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Study on bias in learning analytics earns Brooks Best Full Research Paper Award at LAK conference

By | General Interest, Happenings, News, Research

A paper co-authored by University of Michigan School of Information research assistant professor Christopher Brooks received the Best Full Research Paper Award at the International Conference on Learning Analytics & Knowledge (LAK) Conference in Tempe, Arizona. The award was announced on the final day of the conference, March 7, 2019.

The paper, “Evaluating the Fairness of Predictive Student Models Through Slicing Analysis,” describes a tool designed to test the bias in algorithms used to predict student success.

The goal of the paper, Brooks says, was to evaluate whether the algorithms used to predict whether students would succeed in massive online courses (MOOCs) was skewed by the gender makeup of the classes.

“We were able to find that some have more bias than others do,” says Brooks. “First we were able to show that different MOOCs tend to have different bias in gender representation inside of the MOOCs.”

Read more…

Balzano wins NSF CAREER award for research on machine learning and big data involving physical, biological and social phenomena

By | General Interest, Happenings, News, Research

Prof. Laura Balzano received an NSF CAREER award to support research that aims to improve the use of machine learning in big data problems involving elaborate physical, biological, and social phenomena. The project, called “Robust, Interpretable, and Efficient Unsupervised Learning with K-set Clustering,” is expected to have broad applicability in data science.

Modern machine learning techniques aim to design models and algorithms that allow computers to learn efficiently from vast amounts of previously unexplored data, says Balzano. Typically the data is broken down in one of two ways. Dimensionality-reduction uses an algorithm to break down high-dimensional data into low-dimensional structure that is most relevant to the problem being solved. Clustering, on the other hand, attempts to group pieces of data into meaningful clusters of information.

However, explains Balzano, “as increasingly higher-dimensional data are collected about progressively more elaborate physical, biological, and social phenomena, algorithms that aim at both dimensionality reduction and clustering are often highly applicable, yet hard to find.”

Balzano plans to develop techniques that combine the two key approaches used in machine learning to decipher data, while being applicable to data that is considered “messy.” Messy data is data that has missing elements, may be somewhat corrupted, or is filled heterogeneous information – in other words, it describes most data sets in today’s world.

Balzano is an affiliated faculty member of both the Michigan Institute for Data Science (MIDAS) and the Michigan Institute for Computational Discovery and Engineering (MICDE). She is part of a MIDAS-supported research team working on single-cell genomic data analysis.

Read more about the NSF CAREER award…

MIDAS adds Associate Directors to boost campus engagement

By | General Interest, Happenings, News

The Michigan Institute for Data Science (MIDAS) has added two Associate Directors who will help increase outreach to all academic units at the University of Michigan.

  • Pamela Davis-Kean, Professor of Psychology and Research Professor at the Institute for Social Research, will be the new MIDAS Associate Director for Humanities and Social Sciences.
  • Kayvan Najarian, Professor of Computational Medicine and Bioinformatics and Emergency Medicine, will be the new MIDAS Associate Director for Health Sciences.
  • Ivo Dinov, Professor of Health Behavior and Biological Science, will continue as the MIDAS Associate Director for Education and Training.
  • H.V. Jagadish, Professor of Electrical Engineering and Computer Science, and the recently appointed Director of MIDAS, will lead outreach efforts for Engineering and the Physical Sciences.

“The goal is for each associate director to engage with corresponding parts of the University,” said Prof. Jagadish. “At times, that will mean simply being a primary point of contact for researchers engaged in data-driven science. But it will also entail developing data science activities or programs of particular interest to researchers in their respective parts of campus.”

Davis-Kean and Najarian will take their positions on March 1, 2019.

MIDAS was established in 2015 as part of the university-wide Data Science Initiative to promote interdisciplinary collaboration in data science and education. The institute has built a cohort of more than 200 affiliated faculty members who span all three U-M campuses. Institute funding has catalyzed several multidisciplinary research projects, many of which have generated significant external funding. MIDAS also plays a key role in establishing new educational opportunities, such as the graduate certificate in data science, and provides additional support for student groups, including one team that used data science to help address the Flint water crisis.

