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

 

Interdisciplinary Committee on Organizational Studies (ICOS) Big Data Summer Camp, May 14-18

By | Data, Educational, General Interest, Happenings, News
Social and organizational life are increasingly conducted online through electronic media, from emails to Twitter feed to dating sites to GPS phone tracking. The traces these activities leave behind have acquired the (misleading) title of “big data.” Within a few years, a standard part of graduate training in the social sciences will include a hefty dose of “using of big data,” and we will all be utilizing terms like API and Python.
This year ICOS, MIDAS, and ARC are again offering a one-week “big data summer camp” for doctoral students interested in organizational research, with a combination of detailed examples from researchers; hands-on instruction in Python, SQL, and APIs; and group work to apply these ideas to organizational questions.  Enrollment is free, but students must commit to attending all day for each day of camp, and be willing to work in interdisciplinary groups.

The dates of the camp are all day May 14th-18th.

IOE 899 Seminar Series: Stanley Hamstra, PhD, Milestones Research & Evaluation Accreditation Council for Graduate Medical Education

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Stanley J. Hamstra, PhD

VP, Milestones Research and Evaluation Accreditation Council for Graduate Medical Education

 

“Learning Analytics in Graduate Medical Education: Realizing the Promise of CBME with Milestones Achievement Data”

Abstract: In 2012, the Accreditation Council for Graduate Medical Education (ACGME) introduced the Next Accreditation System (NAS) for improving postgraduate medical education. An important component of the NAS is a shift towards competency-based medical education (CBME), involving milestones as markers of achievement during training. Since 2015, the ACGME has been collecting milestones achievement data (competency ratings) on all resident and fellow physicians in accredited training programs in the USA (n > 110,000 residents and fellows per year). A critical assumption in CBME is that assessment data regarding any learner (in any form) contains some degree of uncertainty. At the same time, program directors must make finite/binary decisions about learners at the time of graduation, and indeed throughout training. The availability of milestones data, in the context of national trends, gives the program director an additional tool for making the best decisions regarding learner progression (and ultimately graduation). I will briefly review tools we have developed to help program directors make use of milestones data to enhance the quality of their decisions regarding resident progression and graduation. In addition, I will outline an approach to using the data for enhancing national curricula within a specialty.

Bio: Dr. Hamstra is responsible for oversight and leadership regarding research in Milestones and assessment systems that inform decisions around resident physician progression and board eligibility. Dr. Hamstra works with medical subspecialty societies, program director organizations, the American Board of Medical Specialties, and specialty certification boards. His research addresses medical education broadly, including competency assessment for residency training programs, and developing administrative support for educational scholarship within academic health settings. Prior to joining the ACGME, Dr. Hamstra was at the University of Michigan, the University of Ottawa, and the University of Toronto Department of Surgery. He has also worked closely with the Royal College of Physicians and Surgeons of Canada on developing policies regarding competency-based medical education for graduate medical education. Dr. Hamstra received his PhD in sensory neuroscience from York University in Toronto in 1994.

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.

CHEPS Seminar: Sung Won Choi, MD, MS, University of Michigan

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Sung Won Choi, MD, MS

Associate Professor, Pediatrics

Inaugural Edith S. Briskin / Shirley K Schlafer Research Professor of Pediatrics

Michigan Medicine

The University of Michigan

 

“Multi-dimensional, Highly Time-resolved Big Data Approach for Disease Prevention”

