Looking for info on Remote Summer Boot Camp 2: Computing, Data, & Visualization?
Remote Summer Boot Camp Computing, Data, & Visualization
June 8-12, 2020
Here are links to recordings of the sessions:
|June 8||8 am 12 pm||Software Carpentry: Bash/Git|
|June 8||1 pm 5 pm||Software Carpentry: Bash/Git|
|June 9||8 am 12 pm||Software Carpentry: Programming with Python|
|June 9||8 am 12 pm||Software Carpentry: Programming with R|
|June 9||1 pm 5 pm||Software Carpentry: Programming with Python|
|June 9||1 pm 5 pm||Software Carpentry: Programming with R|
|June 10||8 am 10 am||Workshop Intro & Ab Initio Modeling|
|June 10||10 am 12pm||Molecular Dynamics|
|June 10||1 pm 5 pm||MOOSE, Phase Field and Finite Element Modeling|
|June 11||8 am 10 am||INL HPC|
|June 11||10 am 12pm||Parallel Programming Using Python|
|June 11||1 pm 3pm||Machine Learning|
|June 11||3 pm 5 pm||Materials Science Examples and Machine Learning Tools Using Python|
|June 12||9 am 12 pm||Research Panel|
Table of Contents
- Workshop Descriptions and Materials
- For more information
- Presenter Bios
- Organization Committee
Hosted by CAES and C3 (Collaborative Computing Center) in collaboration with the CAES universities: Boise State University, Idaho State University, University of Idaho, and University of Wyoming.
Open to students, faculty, and INL researchers interested in using programming, computational modeling, or data science tools in their research. No prior knowledge of the tools to be presented is needed.
Sessions to include:
- Software carpentry helps researchers get more done in less time. This hands-on workshop will cover fundamental skills needed to reproducibly analyze data, create plots, and manage projects with distributed teams of scientists. Specifically, participants will learn command-line interfaces (bash), version control (git), and one of two workhorse languages (R or python) for numerical computing.
- Computational Modeling and Data Science Tools Introduction aims to educate researchers and students (including experimental and computational modelers) enabling rapid implementation of computational modeling and data science tools in their research. Topics will include ab initio calculations, classical molecular dynamics, phase field modeling, finite element method, and machine learning.
- Research Discussion Panel aims to present, highlight, and discuss opportunities for joint research between BSU, ISU, UI and INL researchers, faculty, and staff. Topics include active research projects, potential joint research projects, researchers who are interested in joint research projects, and sources for funding for joint collaborative research
The following workshops will occur remotely on the dates listed. Please register only for the workshops you plan to attend, and register for as much or as little of the program as your schedule allows.
Please note that the Software Carpentry morning and afternoon sessions are repeat sessions. You will only be able to sign up for one or the other during registration.
|June 8||8 am 12 pm||Eric Jankowski & Mike Henry||Boise State University||Software Carpentry: Bash/Git|
|June 8||1 pm 5 pm||Min Xian & Leo Epstein||University of Idaho||Software Carpentry: Bash/Git|
|June 9||8 am 12 pm||Eric Janowski & Mike Henry||Boise State University||Software Carpentry: Programming with Python|
|June 9||8 am 12 pm||Amanda Culley & Travis Seaborn||University of Idaho||Software Carpentry: Programming with R|
|June 9||1 pm 5 pm||Min Xian & Leo Epstein||University of Idaho||Software Carpentry: Programming with Python|
|June 9||1 pm 5 pm||James Van Leuven & Erich Seamo||University of Idaho||Software Carpentry: Programming with R|
|June 10||8 am 8:10 am
8:10 am 10 am
Boise State University
Workshop Intro & Ab Initio Modeling
|June 10||10 am 12pm||Jagdish Patel||University of Idaho||Molecular Dynamics|
|June 10||1 pm 5 pm||Larry Aagesen & Min Long||Idaho National Laboratory, Boise State University||MOOSE, Phase Field and Finite Element Modeling|
|June 11||8 am 10 am||INL HPC team||Idaho National Laboratory||INL HPC|
|June 11||10 am 12pm||Lawrence Spear||Tao Computational Consulting||Parallel Programming Using Python|
|June 11||1 pm 3pm||Alejandro Strachan||Purdue University||Machine Learning|
|June 11||3 pm 5 pm||Dilpuneet Singh Aidhy||University of Wyoming||Materials Science Examples and Machine Learning Tools Using Python|
|June 12||9 am 12 pm||Facilitator: Steve Cutchin||Boise State University||Research Panel|
Workshop Descriptions and Materials
Go here for workshop materials and software requirements.
