Cal State Channel Islands is part of the growing list of AWS Academy institutions, a group of mostly colleges that select at least one instructor to be authorized by the cloud computing giant to teach its courses to students.
Wilwood Engineering, The Leader in Aftermarket High Performance Disc Brake Systems is looking for a detail oriented individual for our Systems Automation department.
Responsibilities will include:
- Software development on PLC control systems
- Design advanced electronic control systems for mechanical systems
- Process data interpretation and presentation of key parameters
- Machine and process automation
- Maintain process Historian
- Provide technical support for onsite control systems
- Assist with the implementation of manufacturing systems
- Ensure system documentation is maintained up to date
- Upgrade the design of existing devices by adding automation
- Create test programs
- Design, engineer, test and troubleshoot control systems
- Ability to work independently while applying the available resources
- Experience or knowledge of DAQ modules, signal processing and software/system integration
- Ability to work with a multi-disciplinary team
- Programming in C++, & C#
- Autodesk Eagle
Wilwood Engineering is an Equal Opportunity Employer (EOE). All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, or national origin.
Information Security and Compliance Manager
The Information Security & Compliance Manager will help establish, manage, and maintaining the Global Information Security & Compliance Management Program encompassing information security, regulatory compliance and data privacy. They are responsible for the development, establishment, and communication of security policies, standards, guidelines, and the education and awareness of these requirements. They will also be responsible for identifying, tracking and reporting on information security and compliance risk and ensuring that information system controls and monitoring systems operate effectively. Prevent, detect and respond to cyber-criminal threats and other risks to corporate information (IT) and operational technologies. If you are interested in applying for this position, please send your resume to Jenna Colitti at firstname.lastname@example.org.
This year I am teaching (online) a sequence of 4 courses in Cloud Computing, in conjunction with the AWS Academy. Students receive AWS accounts, explore AWS services with hands-on labs, and prepare for certification (if they wish to). All classes are open to the public, and can be joined independently of each other (or all taken in sequence!). Please contact
email@example.com to book an information session meeting on Zoom.
We are very happy to have been selected for an SageMaker Pilot for AWS Educate Classrooms! Machine Learning (ML) is a top hard skill for graduates, and it is also becoming a premier tool for research in all areas. SageMaker Studio is a complete development environment for ML.
The theory of ML can always be taught, but in order to have hands on experience with ML, a computing infrastructure is required that is beyond the means of most educational institutions. Our students will have access to AWS Educate accounts with credits to use the SageMaker Studio environment, and access to to powerful CPU/GPU resources (
ml.g4dn.xlarge) for training ML models.
ML use cases include SPAM filtering for emails, recommender systems, e.g., Netflix show recommendations, and uncovering credit card fraud. There are three types of ML: supervised, where the data is labeled and the expected outputs are well understood (is an, is this email SPAM or not); unsupervised, where the ML algorithm has to discover the salient properties of the data; and, reinforcement, where some agent (e.g., RoboMaker) interacts with an environment and learns to navigate it through a system of rewards.
SageMaker supports many leading deep learning frameworks, including: TensorFlow, PyTorch, Apache MXNet, Chainer, Keras, Gluon, Horovod, Scikit-learn, and Deep Graph Library.
We applied last July to be part of the AWS pilot program to make SageMaker available to our students, and we were approved to start this fall 2020. We have a group of about 10 students who are going to be learning to use under my supervision.
We are building on our growing expertise in Artificial Intelligence. This fall term, professor Reza Abdolee is teaching a graduate class in AI (COMP569) and professor Bahareh Abbasi is teaching both an undergraduate course in AI (COMP469) and a graduate class in Neural Networks (COMP572).
Students will learn a variety of auxiliary tools; as you will see from this list, the Python programming language is central to Data Analytics:
- Jupyter Notebook and Jupyter Lab: an open-source web application that allows the creation and sharing of documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, etc.
- Pandas: a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
- Seaborn: a library for making statistical graphics in Python. It is built on top of Matplotlib and closely integrated with Pandas data structures.