H.V. Jagadish appointed director of MIDAS

By | General Interest, Happenings, News

H.V. Jagadish has been appointed director of the Michigan Institute for Data Science (MIDAS), effective February 15, 2019.

Jagadish, the Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan, was one of the initiators of an earlier concept of a data science initiative on campus. With support from all academic units and the Institute for Social Research, the Office of the Provost and Office of the Vice President for Research, MIDAS was established in 2015 as part of the university-wide Data Science Initiative to promote interdisciplinary collaboration in data science and education.

“I have a longstanding passion for data science, and I understand its importance in addressing a variety of important societal issues,” Jagadish said. “As the focal point for data science research at Michigan, I am thrilled to help lead MIDAS into its next stage and further expand our data science efforts across disciplines.”

Jagadish replaces MIDAS co-directors Brian Athey and Alfred Hero, who completed their leadership appointments in December 2018.

“Professor Jagadish is a leader in the field of data science, and over the past two decades, he has exhibited national and international leadership in this area,” said S. Jack Hu, U-M vice president for research. “His leadership will help continue the advancement of data science methodologies and the application of data science in research in all disciplines.”

MIDAS has built a cohort of 26 active core faculty members and more than 200 affiliated faculty members who span all three U-M campuses. Institute funding has catalyzed several multidisciplinary research projects in health, transportation, learning analytics, social sciences and the arts, many of which have generated significant external funding. MIDAS also plays a key role in establishing new educational opportunities, such as the graduate certificate in data science, and provides additional support for student groups, including one team that used data science to help address the Flint water crisis.

As director, Jagadish aims to expand the institute’s research focus and strengthen its partnerships with industry.

“The number of academic fields taking advantage of data science techniques and tools has been growing dramatically,” Jagadish said. “Over the next several years, MIDAS will continue to leverage the university’s strengths in data science methodologies to advance research in a wide array of fields, including the humanities and social sciences.”

Jagadish joined U-M in 1999. He previously led the Database Research Department at AT&T Labs.

His research, which focuses on information management, has resulted in more than 200 journal articles and 37 patents. Jagadish is a fellow of the Association for Computing Machinery and the American Association for the Advancement of Science, and he served nine years on the Computing Research Association board.

Most CSCAR workshops will be free for the U-M community starting in January 2019

By | Educational, General Interest, Happenings, News

Beginning in January of 2019, most of CSCAR’s workshops will be offered free of charge to UM students, faculty, and staff.

CSCAR is able to do this thanks to funding from UM’s Data Science Initiative.  Registration for CSCAR workshops is still required, and seats are limited.

CSCAR requests that participants please cancel their registration if they decide not to attend a workshop for which they have previously registered.

Note that a small number of workshops hosted by CSCAR but taught by non-CSCAR personnel will continue to have a fee, and fees will continue to apply for people who are not UM students, faculty or staff.

Eric Michielssen completes term as Associate Vice President for Research – Advanced Research Computing

By | General Interest, Happenings, News

Eric Michielssen will step down from his position as Associate Vice President for Research – Advanced Research Computing on December 31, 2018, after serving in that leadership role for almost six years. Dr. Michielssen will return to his faculty role in the Department of Electrical Engineering and Computer Science in the College of Engineering.

Under his leadership, Advanced Research Computing has helped empower computational discovery through the Michigan Institute for Computational Discovery and Engineering (MICDE), the Michigan Institute for Data Science (MIDAS), Advanced Research Computing-Technology Services (ARC-TS) and Consulting for Statistics, Computing and Analytics Research (CSCAR).

In 2015, Eric helped launch the university’s $100 million Data Science initiative, which enhances opportunities for researchers across campus to tap into the enormous potential of big data. He also serves as co-director of the university’s Precision Health initiative, launched last year to harness campus-wide research aimed at finding personalized solutions to improve the health and wellness of individuals and communities.

