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 jcolitti@missionproduce.com.
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 jeff.ziskin@csuci.edu 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.m5.xlarge, ml.c5.xlarge, and 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.
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.
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.
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 Director 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).
CSUCI Master of Computer Science students were successful in submitting two papers to KES 2020, the 24rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, which this year is taking place in Verona, Italy, in September 2020. However, due to the COVID pandemic, the conference will be held virtually. The papers are the following:
Malware Persistence Mechanisms, co-authored by Zane Gittins and Michael Soltys. Zane Gittins is a masters student in Computer Science at CSUCI, and this paper is the result of his masters thesis. Zane Gittins has worked as a Cybersecurity experts at HAAS, and currently is working at Meissner Filtration. (This paper will be presented in the General Track session G3b: Cybersecurity.)
Voyager: Tracking with a Click, co-authored by Samuel Decanio, Kimo Hildreth and Michael Soltys. Sam Decanio is a masters student in Computer Science at CSUCI, and this paper is the result of his masters thesis and a fruitful collaboration between Computer Science at CI and the SoCal High Technology Task Force. Sam Decanio is currently working at the Navy. (This paper will be presented in the General Track session G3b: Cybersecurity.)
May 17 – 19, 2021, McMaster University, Hamilton, ON, CANADA
IEEE RDAAPS is the first annual international conference on research in the broadly defined area of data analytics. It brings together researchers from academia, industry, and the public sector to present and discuss various aspects of data analytics, including privacy, security, and automation. This venue is meant to bring together stakeholders whose interests lie at the interface of these concerns, providing a platform for integrating the needs of industry with state-of-the-art scientific advancements, and inspiring original research on solving enterprise data challenges. IEEE RDAAPS seeks papers presenting original research in the areas including, but not limited to:
Big Data Analytics for Decision Making
New models and algorithms for data analytics
Scalable data analytics
Optimization methods in data analytics
Theoretical analysis of data systems
Analytical reasoning systems
Decision making under uncertainty
Learning systems for data analytics
Large-scale text, speech, image, or graph processing systems
Accountable Data Analytics
Privacy-aware data analytics
Fairness in data analytics
Interpretable and transparent data analytics
Data analytics incorporating legal and ethical factors
Strings in Data Analytics
Patterns in Big Data
Data compression
Bioinformatics
Algorithms and data structures for string processing
Useful data structures for Big Data
Data structures residing on secondary storage
Security in Data Analysis
Traceability of decision making
Models for forecasting cyber-attacks and measuring impact
Data usage in mounting security threats
Data analytics for better situational awareness
Domain knowledge modelling and generation
Novel ontology representations
Scalability of domain-based reasoning on big data
Modelling and analyzing unstructured data sets
Automation for data analytics, security, and privacy in manufacturing
Application of data analysis in manufacturing
Big data in Industry 4.0
Privacy and security in manufacturing
Challenges of automation of data analytic processes
Case studies of the automation of data analytics processes
Architecture for data analytics and security
Built-in privacy and security in data analytics automation
Submission instructions:
Successful papers will address real research challenges through analysis, design, measurement, and deployment of data systems. The program committee will evaluate each paper using metrics that are appropriate for the topic area. All submissions must describe original ideas, not published or currently under review for another conference or journal.
Submissions must follow the formatting guidelines of IEEE proceedings, and be submitted electronically as a PDF file through EasyChair. Submissions not adhering to the specified format and length may be rejected immediately.
The submitted papers can include up to 8 pages in IEEE format, including references, appendices, and figures.
Publication:
All accepted papers will be published in the IEEE conference proceeding.
Important dates:
Deadline for full paper submission: December 21st, 2020
Notification to authors: February 22nd, 2021
Deadline for camera ready version: March 15th, 2021
We are following exactly the AWS curriculum, and students will be provided AWS Educate cloud accounts with credits for the duration of the classes, as well as vouchers for writing the corresponding certification exams.
