New course for fall 2018: On-Ramp to Data Science for Chemical Engineers

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Description: Engineers are encountering and generating a ever-growing body of data and recognizing the utility of applying data science (DataSci) approaches to extract knowledge from that data. A common barrier to learning DataSci is the stack of prerequisite courses that cannot fit into the typical engineering student schedule. This class will remove this barrier by, in one semester, covering essential foundational concepts that are not part of many engineering disciplines’ core curricula. These include: good programming practices, data structures, linear algebra, numerical methods, algorithms, probability, and statistics. The class’s focus will be on how these topics relate to data science and to provide context for further self-study.

Eligibility: College of Engineering students, pending instructor approval.

More information:

Instructor: Heather Mayes, Assistant Professor, Chemical Engineering,

Communications & Signal Processing Tutorial (CSP)

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Speaker:  Rajesh Sundaresan, Professor, Indian Institute of Science, Department of Electrical Communication Engineering

Title:  “Application of Belief Propagation for Optimization in Structures Spaces

Tutorial Abstract:  The tutorial will provide an overview of the belief propagation (BP) algorithm and its usefulness in addressing random instances of combinatorial optimization problems (matching, edge-cover, the traveling salesman problem, load-balancing, etc.) on locally tree-like graphs. The tutorial will cover the following topics: description of iterative BP algorithms for finding marginals, max-marginals, etc. on a graphical model, issues related to convergence and correctness of the iterations, mapping of random instances of optimization problems as ground states of associated disordered statistical physics systems, cavity equations, their solutions, properties of the ground states, and approaches to establish correctness in locally tree-like settings.

Biography: Rajesh Sundaresan is a Professor in the Department of Electrical Communication Engineering and an Associate Faculty in the Robert Bosch Centre for Cyber-Physical Systems at the Indian Institute of Science. His interests are in communication, computation, and control over networks. He was an associate editor of the IEEE Transactions on Information Theory for the period 2012-2015. He received the B.Tech. degree in electronics and communication from the Indian Institute of Technology Madras, the M.A. and Ph.D. degrees in electrical engineering from Princeton University in 1996 and 1999, respectively. From 1999 to 2005, he worked at Qualcomm Inc. on the design of communication algorithms for wireless modems. Since 2005, he has been with the Indian Institute of Science with brief visiting positions at Qualcomm (2007), Coordinated Sciences Laboratory of the University of Illinois at Urbana-Champaign (2012-13), and the Toulouse Mathematics Institute (2015).


National Academies of Science Webinar: Undergraduate Data Science Education

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The National Academies of Sciences, Engineering, and Medicine (NASEM) is conducting a study on Envisioning the Data Science Discipline: The Undergraduate Perspective. The study committee needs your help in identifying key issues impacting how data science is taught. During the webinar, committee members will discuss the study’s plans and solicit your input on directions and topics the study should examine.

For questions about the webinar, please email Michelle Schwalbe at

Register to attend the webinar.

CoE Endowed Professorship Recognition: Eric Michielssen, The Future of Scientific Computing

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For decades, high-end computer-aided simulations have helped researchers gain new insights into the nature of the physical world. But only relatively recently has computational science developed the ability to quantitatively predict the behavior of physical phenomena, and taken its place next to theory and physical experimentation as the third pillar of scientific inquiry. In this talk, I will explain the mathematical algorithms and computing hardware that fueled this transformation. I will also discuss what the future of scientific computing holds, given the demise of Moore’s law, using computational electromagnetics as an example. Finally, I will argue that U-M is ideally positioned to become a national leader in research computing, giving researchers in its 19 schools and colleges a competitive advantage in their pursuit of engineering, scientific, and medical discoveries.


Eric Michielssen is Professor of Electrical Engineering and Computer Science and Associate Vice President for Advanced Research Computing. He was also the founding director of the Michigan Institute for Computational Discovery and Engineering (MICDE).  Eric is an international leader in the field of computational electromagnetics (CEM), which involves the development and application of computer algorithms to simulate the generation, propagation, and interaction of electromagnetic radiation with matter. He has applied his techniques to the characterization of semiconductor and microelectronic devices, photonic crystals and optical phenomena, aircraft scattering, and terrain detection, to name a few.

Prof. Michielssen’s research on fundamental algorithms is found in the codes and simulations of countless other researchers as well as commercially available simulators. His more than 500 journal and conference publications have been cited more than 10,500 times, with an h-index of 43.  Eric serves as Editor-in-Chief of the International Journal of Numerical Modeling, and served on the National Academy’s Committee on Mathematical Foundations of Uncertainty Quantification, Validation, and Verification. He is an IEEE Fellow.