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Research Opportunity, Mechanical Engineering, TREE Lab – Summer 2019

By | Educational, Research, SC2, SC2 jobs

Dr. Bala Chandran’s Research Group, Mechanical Engineering, TREE Lab

Dr. Bala Chandran is seeking a highly motivated graduate (doctoral or masters) student interested in
doing research in the broad area of understanding radiative heat transfer in granular and
suspension flows via computational modeling for applications of high-temperature
energy storage and catalysis applications. Applicants are expected to have a sound
knowledge of fluid/continuum mechanics and the fundamentals of heat-transfer;
experience in complex fluids or multiphase flows is desirable, though not essential.
Applicants should be interested in the computational aspects of this project to develop
and write code.


  • Strong analytical and computational skills, and intellectual independence (i.e.,
    able to read books and papers and learn by oneself; able to apply theoretical
    knowledge to practical situations)
  • Relevant course work and experience related to
    • Undergraduate level fluid mechanics, solid mechanics, heat transfer,
      radiation, numerical methods and programming, computational fluid/solid
    • Graduate level courses on any/all of the above topics will be a plus point
  • Excellent professional and work ethic
  • Team player that is ready to interface with people developing experiments on
    this project

Application Procedure

If you are interested in this opportunity, please email Prof. Bala Chandran
(rbchan@umich.edu) all the following documents AS SOON AS POSSIBLE:

  1. A 2-page CV with references listed
  2. Unofficial academic transcript
  3. 1 one-page (maximum) statement of interest that explains why you are best suited for working on the proposed research topic and indicates how you meet the required project criteria.
  4. Slides (maximum 5) that showcase your research experience and contributions

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…

MICDE to provide data analysis and dissemination support for $18 million tobacco research center

By | General Interest, Happenings, News, Research

The University of Michigan School of Public Health will house a new, multi-institutional center focusing on modeling and predicting the impact of tobacco regulation, funded with an $18 million federal grant from the National Institutes of Health and the Food and Drug Administration.

The Center for the Assessment of the Public Health Impact of Tobacco Regulations will be part of the NIH and FDA’s Tobacco Centers of Regulatory Science, the centerpiece of an ongoing partnership formed in 2013 to generate critical research that informs the regulation of tobacco products.

The Michigan Institute for Computational Discovery and Engineering (MICDE) will support the center’s Data Analysis and Dissemination core by collecting national and regional survey data, conducting analysis of the use of tobacco products including vaping and e-cigarettes, and disseminate the resulting tobacco modeling parameters to other research centers and the Food and Drug Administration.

The center is led by MICDE affiliated faculty member Rafael Meza, associate professor of Epidemiology, and David Levy, professor of Oncology at Georgetown University.

For more on the center, see the press release from the U-M School of Public Health: https://sph.umich.edu/news/2018posts/tcors-091718.html

U-M part of new software institute on high-energy physics

By | General Interest, Happenings, News, Research

The University of Michigan is part of an NSF-supported 17-university coalition dedicated to creating next-generation computing power to support high-energy physics research.

Led by Princeton University, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) will focus on developing software and expertise to enable a new era of discovery at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland.

Shawn McKee, Research Scientist in the U-M Department of Physics, is a co-PI of the institute. His work will focus on integrating and extending the Open Storage Grid networking activities with similar efforts at the LHC.

For more information, see Princeton’s press release, and the NSF’s announcement.