- Scikit-learn: a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms.
- Matplotlib: a comprehensive library for creating static, animated, and interactive visualizations in Python.
- NumPy: a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
- PyTorch (AWS testimonials): an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab.
In the words of Jose Cahue:
One of the major hurdles to learn ML as a student is having access to a machine optimized for model training. Cloud computing can be one practical solution to provide the computation resources needed to learn ML.Jose Cahue
Today I participated in Webinars and Training Sessions for Educators and Students, which is a follow-up to the popular April AWS workshop on Remote Learning.
From the AWS page supporting the rapid transition to Remote Learning:
With the global move from in-classroom delivery to remote learning due to temporary and sustained school closings, AWS Educate wants to help educators and students with webinars and workshops ranging from beginner to advanced levels. Any educator or student is invited to join, and there’s no cost for participating. Each webinar will be recorded and available on-demand in over 100 languages.https://aws.amazon.com/education/remote-instruction-resources-for-educators/
In May 2018, Ryan McIntyre defended his masters thesis (at CSUCI under my supervision) on Bounding the size of minimal clique covers. We followed up with a publication of the results in the Journal of Discrete Algorithms (https://doi.org/10.1016/j.jda.2018.03.002) [post], and now, two years later, our results are cited and built upon in an interesting paper Static beam placement and frequency plan algorithms for LEO constellations (https://doi.org/10.1002/sat.1345) written by an Astronautics research group at MIT.
What is interesting about this is the serendipitous manner in which results build on each other: our result consisted in a partial solution to an original problem in combinatorics posed by the itinerant mathematician Paul Ërdos (posed in the mid 1960s), which we then used to partially solve a problem related to string indeterminates (also in this case working on previous results of Joel Helling [post]), which are related to genetics. Now, our work is being used to solve the problem of satellite allocation.
We hope you will take the opportunity to announce the NSF Graduate Research Fellowship Program (GRFP). Interested students should begin at the applicant information page http://www.nsfgrfp.org . The GRFP supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based master’s and doctoral degrees at accredited United States institutions. The program provides up to three years of graduate education support, including an annual $34,000 stipend. Applications for Mathematical Sciences topics are due October 22, 2020.
US citizens and permanent residents who are planning to enter graduate school in fall 2021 are eligible (as are those in the first two years of such a graduate program, or who are returning to graduate school after being out for two or more years). The program solicitation NSF 20-587 (http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=6201 ) contains full details.
The GRFP awards more than 1,500 new fellowships each year. In the years 2013 through 2020, GRFP awards in the mathematical sciences have been given to more than 660 students who earned baccalaureate degrees from approximately 200 colleges and universities throughout the US. The number of baccalaureate institutions has been growing through the years.
The GRFP also needs qualified faculty reviewers. Review panels are conducted by videoconference. Please see the reviewer information page (https://www.nsfgrfp.org/reviewers ) and consider volunteering to serve as a panelist by registering at https://nsfgrfp.org/reviewer_system .
Juan C. Meza
Division of Mathematical Sciences
National Science Foundation
It would be an understatement to say it’s been a turbulent year since the last time IEEE Spectrum broke out the digital measuring tools to probe the relative popularity of programming languages. Yet one thing remains constant: the dominance of Python.
Since it’s impossible for even the most aggressive spy agency in the world to find out what language every single programmer uses when they sit down at their keyboards—especially the ones tapping away on retro computers or even programmable calculators—we rely on combining 11 metrics from online sources that we think are good proxies for the popularity of 55 languages.
Because different programmers have different interests and needs, our online rankings are interactive, allowing you to weight the metrics as you see fit. Think one measure is way more valuable than the others? Max it out. Disagree with us about the worth of another? Turn it off. We have a number of preset rankings that focus on things such as emerging languages or what jobs employers are looking to fill (big thanks to CareerBuilder for making it possible to query their database this year, now that it’s no longer accessible using a public application programming language).