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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. 

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

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:

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

ACNN Big Data Neuroscience Workshop

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BIG DATA NEUROSCIENCE WORKSHOP

Organized by Advanced Computational Neuroscience Network (ACNN)

Registration

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.

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

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Fjola Helgadottir, PhD

Director of AI Therapy

Vancouver CBT Centre

 

Translating Clinical Psychology Treatments into Algorithms: Successes and Challenges

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

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

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

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

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

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

MIDAS Learning Analytics Challenge Symposium

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

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

ASA Symposium on Data Science & Statistics

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SAVE THE DATE FOR SDSS 2018!

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.

Agenda

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.

Panelists:

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

 

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

For questions: midas-research@umich.edu.

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

 

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.

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.

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.

Agenda:

  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 6thhttps://goo.gl/5Bdqke