Job as Information Security and Compliance Manager

Information Security and Compliance Manager

Oxnard, CA

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.

https://worldsfinestavocados.com/careers

Cloud Computing certification training open to the public at CSUCI

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.

Computer Science selected for an AWS Pilot program in Machine Learning

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).

ML is one of the areas of AWS certification.

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

OnDemand AWS Educate Professional Learning Series

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/

Our results in “Edge Covers” picked up by an Astronautics research group at MIT

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.

NSF Graduate Research Fellowship (GRF) opportunities

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

Top Programming Languages 2020 – IEEE Spectrum

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).

Source: Top Programming Languages 2020 – IEEE Spectrum

CI master students’ research accepted at the KES2020 international conference in Verona

KES 2020 in Verona but virtual

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.)

IEEE Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS) 2021 conference

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

AWS training at CI in the Fall 2020

For questions please contact: jeff.ziskin@csuci.edu (805-437-2653). To register for an information session, or to register for the classes:

https://ext.csuci.edu/programs/professional-community-ed/aws

These classes are open to the public, and they are given in partnership with the AWS Academy.

  1. Cloud Foundations: online from August 24 to October 5:
  2. Cloud Architecting: online from October 19 to December 14:

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.