December 2019 - Affiliates - mailman3.cels.anl.gov

EERE Robotics Internship Program
by Lindgren, Shannon Ann 12 Dec '19

12 Dec '19

Good Morning All- Below is information on DOE’s EERE Robotics Internship Program. Duration: 10-12 weeks Cost: FREE Action: The following information is requested by 9:00 a.m. Monday, December 16. If any staff member is interested, please send the following information below directly to Lisa Reed at lisareed(a)anl.gov<mailto:[email protected]>: * Mentor Name * Mentor Email * Division * Project Description * Information about this project will be made available in Zintellect. Enter a description of the project(s) and activities in which a participant may be involved. Project assignments should provide the participant with opportunities to receive hands-on experience that complements their educational and professional background and helps the participant gain knowledge in areas related robotics. As an educational opportunity, terminology such as participate, collect and analyze, research and collaborate are encouraged. * Academic Discipline(s) Choose the academic discipline(s) that is closely related to the project. These include Computer Science, Engineering, Mathematics & Statistics, Nanotechnology, Other Physical Sciences, or Physics. About the EERE Robotics Internship Program: The Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Advanced Manufacturing Office (AMO) is once again sponsoring internship opportunities for summer 2020. These internships are available for undergraduate and graduate students and recent graduates interested in pursuing degrees and careers in robotics-related science, technology, engineering and mathematics disciplines. The interns will receive a stipend, housing allowance, and an allowance for travel to and from Argonne. These positions are funded by EERE and are no cost to Argonne. By hosting a student, mentors will have the opportunity to introduce students to their research research mission and attract a pool of qualified candidates for future employment/collaboration opportunities. Project information will be made public to applicants via a searchable Project Catalog accessible from Zintellect application. You may submit more than one project. Note that not all projects are guaranteed to be filled. I will submit all requests on behalf of Argonne. In response to the feedback received from last year, AMO has provided the attached guidance on what makes an acceptable project for this program. If you have any questions, please reach out directly to Lisa Reed. Thanks, Shannon & Tamra -- Computing mailing list Computing(a)anl.gov https://lists.cels.anl.gov/mailman/listinfo/computing

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The Argonne Computing Coffee Hour for Today is Canceled
by Werner, Janet M. 11 Dec '19

11 Dec '19

Good Morning, Due to the scheduled maintenance in the Viz Lab the Argonne Computing Coffee Hour for Today is canceled. Janet -- Computing mailing list Computing(a)anl.gov https://lists.cels.anl.gov/mailman/listinfo/computing

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Becca Willett talk on Leveraging Physical Models in Machine Learning
by Martin, David E. 09 Dec '19

09 Dec '19

This Thursday (11/12), Becca Willett will be giving a seminar as part of the Fermilab/UChicago/Argonne computational science speaker series. The goal is to foster collaboration between the three organizations through monthly formal talks followed by informal discussion. Rinku and I are on the organizing committee. Becca is an interesting and dynamic speaker and we're thrilled to have her. Seminar Title: Leveraging Physical Models in Machine Learning Speaker: Rebecca Willett, Professor of Statistics and Computer Science at the University of Chicago Date: Thursday, December 12, 2019 Time: 10:30 AM - 11:30 AM Location: ANL, Bldg. 240, TCS Rm. 1416 Host: David Martin and Rinku Gupta Abstract: Machine learning, at its heart, is the process of learning from examples. However, in many scientific domains, we not only have training data or examples from which to learn, but also physical models of either the data collection mechanism or the underlying physical phenomenon. In this talk, I will describe two settings in which physical models can be incorporated within a machine learning framework to yield improved predictive performance. First, we will consider using training data to help solve ill-posed linear inverse problems such as deblurring, deconvolution, inpainting, compressed sensing, and superresolution. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from training data that can outperform more traditional regularizers. We will see that whether or how a forward model is leveraged can significantly impact how many training samples are needed to achieve a target accuracy. Second, we will examine using a combination of observational data and simulated data to improve subseasonal climate forecasts. Treating both types of data as co-equal training samples can bias many learning methods and yield misleading results. I will describe an alternative framework that combines observational data with a correlation graph that can be estimated from large ensemble climate model outputs, and we will see how this approach leads to more accurate forecasts. Finally, we will discuss open problems and future directions at the intersection of machine learning and the physical sciences. Speaker Bio: Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. Prof. Willett completed her PhD in Electrical and Computer Engineering at Rice University. Prior to joining the University of Chicago, she held the position of Assistant and Associate Professor in the Electrical and Computer Engineering department at Duke University and then the position of Associate professor in the Electrical and Computer Engineering department at the University of Wisconsin-Madison. She has received several awards, including the National Science Foundation CAREER Award and the Air Force Office of Scientific Research Young Investigator Program award. She has also held numerous visiting researcher positions which include the Pure and Applied Mathematics at UCLA, the University of Wisconsin-Madison, the French National Institute for Research in Computer Science and Control (INRIA), and the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare). This seminar will be streamed via https://bluejeans.com/768600859 -- Computing mailing list Computing(a)anl.gov https://lists.cels.anl.gov/mailman/listinfo/computing