Participants must have a laptop/desktop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on, camera and microphone that will be used to interact with instructors and other participants.
Create a nanoHUB account here: The computational modeling and data science tools the workshops will demonstrate are available on the nanoHUB.
Software Carpentry Instructors
Software carpentry instructors will team to demonstrate:
- Bash/Git Develop mental models of command-line interfaces and version control to automate repetitive tasks and manage changes to these scripts for data analysis.
- Python Practice manipulating and plotting arrays of data for visualization and analysis.
- R Practice managing data frames and using them for statistical analysis and plotting.
Eric Jankowski, Boise State University
Eric Jankowski is an Associate Professor in the Micron School of Materials Science and Engineering at Boise State University. Eric has been a Carpentries instructor since 2016 and is passionate about making reproducible scientific computing a pleasant thing to participate in.
Travis Seaborn, University of Idaho
Travis Seaborn is a post-doctoral researcher at University of Idaho in the department of Fish and Wildlife Sciences. Travis is interested in a broad range of ecological modelling and simulation techniques. He loves helping new learners tackle the basics of coding.
Amanda Stahlke, University of Idaho
Amanda Stahlke is a PhD Candidate in the Bioinformatics and Computational Biology Program at the University of Idaho. Her research centers around characterizing the genomic basis of rapid evolution in wild populations. She became a badged Carpentries Instructor in 2019 and has taught or helped with bash, genomics, and geospatial Carpentry workshops.
Clint Elg, University of Idaho
Clint Elg is a PhD Candidate in the Bioinformatics and Computation Biology (BCB) program at the University of Idaho. He is experienced in sequencing bacterial genomes and has authored a software package in Python. He enjoys helping others make connections and learn new things.
James Van Leuven, University of Idaho
James Van Leuven is a Research Assistant Professor in the Department of Biological Sciences and the Institute for Modeling, Collaboration, and Innovation at the University of Idaho. James uses computer programming to study microbial genome evolution and enjoys teaching students bioinformatic and computational skills.
Mike Henry, Boise State University
Mike Henry is a PhD graduate student in the Micron School of Materials Science and Engineering at Boise State University. Mike has been a Carpentry instructor since 2018 and believes that creating welcoming communities around open source scientific software enables great science.
Salvador Chava Castaneda, University of Idaho
Castaneda is a graduate student in the Bioinformatics and Computational Biology program at the University of Idaho. He has been a carpentries instructor since 2019 and is passionate about teaching and about making the process of learning how to code less intimidating and more rewarding.
Amanda Pavlov Culley, Statistical Analyst at ArcherDX
Culley is a statistical analyst at ArcherDX, a genomics company in Boulder, CO, who has been using R extensively for statistical analysis for 8 years. She was recently certified as a Carpentries instructor and enjoys training others to use R.
Breanna Sipley, University of Idaho
Breanna Sipley is a PhD student in PhD student in Bioinformatics and Computational Biology at the University of Idaho. She is grateful for her growing computational toolkit, which, for better or worse, has emboldened her knack for picking hard problems to study and compulsion for making figures unnecessarily beautiful. Eager to help empower others to do and share exciting and reproducible science, Sipley was recently certified as a Software Carpentry instructor and is looking forward to helping out with her fourth workshop.
