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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.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

By | Research

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Published in
Scientific Reports 8, October 2018


Alexandr A. Kalinin, Ari Allyn-Feuer, Alex Ade, Gordon-Victor Fon, Walter Meixner, David Dilworth, Syed S. Husain, Jeffrey R. de Wet, Gerald A. Higgins, Gen Zheng, Amy Creekmore, John W. Wiley, James E. Verdone, Robert W. Veltri, Kenneth J. Pienta, Donald S. Coffey, Brian D. Athey & Ivo D. Dino

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

The effectiveness of parking policies to reduce parking demand pressure and car use

By | Research

This study is a part of the “Reinventing Transportation and Urban Mobility” project, funded by the Michigan Institute for Data Science.

The effectiveness of parking policies to reduce parking demand pressure and car use

Published in
Transport Policy, January 2019


Xiang Yan, Jonathan Levine, Robert Marans

Evaluating the effectiveness of parking policies to relieve parking demand pressure in central areas and to reduce car use requires an investigation of traveler responses to different parking attributes, including the money and time costs associated with parking. Existing parking studies on this topic are inadequate in two ways. First, few studies have modeled parking choice and mode choice simultaneously, thus ignoring the interaction between these two choice realms. Second, existing studies of travel choice behavior have largely focused on the money cost of parking while giving less attention to non-price-related variables such as parking search time and egress time from parking lot to destination. To address these issues, this paper calibrates a joint model of travel mode and parking location choice, using revealed-preference survey data on commuters to the University of Michigan, Ann Arbor, a large university campus. Key policy variables examined include parking cost, parking search time, and egress time. A comparison of elasticity estimates suggested that travelers were very sensitive to changes in egress time, even more so than parking cost, but they were less sensitive to changes in search time. Travelers responded to parking policies primarily by shifting parking locations rather than switching travel mode. Finally, our policy simulation results imply some synergistic effects between policy measures; that is, when pricing and policy measures that reduce search and egress time are combined, they shape parking demand more than the sum of their individual effects if implemented in isolation.

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

By | Research

This research was supported by funding from the Michigan Center for Single-Cell Genomic Data Analytics—a part of the Michigan Institute for Data Science.

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

Published in
Genome Biology, November 12, 2018


Mengie Chen and Xiang Zhou

We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.

TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction

By | Research

MIDAS-affiliated researchers recently published a paper on accurate and fast computational tools for predicting drug synergistic effects.

TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction

Published in
Bioinformatics, November 21, 2018


Hongyang Li, Shuai Hu, Nouri Neamati, Yuanfang Guan



Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs.


Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance.


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.


ARC Director Sharon Broude Geva elected Chair of the Coalition for Academic Scientific Computation

By | HPC, News

Dr. Sharon Broude Geva, Director of Advanced Research Computing at the University of Michigan, has been elected Chair of the Coalition for Academic Scientific Computation (CASC) for 2019.

Founded in 1989, CASC advocates for the use of advanced computing technology to accelerate scientific discovery for national competitiveness, global security, and economic success. The organization’s members represent 87 institutions of higher education and national labs.

The chair position is one of four elected CASC executive officers. The officers work closely as a team with the director of CASC. The Chair is responsible for arranging and presiding over general CASC meetings and acts as an official representative of CASC.

Geva served as CASC secretary in 2015 and 2016, and vice-chair in 2017 and 2018.

The other executive officers for 2019 are Neil Bright, Georgia Institute of Technology, Vice Chair; Craig Stewart, Indiana University, Secretary; Scott Yockel, Harvard University, Treasurer; Rajendra Bose, Columbia University, past chair. Lisa Arafune is CASC Director.


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.


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”