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UChicago Computational Social Science Internship Program
by Lindgren, Shannon Ann 09 Dec '19

09 Dec '19

Good Morning All, Below is information on The University of Chicago’s Computational Social Science Internship Program. Duration: 10-12 weeks, dependent on start date, but anticipate internships running mid-June through mid-September. Cost: Hourly rate using the current Research Aide pay scale Action: The following information is requested by Friday, December 13. If any staff member is interested, please send the following information below directly to Lisa Reed at lisareed(a)anl.gov: 1. Brief job description 2. Preferred Qualifications 3. Please indicate technical competencies preferred using the following list: R Python C++ Git Java MATLAB GeoDa HDFS Kafka Storm Cassandra Pig Rift MapReduce SQL C Hadoop ggplot2 D3 Tablau PySAL Excel Spark RapidMinor KNIME skikit-lern Scala Splunk Amazon Machine Learning Dato Dataiku H2O Other (Please List): About the UChicago Computational Social Science Internship Program: The Computational Social Science Internship Program at the University of Chicago was created as a means of developing long-term partnerships between the Masters in Computational Social Science program and participating organizations. At the heart of this program is the desire to provide graduate students with compelling experiences and career opportunities aligned with our research training while allowing UChicago partners to create a pipeline of talent to possibly fill future full-time positions. As a leader in higher education with high standards for intellectual rigor, UChicago has a long tradition of training its graduate students for analytical careers in the public and private sectors. The Masters in Computational Social Science program builds a strong foundation in large data analysis, modeling, and computer programming. Additionally, the program adds the ability to apply cutting edge technologies and statistical techniques to big data sets, magnitudes larger than one normally encounters as a social science investigator. These students are trained to apply strong qualitative and quantitative social science research skills to analyze human behavior and big data across multiple industries. The students seek internships between the first and second year of their MA program and have an average quantitative GRE of 88%, and a full year of UChicago training in computational inference, modeling, simulation, and big data analysis. By the end of their first year, the students will have had serious exposure to R, Python, and the fundamentals of computational inference with big data. Some will have Julia, SQL, STATA, and other programming languages that will be of interest. They will have taken at least 3 courses emphasizing applied research in their field, along with a 4-course core and two electives in math and statistics. If you have any questions, please reach out directly to Lisa Reed. Thanks, Shannon & Tamra -- Computing mailing list Computing(a)anl.gov https://lists.cels.anl.gov/mailman/listinfo/computing

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FW: REMINDER: Argonne Computing Coffee Hour Today in the Viz Lab from 2:00-3:00
by Werner, Janet M. 04 Dec '19

04 Dec '19

Hello, The weekly Argonne Computing Coffee Hours are going well with having presentations each week to facilitate everyone getting to know about each other’s computing activities to enable more collaborations. The coffee hours take place on Wednesdays, 2pm – 3pm in the Viz Lab on the 4th floor, 4312. The speaker for today is Tom Peterka. The title of Tom’s talk is “Enabling Scientific Discovery from Diverse Data Sources Through In Situ Data Management”. Janet -- Computing mailing list Computing(a)anl.gov https://lists.cels.anl.gov/mailman/listinfo/computing

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2019 LDRD Expedition Day for Advanced Computing, this Friday, December 6
by Papka, Michael E. 03 Dec '19

03 Dec '19

You are invited to attend the 2019 LDRD Expedition Day for Advanced Computing, this Friday, December 6, in the TCS Conference Center starting at 9:00 A.M. The PIs of 17 expedition projects will present their results in the areas of quantum, neuromorphic, accelerators for deep learning, and the Aurora exascale computer. Each 20-minute talk includes a Q&A session. This event is sponsored by the CELS Directorate and is open to everyone. Bldg. 240, Rooms 1406/07. _________________________________ Michael E. Papka Deputy Associate Laboratory Director - Computing, Environment and Life Sciences Director - Argonne Leadership Computing Facility papka(a)anl.gov<mailto:[email protected]> Professor of Computer Science Northern Illinois University papka(a)niu.edu<mailto:[email protected]> -- Computing mailing list Computing(a)anl.gov https://lists.cels.anl.gov/mailman/listinfo/computing

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CPS Meeting - Speaker: Alvaro Vazquez Mayagoitia
by CPS Division Office 02 Dec '19

02 Dec '19

Good morning CPS, Today’s speaker, Alvaro Vazquez Mayagoitia, will present a talk titled “Training and validation of a machine learning force field of Hafnia.” December 2, 2019 2:00 pm – 3:00 pm Building 240, Room 4301 [cid:[email protected]]

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