Abstract: Individualized prediction of disease (and disease‐related events) is a major unmet challenge, yet is essential for realizing the full potential of personalized medicine. Underlying the prediction problem is the fact that disease processes, and the human hosts in which they occur, represent complex dynamical systems comprised of large numbers of components that interact in non‐linear ways over time. A key insight from complexity science is that accurate long‐term prediction in such systems is usually not feasible, but short‐term predictions can be successful if multi‐parameter, highly time‐resolved data can be collected and integrated using computational methods. Complex science indicates that prediction of disease needs to be done on an ongoing basis, in near “real‐time”, because complex dynamical processes tend to proceed non‐linearly. There are “windows of opportunity” when signal begins to exceed background noise and the disease process is early enough for intervention to be successful. Please join Dr. Choi as she discusses how she and her collaborators, including Dr. Wiens (Computer Science/Machine Learning), Dr. Tewari (Medical Oncology), Dr. Kurabayashi (Mechanical Engineering), and Dr. Li (Computational Biology) are using the blood and marrow transplantation setting as an ideal model system to prototype such an approach for disease prediction that is consistent with the highly complex nature of human disease.

Bio: Sung Won Choi, MD, MS trained as a pediatric resident at New York University and later as a fellow in pediatric hematology‐oncology at the University of Michigan. Through an NIH K23 award, Sung received additional training in Clinical Research Design and Statistical Analysis through the University of Michigan School of Public Health. She is currently an Associate Professor in the Department of Pediatrics, and in 2017, she was named the inaugural Edith S. Briskin / Shirley K Schlafer Research Professor of Pediatrics. Sung specializes in the field of blood and marrow transplantation (BMT) and is recognized for her work in translating the use of histone deacetylase inhibition in BMT patients for prevention of a devastating complication known as graft-versus‐host disease (GVHD). She enjoys translatitional research initiatives that include the use of novel, non‐steroidal therapeutics both in the prevention and treatment of GVHD. Her research efforts focus on: 1) providing an improved understanding of clinical BMT through translation of experimental studies 2) exploring clinical outcomes in BMT patients alongside laboratory correlates; and 3) leveraging novel tools, such as information technology, to support patient‐ and caregiver‐centered care in her clinical and translational research efforts in BMT.

The seminar series “Providing Better Healthcare through Systems Engineering” is presented by the U‐M Center for Healthcare Engineering and Patient Safety (CHEPS): Our mission is to improve the safety and quality of healthcare delivery through a multi‐disciplinary, systems‐engineering approach.

The National Academies Webinar Series: Data Science Undergraduate Education

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Webinar

 

REGISTRATION

Webinar Series: Data Science Undergraduate Education

Join the National Academies of Sciences, Engineering, and Medicine for a webinar series on undergraduate data science education. Webinars will take place on Tuesdays from 3-4pm ET starting on September 12 and ending on November 14. See below for the list of dates and themes for each webinar.

This webinar series is part of an input-gathering initiative for a National Academies study on Envisioning the Data Science Discipline: The Undergraduate Perspective. Learn more about the study, read the interim report, and share your thoughts with the committee on the study webpage at nas.edu/EnvisioningDS.

Webinar speakers will be posted as they are confirmed on the webinar series website.

Webinar Dates and Topics

•    9/12/17 – Building Data Acumen
•    9/19/17 – Incorporating Real-World Applications
•    9/26/17 – Faculty Training and Curriculum Development
•    10/3/17 – Communication Skills and Teamwork
•    10/10/17 – Inter-Departmental Collaboration and Institutional Organization
•    10/17/17 – Ethics
•    10/24/17 – Assessment and Evaluation for Data Science Programs
•    11/7/17 – Diversity, Inclusion, and Increasing Participation
•    11/14/17 – Two-Year Colleges and Institutional Partnerships

All webinars take place from 3-4pm ET. You will have the option to register for the entire webinar series or for individual webinars.

The National Academies Webinar Series: Data Science Undergraduate Education

By |

Webinar

 

REGISTRATION

Webinar Series: Data Science Undergraduate Education

Join the National Academies of Sciences, Engineering, and Medicine for a webinar series on undergraduate data science education. Webinars will take place on Tuesdays from 3-4pm ET starting on September 12 and ending on November 14. See below for the list of dates and themes for each webinar.

This webinar series is part of an input-gathering initiative for a National Academies study on Envisioning the Data Science Discipline: The Undergraduate Perspective. Learn more about the study, read the interim report, and share your thoughts with the committee on the study webpage at nas.edu/EnvisioningDS.