Lan (Samantha) Li, Boise State University Ab Initio Modeling
Dr. Li is an Associate Professor of Materials Science and Engineering and a leader of Materials Theory and Modeling group at Boise State University in Boise, ID. Her general research interest is multiscale modeling, specifically coupling ab initio to the larger length scale modeling approaches to develop materials with desired properties and performance. Dr. Li finished her doctorate in Nanomaterials at the University of Cambridge in the UK, followed by working in the Bio-Nano Electronic Research Center at Toyo University in Japan. She conducted her research as a post-doc associate on the theoretical and computational studies of metal-fullerene nano-systems, hydrogen-storage materials, and metal oxide thin films at the Department of Physics, University of Florida. She then joined the Center for Materials Informatics at Kent State University in Ohio and worked in collaboration with various national labs and universities on the development of computational materials research code projects and the transformation of these research codes into modules suitable for effective use in undergraduate education. In 2011, she was a senior fellow at the National Institute of Standards and Technology in Gaithersburg, MD, working on energy and sustainability. Dr. Li was awarded American Recovery and Reinvestment Act Program Fellowship by National Institute of Standards and Technology (NIST) in 2011, Young Leader Professional Development Award by the Minerals, Metal and Materials (TMS) society in 2014, William Mong Visiting Research Fellowship in Engineering by University of Hong Kong and Top Ten Scholar Honored Faculty by Boise State University both in 2018. She has served as a former chair of TMS Education Committee, and presently serves as a programing chair for TMS Integrated Computational Materials Engineering (ICME) Committee.
In this tutorial, she will introduce various ab initio modeling tools and how to use the tools to analyze structural stability and electronic properties and simulate chemical reactions.
Jagdish Patel, University of Idaho Molecule Dynamics
Dr. Patel is a research assistant professor in the department of Biological Sciences and affiliated with the Institute for Modeling Collaboration and Innovation at the University of Idaho. He is a molecular modeler and his research experience is in interdisciplinary biomolecular modeling. He has more than eight years of experience performing biomolecular simulations. He uses variety of molecular modeling techniques to study protein-protein interactions, protein-ligand interactions, and sequence-structure-function relationships. He has an experience of dealing with membrane proteins, protein-protein systems, protein-ligand systems, and other soluble proteins in microbes and vertebrates. His lab has interdisciplinary research programs focused on discovering novel anti-viral compounds and studying genome to phenome relationships in opsins, a protein present in the eye.
In this tutorial, you will learn basics of classical MD simulation and you will have hands-on experience of using GROMACS, an open source program for performing MD simulations with an emphasis on proteins.
Larry Aagesen, Idaho National Laboratory MOOSE Phase Field Modeling
Larry Aagesen is a staff scientist at INL and is the lead of the Computational Microstructure Science group. He is one of the developers of Marmot, INL's application for simulating microstructural evolution in nuclear fuels and reactor structural materials, which is based on INL's MOOSE framework. His primary area of expertise is in phase-field modeling, having developed phase-field models for a variety of physical phenomena, including fission gas bubble evolution, solid-state precipitation, solidification and coarsening in metallic alloys and ceramics, and semiconductor growth.
During the session, I will discuss the fundamentals of phase-field modeling and how it is implemented in the open-source MOOSE phase-field module.
Min Long, Boise State University MOOSE Finite Element Modeling
Dr. Min Long is an Assistant Professor in the Department of Computer Science at Boise State University. Prof. Long has extensive experience in scientific computing, numerical methods, data structures and algorithms, data analytics and computational physics. His primary interests in materials are in correlation between material characteristics and microscopic structures of ceramics, alloys, and complex systems. He works on multi-scale modeling of microstructures of core and structural materials, including solidification and irradiation process; FEM analysis using MOOSE framework and its applications of phase-field method code MARMOT, fuel performance code BISON. His area of expertise in computational high-energy physics covers data-driven, large scale computational fluid dynamics simulation of plasmas. He is a DOE visiting faculty and has collaborated with INL scientists in multiple projects. He received his Ph.D. from Cornell University and then worked as a postdoc fellow in computational science in University of Illinois and University of Chicago before joining the Computer Science Department in 2016.
INL HPC Team HPC at INL
Members of the high-performance computing group at Idaho National Laboratory represent system administrators, software consultants, and data scientists with expertise in density functional theory, hydrodynamics, deep learning, storage technology, networks, cybersecurity, and performance modeling. This tutorial will introduce the HPC systems at INL and deep learning, and demonstrate special use cases (like VASP, Natural Language Processing, etc.).