The Office of Research will convene a group to assess the University’s current and emerging needs in the area of research computing and how best to address them.

U-M approves new graduate certificate in computational neuroscience

By | Educational, General Interest, Happenings, News

The new Graduate Certificate in Computational Neuroscience will help bridge the gap between experimentally focused studies and quantitative modeling and analysis, giving graduate students a chance to broaden their skill sets in the diversifying field of brain science.

“The broad, practical training provided in this certificate program will help prepare both quantitatively focused and lab-based students for the increasingly cross-disciplinary job market in neuroscience,” said Victoria Booth, Professor of Mathematics and Associate Professor of Anesthesiology, who will oversee the program.

To earn the certificate, students will be required to take core computational neuroscience courses and cross-disciplinary courses outside of their home departments; participate in a specialized interdisciplinary journal club; and complete a practicum.

Cross-discplinary courses will depend on a student’s focus: students in experimental neuroscience programs will take quantitative coursework, and students in quantitative science programs such as physics, biophysics, mathematics and engineering will take neuroscience coursework.

The certificate was approved this fall, and will be jointly administered by the Neuroscience Graduate Program (NGP) and the Michigan Institute for Computational Discovery and Engineering (MICDE).

For more information, visit micde.umich.edu/comput-neuro-certificate. Enrollment is not yet open, but information sessions will be scheduled early next year. Please register for the program’s mailing list if you’re interested.

Along with the Graduate Certificate in Computational Neuroscience, U-M offers several other graduate programs aimed at training students in computational and data-intensive science, including:

  • The Graduate Certificate in Computational Discovery and Engineering, which is focused on quantitative and computing techniques that can be applied broadly to all sciences.
  • The Graduate Certificate in Data Science, which specializes in statistical and computational methods required to analyze large data sets.
  • The Ph.D in Scientific Computing, intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This degree is awarded jointly with an existing program, so that a student receives, for example, a Ph.D in Aerospace engineering and Scientific Computing.

 

U-M participates in SC18 conference in Dallas

By | General Interest, Happenings, News

University of Michigan researchers and IT staff wrapped up a successful Supercomputing ‘18 (SC18) in Dallas from Nov. 11-16, 2018, taking part in a number of different aspects of the conference.

SC “Perennial” Quentin Stout, U-M professor of Electrical Engineering and Computer Science and one of only 19 people who have been to every Supercomputing conference, co-presented a tutorial titled Parallel Computing 101.

And with the recent announcement of a new HPC cluster on campus called Great Lakes, IT staff from Advanced Research Computing – Technology Services (ARC-TS) made presentations around the conference on the details of the new supercomputer.

U-M once again shared a booth with Michigan State University booth, highlighting our computational and data-intensive research as well as the comprehensive set of tools and services we provide to our researchers. Representatives from all ARC units were at the booth: ARC-TS, the Michigan Institute for Data Science (MIDAS), the Michigan Institute for Computational Discovery and Engineering (MICDE), and Consulting for Statistics, Computing and Analytics Research (CSCAR).

The booth also featured two demonstrations: one on the Open Storage Research Infrastructure or OSiRIS, the multi-institutional software-defined data storage system, and the Services Layer At The Edge (SLATE) project, both of which are supported by the NSF; the other tested conference-goers’ ability to detect “fake news” stories compared to an artificial intelligence system created by researchers supported by MIDAS.

