Andrew Younge is a Computer Scientist at Sandia National Laboratories with the Scalable System Software group. His research interests include High Performance Computing, Virtualization, Distributed Systems, and energy efficient computing. The central focal point of Andrew’s work is to improve the usability and efficiency of supercomputing system software. He has a Ph.D in Computer Science from Indiana University, where he was the Persistent Systems fellow and a member of the FutureGrid project, an NSF-funded experimental Cloud testbed. Over the years, Andrew has held visiting positions at the MITRE Corporation, the University of Southern California / Information Sciences Institute, and the University of Maryland, College Park. He received his Bachelors and Masters of Science from the Computer Science Department at Rochester Institute of Technology (RIT) in 2008 and 2010, respectively.
CTO Rob Nagler has designed and deployed leading-edge distributed systems for over 30 years. Rob has written extensively about software, including his book on agile development methods, Extreme Perl. Rob started his career in the Distributed Systems Group at Stanford working with Dr. David Cheriton. At Sun Microsystems, he worked with Eric Schmidt in the telecommunications group. He led OLAP development at Olsen & Associates, a pioneer in financial research and analysis. At Tandem Computers(tm) High Performance Research Center, Rob implemented a multi-platform, distributed architecture based on CORBA and used for internet traffic routing for mobile phone networks. Rob holds a BS in Computer Engineering from the University of California, San Diego and an MS in Computer Engineering from Stanford University.
Michael Riera received his master degree in Computer Engineering at the University of Central Florida. He currently is pursuing a PhD in Computer Engineering at Arizona State University. His main focus at ASU is on heterogeneous multimode accelerator architectures. He is currently looking into making accelerators and accelerator clusters more accessible.
BS in Planetary Science, Caltech, 2011; BS in Environmental Science and Engineering, Caltech, 2011; MS in Atmospheric Science, MIT, 2014; PhD in Climate Physics and Chemistry, MIT, 2016; Chief Scientist at ACME AtronOmatic 2016-present
In the last decade, Machine Learning has experienced a dramatic increase of performance. This has corresponded to an understandable hype especially for the remarkable results achieved in some cases.
Practitioners of Scientific Disciplines have become interested in utilizing new Machine Learning techniques, and have sometimes started doing so with mixed success.
In this talk I will briefly describe some of the common Traps, Pitfalls and Misconceptions of Machine Learning as relevant to the Scientific Discipline, and especially how to avoid them.
Speaker Description:
Davide Del Vento is Software Engineer at National Center for Atmospheric Research, where he has worked since 2008 as a High Performance Computing Specialist. In this role, he provides assistance to the UCAR computing community of scientists and programmers on a variety of topics, including optimization and tuning, parallel computing, data analysis and debugging. He contributes to design, development, testing and maintenance of local software packages. Davide also serves as a Software Engineer for the XSEDE Novel and Innovative Projects (NIP) group and for the XSEDE Extended Collaborative Support Services (ECSS) program. In 2018 he was chosen to be XSEDE Campus Champion for NCAR.Moreover, Davide is the Chairman of the Software Engineering Assembly since 2010.Earlier in his career he worked as a member of the control team for the Visible InfraRed Thermal Imaging Spectrometer onboard the Venus Express mission from the European Space Agency. The mission was proposed in 2001, launched in 2005 and operational around the planet Venus from 2006 to 2014.Davide has a MS in Physics from University of Rome Tor Vergata and a Ph.D. in Physics from University of Rome Tre respectively.
Tyler McCandless is a Machine Learning Scientist in the Research Applications Laboratory at the National Center for Atmospheric Research in Boulder, Colorado. He earned three degrees in Meteorology from Penn State University: PhD - 2015, MS - 2010 and BS - 2010. After earning his doctorate, Tyler worked in the private sector until May 2018, with his previous position as Manager of Services at Ascend Analytics, which is a energy risk modeling software company in Boulder. At NCAR, Tyler is responsible for providing high level machine learning expertise to various projects, including using machine learning techniques to improve wind and solar power prediction for a project in Kuwait, and building an algorithm to improve wildfire prediction with gridded fuel moisture content estimates.
Maggie has been working as a Software Engineer for the University Corporation for Atmospheric Research (UCAR) since 2002. As part of her duties, she oversees data transfer and processing operations for Radio Occultation satellite missions such as the COSMIC-1 mission launched in 2006. Maggie holds a Bachelor of Science in Computer Science, as well as a Master of Science in Data Science from Regis University. She also holds a Bachelor of Arts and a Master of Music degrees, and she enjoys playing the violin with her Gypsy Jazz quartet.
Jonathan Edelen is an accelerator physicist with a broad range of experience across the field. Currently, He is working on the development of symplectic space charge algorithms, novel symplectic algorithms for phase space deposition, machine learning for modeling and control of particle accelerators, and the application of nonlinear optimization methods to accelerator technology such as thermionic energy converters.
Christina comes from a math background and got her undergrad degree at CU Boulder in Applied Math. She then went to University of Miami FL to get her masters in Meteorology and Oceanography. She enjoys taking photos while perusing her hobbies of cooking and baking as well as being outside where she can be found hiking or racing triathlons. This sparked her interest in weather and feeds her drive to make weather forecasts better with improving how we handle big data.