Lawrence Spear, Tao Computational Consulting, LLC Parallel Programming
Lawrence Spear is president of Tao Computational Consulting (C2). Tao C2 is a local company based on Boise, ID. The company specializes in programming, artificial intelligence, machine learning, data management, computational modeling and workforce training. Lawrence received his M.S. and B.S. both in Computer Science at Boise State University. He has over 20 years of development experience in programming, data management, retrieval and content creation. Before founding Tao C2, Lawrence worked as a solutions architect at St. Luke's Health Partners and Blue Cross of Idaho, an infrastructure senior software developer at Scentsy, Inc., and an R&D Senior Engineer at Healthwise, Inc.
In this tutorial, Lawrence will demonstrate different ways of parallel programming using Python, allowing you to leverage multiple processors on a machine to run your job.
Alejandro Strachan, Purdue University Machine Learning
Prof. Alejandro Strachan is a Professor of Materials Engineering at Purdue University and the Deputy Director of NSF's Network for Computational Nanotechnology, home of nanoHUB. Before joining Purdue, he was a Staff Member in the Theoretical Division of Los Alamos National Laboratory and worked as a Postdoctoral Scholar and Scientist at Caltech. He received a Ph.D. in Physics from the University of Buenos Aires, Argentina. Prof. Strachan's research focuses on the development of predictive atomistic and multiscale models to describe materials from first principles and their application to problems of technological importance. His group uses these tools to understand how materials work and use this insight to design new materials combining simulation and experimental results with data science tools. Application areas of interest include: high-energy density and active materials, metallic alloys for high-temperature applications, materials and devices for nanoelectronics and energy, as well as polymers and their composites. Prof. Strachan has published over 150 peer-reviewed scientific papers and his contributions to research have been recognized by the Early Career Faculty Fellow Award from TMS in 2009 and his induction as a Purdue University's Faculty Scholar (2012-2017). His contributions to education have been recognized with the Schuhmann Best Undergraduate Teacher Award from the School of Materials Engineering, Purdue University, in 2007 and 2017.
This hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. Participants will use APIs to query online repositories, organize and process the resulting data, and use it to build predictive. The activities will include building artificial neural networks and random forests, training them with the data acquired and using these models to make decisions. We will exemplify how active learning can be used to reduce the number of experiments required to arrive at a desired design goal. All simulations will be performed using Jupyter notebooks via nanoHUB and will make use of several online data repositories.
Dilpuneet (DP) S. Aidhy, University of Wyoming Machine Learning
Dr. Dilpuneet (DP) S. Aidhy is an assistant professor in the department of mechanical engineering at University of Wyoming. He has expertise in ab initio modeling of materials, molecular dynamics simulations, and data-science. His group specifically focuses on predicting point-defect, microstructure, and mechanical properties in complex multielemental high entropy alloys by learning from simpler alloys using a combination of atomistic and machine-learning methods. His research group also focuses on understanding kinetics and thermodynamics of defects in alloys, radiation effects and microstructure evolution in nuclear materials, defects in complex oxides and 2D materials. He is an editorial board of journal Nature Scientific Reports. He received his Phd from University of Florida.
In the bootcamp, Dr. Aidhy will demonstrate the application of machine learning to predict properties of materials using the data generated from atomistic simulations. This exercise will include building the database, identification of appropriate machine learning models, and prediction of materials properties.
CAES/INL C3 Computational Training Group
Boise State University: Lan Li, Eric Jankowskil, Steven Cutchin, Mendi Edgar
University of Idaho: James VanLeuven, Amanda Culley, Travis Seaborn
Idaho State University: Shannon Kobs Nawotniak
University of Wyoming: Dilpuneet Singh Aidhy
Idaho National Laboratory: Eric Whiting, Ben Nickell
Center for Advanced Energy Studies: Monica S. Dudenhoeffer
If you were an instructor, please complete this survey.
If you were an attendee, please complete this survey.