U-M is seeking an XSEDE Student Champion to provide outreach on campus to help users access the best advanced computing resource that will help them accomplish their research goals, provide training to users on campus, or work on special projects assigned by a mentor.
U-M is offering a new, campus-wide license for MATLAB, Simulink, and companion products. All faculty, researchers, and students are eligible to download and install these products, including toolboxes such as:
- Bioinformatics Toolbox
- Control System Toolbox
- Curve Fitting Toolbox
- Data Acquisition Toolbox
- Image Processing Toolbox
- Instrument Control Toolbox
- Optimization Toolbox
- Parallel Computing Toolbox
- Signal Processing Toolbox
- Simscape Multibody
- Simulink Control Design
- Statistics and Machine Learning Toolbox
- Symbolic Math Toolbox.
Access free, self-paced training to get started in less than 2 hours: MATLAB Onramp.
Commercial use of MathWorks products is not covered by our TAH license, so if you are using a commercial license, please continue to do so.
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.
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.
The Clare Boothe Luce Program of the Henry Luce Foundation has awarded a $270,000 grant to the University of Michigan. The funding will support women PhD students through the Michigan Institute for Computational Discovery and Engineering (MICDE). The program aims to encourage women “to enter, study, graduate and teach” in science, and the funding will support women PhD students who make use of computational science in their research.
“We’re very excited to be able to promote women in scientific computing,” said Mariana Carrasco-Teja, manager of the grant and Associate Director of MICDE. “These resources generously provided by the Clare Boothe Luce program will make a huge difference in the careers of women pursuing computational science at U-M.”
For details on applying, and fellowship requirements, see the fellowship page at micde.umich.edu/academic-programs/cbl/.
The fellowships carry a $35,000 annual stipend and tuition, among other benefits. They will be awarded to students applying for PhD programs in fall 2019 in the College of Engineering, or several programs in the College of Literature, Science and the Arts (Applied and Interdisciplinary Mathematics, Applied Physics, Astronomy, Chemistry, Earth & Environmental Sciences, Mathematics, Physics, and Statistics).
The CBL program at U-M is funded by the Clare Boothe Luce Program of the Henry Luce Foundation, with additional support from the Rackham School of Graduate Studies, the College of Engineering, the College of Literature, Sciences and the Arts, and MICDE.
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.
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”
The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce the 2018-2019 recipients of the MICDE Fellowships for students enrolled in the PhD in Scientific Computing or the Graduate Certificate in Computational Discovery and Engineering. The fellowships, which carry a $4,000 stipend, are meant to augment other sources of funding and are available to students in both programs. See our Fellowship page for more information.
Zhitong Bai, Mechanical Engineering
Kyle Bushick, Materials Science and Engineering
Geunyeong Byeon, Industrial and Operations Engineering
Sehwan Chung, Civil and Environmental Engineering
Khoi Dang, Chemistry
Sicen Du, Materials Science and Engineering
Joseph Hollowed, Physics
Jia Li, Physics
Sabrina Lynch, Biomedical Engineering
Samar Minallah, Climate and Space Sciences and Engineering
Everardo Olide, Applied Physics
Shaowu Pan, Aerospace Engineering
Alicia Petersen, Climate and Space Sciences and Engineering
Vyas Ramasubramani, Chemical Engineering
Fabricio Vasselai, Political Science
Nathan Vaughn, Applied and Interdisciplinary Mathematics
Blair Winograd, Chemistry
Samuel Young, Chemical Engineering
Kexin Zhang, Chemistry
Bu Zhao, School of Environment and Sustainability
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: http://myumi.ch/LzqPq
Instructor: Heather Mayes, Assistant Professor, Chemical Engineering, firstname.lastname@example.org.
The course, instructed by Prof. Raj Rao Nadakuditi (EECS), is an in-depth introduction to computational methods for identifying, fitting, extracting and making sense of patterns in large data sets.
The dates of the camp are all day May 14th-18th.