Webinar speakers will be posted as they are confirmed on the webinar series website.

Webinar Dates and Topics

•    9/12/17 – Building Data Acumen
•    9/19/17 – Incorporating Real-World Applications
•    9/26/17 – Faculty Training and Curriculum Development
•    10/3/17 – Communication Skills and Teamwork
•    10/10/17 – Inter-Departmental Collaboration and Institutional Organization
•    10/17/17 – Ethics
•    10/24/17 – Assessment and Evaluation for Data Science Programs
•    11/7/17 – Diversity, Inclusion, and Increasing Participation
•    11/14/17 – Two-Year Colleges and Institutional Partnerships

All webinars take place from 3-4pm ET. You will have the option to register for the entire webinar series or for individual webinars.

U-M partners with Cavium on Big Data computing platform

By | Feature, General Interest, Happenings, HPC, News

A new partnership between the University of Michigan and Cavium Inc., a San Jose-based provider of semiconductor products, will create a powerful new Big Data computing cluster available to all U-M researchers.

The $3.5 million ThunderX computing cluster will enable U-M researchers to, for example, process massive amounts of data generated by remote sensors in distributed manufacturing environments, or by test fleets of automated and connected vehicles.

The cluster will run the Hortonworks Data Platform providing Spark, Hadoop MapReduce and other tools for large-scale data processing.

“U-M scientists are conducting groundbreaking research in Big Data already, in areas like connected and automated transportation, learning analytics, precision medicine and social science. This partnership with Cavium will accelerate the pace of data-driven research and opening up new avenues of inquiry,” said Eric Michielssen, U-M associate vice president for advanced research computing and the Louise Ganiard Johnson Professor of Engineering in the Department of Electrical Engineering and Computer Science.

“I know from experience that U-M researchers are capable of amazing discoveries. Cavium is honored to help break new ground in Big Data research at one of the top universities in the world,” said Cavium founder and CEO Syed Ali, who received a master of science in electrical engineering from U-M in 1981.

Cavium Inc. is a leading provider of semiconductor products that enable secure and intelligent processing for enterprise, data center, wired and wireless networking. The new U-M system will use dual socket servers powered by Cavium’s ThunderX ARMv8-A workload optimized processors.

The ThunderX product family is Cavium’s 64-bit ARMv8-A server processor for next generation Data Center and Cloud applications, and features high performance custom cores, single and dual socket configurations, high memory bandwidth and large memory capacity.

Alec Gallimore, the Robert J. Vlasic Dean of Engineering at U-M, said the Cavium partnership represents a milestone in the development of the College of Engineering and the university.

“It is clear that the ability to rapidly gain insights into vast amounts of data is key to the next wave of engineering and science breakthroughs. Without a doubt, the Cavium platform will allow our faculty and researchers to harness the power of Big Data, both in the classroom and in their research,” said Gallimore, who is also the Richard F. and Eleanor A. Towner Professor, an Arthur F. Thurnau Professor, and a professor both of aerospace engineering and of applied physics.

Along with applications in fields like manufacturing and transportation, the platform will enable researchers in the social, health and information sciences to more easily mine large, structured and unstructured datasets. This will eventually allow, for example, researchers to discover correlations between health outcomes and disease outbreaks with information derived from socioeconomic, geospatial and environmental data streams.

U-M and Cavium chose to run the cluster on Hortonworks Data Platform, which is based on open source Apache Hadoop. The ThunderX cluster will deliver high performance computer services for the Hadoop analytics and, ultimately, a total of three petabytes of storage space.

“Hortonworks is excited to be a part of forward-leading research at the University of Michigan exploring low-powered, high-performance computing,” said Nadeem Asghar, vice president and global head of technical alliances at Hortonworks. “We see this as a great opportunity to further expand the platform and segment enablement for Hortonworks and the ARM community.”