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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. 

Keynote Speaker:

Xihong Lin
Henry Pickering Walcott Professor of Biostatistics
Chair, Department 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:

  • Veronica Berrocal, Biostatistics
  • Amy Cohn, Industrial Operations and Engineering
  • Liza Levina, Statistics
  • Heather Mayes, Chemical Engineering
  • Bhramar Mukherjee, Biostatistics
  • Maureen Sartor, Computational Medicine and Bioinformatics
  • Rocio Titiunik, Political Science
  • Jenna Wiens, Computer Science and Engineering

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.

Agenda

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

Panelists:

  • 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

U-M, MIDAS researchers supported by Chan Zuckerberg Initiative

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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.

Agenda

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-research@umich.edu.

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

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Women in Data Science (WiDS) Conference

and Datathon

Registration for Livestream

Schedule

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).

MIDAS Working Group: Data Integration

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Data integration is an essential component of data science research in almost all research areas that use heterogeneous data varying in format, dimensionality, quality and granularity.  The examples are endless: multi-omics data integration is increasingly critical in biological research; clinical research benefits greatly from the integration of patient longitudinal data, lab data, sensor data and other types of diagnosis and self-report; environmental monitoring often needs the integration of statistical data, image data and geospatial data; social science research, including education, political science and economics, increasingly integrates social media and other web-based data with traditional survey data…  All the applications encounter similar data science challenges, including idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues.  Our working group welcomes researchers with interest in data integration methodology and its application in any scientific domain.  The Michigan Institute for Data Science (MIDAS) continues to convene a research working group on data integration to create a forum that will foster new ideas and collaborations.

Please RSVP.

Agenda:

  • Introduction
  • Chalk talks
    • Yang Chen (Assistant Professor, Dept. Statistics) will talk about her experience on data integration and some statistical methodology, and seek interests in collaboration.
    • Jamie Estill (staff scientist, HITS) will describe at a high level the capabilities and strength of data virtualization for data integration, using medical research examples, and discuss with the group how data virtualization can facilitate their research.
  • Open discussion on ideas and collaboration.

For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (ljing@umich.edu; 734-764-2750).  Please share this announcement with your colleagues who might be interested.

2017 MIDAS Symposium

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Please join us for the 2017 Michigan Institute for Data Science Symposium.

The keynote speaker will be Cathy O’Neil, mathematician and best-selling author of “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.”

Other speakers include:

  • Nadya Bliss, Director of the Global Security Initiative, Arizona State University
  • Francesca Dominici, Co-Director of the Data Science Initiative and Professor of Biostatistics, Harvard T.H. Chan School of Public Health
  • Daniela Whitten, Associate Professor of Statistics and Biostatistics, University of Washington
  • James Pennebaker, Professor of Psychology, University of Texas

More details are available at: http://midas.umich.edu/2017-symposium/

MIDAS Data Integration Working Group

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The Michigan Institute for Data Science (MIDAS) is convening a research working group on data integration.  Data integration is an essential component of data science research in almost all research areas that use heterogeneous data varying in format, dimensionality, quality and granularity.  The examples are endless: multi-omics data integration is increasingly critical in biological research; clinical research benefits greatly from the integration of patient longitudinal data, lab data, sensor data and other types of diagnosis and self-report; environmental monitoring often needs the integration of statistical data, image data and geospatial data; social science research, including education, political science and economics, increasingly integrates social media and other web-based data with traditional survey data…  All the applications encounter similar data science challenges, including idiosyncratic integration methods, missing data, bias and coverage, consistency and quality control issues.  Our working group welcomes researchers with interest in data integration methodology and its application in any scientific domain.  We hope to create an interdisciplinary forum that will foster new ideas and collaborations.

Agenda:

  • Introduction.  Each participant has a few minutes (based on the number of RSVPs) to present
    • their research background, interest and needs for collaboration, and
    • current projects involving data integration
  • Short chalk talks.  2-3 slots are available for 10-minute presentations if you want to
    • seek the group’s input on methods and discuss roadblocks and/or
    • share useful methods or tools
  • Open discussion on ideas, collaboration and current funding announcements.

Future Plan: Based on the interest of participants, MIDAS will hold regular meetings on data integration (chalk talks, discussion of funding announcements, etc.), and work with the UM Business Engagement Center to bring in industry partnership as needed.

Please RSVP.  For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (ljing@umich.edu; 734-764-2750).  Please share this announcement with your colleagues who might be interested.