulations identified from the Human Cell Atlas (HCA) project, to maximize the impact of HCA and facilitate our understanding of the genetic architecture of various human traits and diseases — a question of central importance to human health.

Joint analysis of single cell and bulk RNA data via matrix factorization
Investigator: Clayton Scott, Electrical Engineering and Computer Science, MIDAS Affiliated Faculty
Description: Single cell RNA sequence (ssRNAseq) data is a recently developed platform that enables the measurement of thousands of gene expression levels across individual cells in a tissue sample of interest. The ability to quantify gene expression at the cell level has great potential for advancing our understanding of the cellular processes that characterize a broad range of biological phenomena. However, compared with older bulk RNA technology, which measures expression levels of large numbers of cells in aggregate, ssRNAseq data has higher levels of measurement noise, which complicates its analysis. Furthermore, the problem of inferring cell type from ssRNAseq data is an unsupervised machine learning problem, an already difficult problem even without high measurement noise. To address these issues, we propose a mathematical and algorithmic framework to infer cellular characteristics by analyzing single cell and bulk RNA data simultaneously, via an approach grounded in matrix factorization. The developed algorithms will be evaluated on real data gathered by researchers at the University of Michigan who study breast cancer and spermatogenesis.

Integrating single cell profiles across modalities using manifold alignment
Investigator: Joshua Welch, Computational Medicine and Bioinformatics
Description: Integrating the variation underlying different types of single cell measurements is a critical step toward a comprehensive catalog of human cell types. The ideal approach to construct a cell type atlas would use high-throughput single cell multi-omic profiling to simultaneously measure all cellular modalities of interest within each cell. Although this approach is currently out of reach, it is possible to separately perform high-throughput transcriptomic, epigenomic, and proteomic measurements at the single cell level. Computationally integrating multiple data modalities measured on different individual cells can circumvent the experimental challenges of multi-omic profiling. If different types of single cell measurements are performed on distinct single cells from a common population, each modality will sample a similar set of cells. Matching up similar cells to infer multimodal profiles enables some analyses for which multi-omic profiling is desirable, including multimodal cell type definition and studying covariance among different data types. Manifold alignment is a powerful computational technique for integrating multiple sources of data that describe the same set of events by discovering the common manifold (general geometric shape) that underlies them. Previously, we showed that transcriptomic and epigenomic measurements performed on distinct single cells share underlying sources of variation. We developed a computational method, MATCHER, which uses manifold alignment to integrate cell trajectories constructed from these measurements and infer single cell multi-omic profiles. Here, we will extend this approach to match multimodal single cell profiles sampled from an entire tissue.

Computational methods to enable robust and cost-effective multiplexing of single cell rna-seq experiments in population-scale
Investigator: Hyun Min Kang, Biostatistics
Description: With the advent of single-cell genomic technologies, Human Cell Atlas (HCA) seeks to create a reference maps of each individual cell type and to understand how they develop and maintain their functions, how they interact with each other, and which environmental and/or genetic changes trigger molecular dysfunction that leads to disease. To achieve these goals, it becomes increasingly important to creatively integrate single-cell genomic technologies with novel computational methods to maximize the potential of the new technological advances. Recently, our group has developed a computational tool demuxlet that enable population- scale multiplexing of droplet-based single-cell RNA-seq (dscRNA-seq) experiments. Our approach harnesses natural genetic variation carried within dscRNA-seq reads to multiplex cells from many samples in a single library prep, and statistically deconvolute the sample identity of each barcoded droplet while filtering out multiplets (droplets that contain two or more cells). In this proposal, we aim to further extend our method to increase the accuracy by harnessing cell-specific expression levels, and to eliminate the constraint requiring external genotype data. We will enable application of these methods through production, distribution, and support of efficient, well-documented, open-source software; and test these tools through analysis of simulated data and of real dscRNA-seq data.


MIDAS Health Sciences Challenge Symposium

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Data-intensive health science 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 three Challenge Award teams, and all participants are encouraged to submit posters on data-intensive health science research.