Gallery

U-M Activities

  • Tutorial: Parallel Computing 101: Prof. Stout and Associate Professor Christiane Jablonowski of the U-M Department of Climate and Space Sciences and Engineering provided a comprehensive overview of parallel computing.
  • Introduction to Kubernetes. Presented by Bob Killen, Research Cloud Administrator, and Scott Paschke, Research Cloud Solutions Designer, both from ARC-TS. Containers have shifted the way applications are packaged and delivered. Their use in data science and machine learning is skyrocketing with the beneficial side effect of enabling reproducible research. This rise in use has necessitated the need to explore and adopt better container-centric orchestration tools. Of these tools, Kubernetes – an open-source container platform born within Google — has become the de facto standard. This half-day tutorial introduced researchers and sys admins who may already be familiar with container concepts to the architecture and fundamental concepts of Kubernetes. Attendees explored these concepts through a series of hands-on exercises and left with the leg-up in continuing their container education, and gained a better understanding of how Kubernetes may be used for research applications.
  • Brock Palen, Director of ARC-TS, spoke about the new Great Lakes HPC cluster:
    • DDN booth (3123)
    • Mellanox booth (3207)
    • Dell booth (3218)
    • SLURM booth (1242)
  • Todd Raeker, Research Technology Consultant for ARC-TS, went to the Globus booth (4201) to talk about U-M researchers’ use of the service.
  • Birds of a Feather: Meeting HPC Container Challenges as a Community. Bob Killen, Research Cloud Administrator at ARC-TS, gave a lightning talk as part of this session that presented, prioritized, and gathered input on top issues and budding solutions around containerization of HPC applications.
  • Sharon Broude Geva, Director of ARC, was live on the SC18 News Desk discussing ARC HPC services, Women in HPC, and the Coalition for Scientific Academic Computation (CASC). The stream was available from the Supercomputing Twitter account: https://twitter.com/Supercomputing
  • Birds of a Feather: Ceph Applications in HPC Environments: Ben Meekhof, HPC Storage Administrator at ARC-TS, gave a lightning talk on Ceph and OSiRIS as part of this session. More details at https://www.msi.umn.edu/ceph-hpc-environments-sc18
  • ARC was a sponsor of the Women in HPC Reception. See the event description for more details and to register. Sharon Broude Geva, Director of ARC, gave a presentation.
  • Birds of a Feather: Cloud Infrastructure Solutions to Run HPC Workloads: Bob Killen, Research Cloud Administrator at ARC-TS, presented at this session aimed at architects, administrators, software engineers, and scientists interested in designing and deploying cloud infrastructure solutions such as OpenStack, Docker, Charliecloud, Singularity, Kubernetes, and Mesos.
  • Jing Liu of the Michigan Institute for Data Science, participated in a panel discussion at the Purdue University booth.

Follow ARC on Twitter at https://twitter.com/ARC_UM for updates.

MIDAS announces winners of 2018 poster competition

By | Educational, General Interest, Happenings, Research

The Michigan Institute for Data Science (MIDAS) is pleased to announce the winners of its 2018 poster competition, which is held in conjunction with the MIDAS annual symposium.

The symposium was held on Oct. 9-10, 2018, and the student poster competition had more than 60 entries. The winners, judged by a panel of faculty members, received cash prizes.

Best Overall

Arthur Endsley, “Comparing and timing business cycles and land development trends in U.S. metropolitan housing markets”

Most likely health impact

  • Yehu Chen, Yingsi Jian, Qiucheng Wu, Yichen Yang, “Compressive Big Data Analytics – CBDA: Applications to Biomedical and Health Studies”
  • Jinghui Liu, “An Information Retrieval System with an Iterative Pattern for TREC Precision Medicine”

Most likely transformative science impact

  • Prashant Rajaram, “Bingeability and Ad Tolerance: New Metrics for the Streaming Media Age”
  • Mike Ion, “Learning About the Norms of Teaching Practice: How Can Machine Learning Help Analyze Teachers’ Reactions to Scenarios?”

Most interesting methodological advancement

  • Nina Zhou and Qiucheng Wu, “DataSifter: Statistical Obfuscation of Electronic Health Records and Other Sensitive Datasets”
  • Aniket Deshmukh, “Simple Regret Minimization for Contextual Bandits”

Most likely societal impact

  • Ece Sanci, “Optimization of Food Pantry Locations to Address Food Scarcity in Toledo, OH”
  • Rohail Syed, “Human Perception of Surprise: A User Study”

Most innovative use of data

  • Lan Luo, “Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Datasets”
  • Danaja  Maldeniya, “Psychological Response of Communities affected by Natural Disasters in Social Media”