9 am to 12:50 pm: Welcome and Challenge Award Presentations

12:50 pm to 2:15 pm: Lunch, Poster Session and Networking [poster dimensions: up to 6ft wide X 4ft height]

2:15 pm: Panel Discussion: The Future of Data-intensive Health Sciences at U-M.

  • Panelists: Brian Athey (Moderator), Marisa Eisenberg, Jun Li, Brahmajee Nallamothu, Srijan Sen, Kevin Ward

Please register online.  Please submit poster abstracts (< 300 words).  Submission Deadline: April 28.

For questions:

MIDAS Trustworthy Data Science Working Group

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The Michigan Institute for Data Science (MIDAS) is convening a research working group on Trustworthy Data Science.  We had a working group meeting last summer in response to an NSF funding announcement on secure and trustworthy cyberspace, and would like to expand to cover a wider range of research topics under the broad theme of “Trustworthy Data Science”.  This will include research and its application on data security, privacy, data fairness, validity, and sensible applications to policy.  Such topics are essential in data science methodology and tools development, and in many research areas including healthcare, education, business and finance, sustainability, and social sciences.  Our working group welcomes methodologists as well as researchers in any research area who take these issues into consideration.  We hope to create an interdisciplinary forum that will foster innovative ideas and new collaboration.


  1. Introduction
    1. Each participant has 2-3 minutes (based on the number of participants) to describe: a) their research focus and, b) their interest in any aspect of data security, privacy, fairness and validity.
  2. Presentation
    1. Dr. H. V. Jagadish (EECS) will give an overview of these research areas and major issues.
  3. Open discussion on ideas, collaboration and interesting funding opportunities.

Future Plan: Based on the interest of participants, MIDAS will hold regular meetings on Trustworthy Data Science (in the form of chalk talks, discussion of funding announcements, etc.), to foster innovative ideas and collaboration.

Please sign up using the online form.  

For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (ljing@umich.edu734-764-2750).  Please share this announcement with your colleagues who might be interested.

Data for Public Good Symposium

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+ Are you interested in working alongside community partners around data and evaluation?

+ Do you want to learn how to use your data skills for justice?

+ Do you want to connect with students and student organizations who are using data for social good?

Join us for a symposium on April 13th bringing together graduate students, faculty, and staff from across the university discussing effective methods for justice-oriented approaches to community-facing data projects.

Activities Include:
+ Asset Mapping
+ Building muscles for collaboration
+ Skills-sharing
RSVP by April 6th


Women in Data Science: Stanford University, March 5, 2018

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Women in Data Science (WiDS) Conference

and Datathon

Registration for Livestream


The Global Women in Data Science (WiDS) Conference aims to inspire and educate data scientists worldwide, regardless of gender, and support women in the field. This annual one-day technical conference provides an opportunity to hear about the latest data science related research and applications in a broad set of domains, All genders are invited to participate in the conference, which features exclusively female speakers.

Next WiDS Conference: March 5, 2018 at Stanford University & 100+ locations worldwide
WiDS will be held at Stanford university, and at 100+ regional events hosted by WiDS Ambassadorsand available via livestream. The 2018 program will feature fantastic speakers on a broad array of topics ranging from cybersecurity to astrophysics to computational finance, and more. Register now for an event near you.

New for 2018: WiDS Datathon
This year, we’ll be conducting the first-ever WiDS Datathon, a joint effort between Stanford, Kaggle (a Google company), Intuit, InterMedia (a recipient of the Bill & Melinda Gates foundation, and West Big Data Innovation Hub.. The datathon runs from February 1-28, 2018, and winners will be announced at our March 5, 2018, conference at Stanford.

2017 Conference Highlights

  • 75,000+ participants from 75 countries via live stream and Facebook Live, at regional events or online
  • 80+ regional events worldwide from 30 countries, simultaneous or delayed broadcast, many with regional speakers.
  • #WiDS2017 hashtag trended on Twitter all day long
  • WiDS Stanford: 400 attendees from 31 universities and 114 companies and other organizations​, with 1/3 students and 2/3 academics and industry professionals
  • 33 distinguished female speakers, moderators, and panelists

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).