7th Annual Texas A&M Analytics Forum
Hosted by Mays Business School & SAS®
2019 Forum slides are now available. To view, click here
Date: January 30, 2020
Location: Houston, TX – CityCentre Three, Suite 200 (Texas A&M University-Mays Business School facility)
Cost: There is no cost to attend this event.
|8:15-8:45 AM||Arrive, registration and breakfast, visit exhibitors|
|9:00-10:00 AM||A Sessions (pick one of two)|
|10:00-10:15 AM||BREAK & Transition|
|10:15-11:15 AM||B Sessions (pick one of two)|
|11:30-01:00 PM||Lunch and Keynote Speakers|
Space is limited RSVP today:
Texas A&M Analytics Forum
An event hosted by the Mays Business School at Texas A&M University and SAS®. This event will highlight industry experts, including former Texas A&M MS Analytics students and SAS® speakers who will discuss topics including analytics, data modeling, and data mining to make better business decisions. Join us to hear from industry as we discuss how their companies are using data and analytics, and how they are incorporating open source with SAS®. The number of participants is limited. We are delighted to have three keynote speakers to discuss various security risks such as:
- Insider Threat
- Cyber Crime
- Threat Intelligence
Many sessions, but not all, will have a cyber analytics/security theme this year.
Participants will hear how big data and analytics are being used in companies in:
- Financial analytics
- Cyber analytics
Participants also gain insight into:
• The benefits from using data and analytics in your business
• Common obstacles to data and analytics
• A variety of tools and software for Analytics
• The types of data, software and statistical methods used in analytics decision making
Principal Solutions Architect
Fraud & Security Intelligence Division
John is a Fraud and Cyber Solutions Architect for the Security Intelligence practice at SAS. With a Master’s degree in Epidemiology and Biostatistics from Tulane University’s School of Public Health and Tropical Medicine and 28 years of experience using SAS, John has helped public and private sector customers leverage advanced analytics to improve their techniques for mitigating risk primarily in the areas of fraud and cybersecurity.
John also has a passion for implementing enterprise data management architectures to deploy comprehensive data pre-processing and data validation strategies that support the fusion of data purposefully built for machine learning, anomaly detection and network analysis. John’s popular webinar Data Preparation Best Practice Approaches for Deriving Better Insights emphasizes how important data preparation is to the successful use of advanced analytics.
John resides in the Washington DC area and has been employed with SAS since 1999.
Using advanced analytics to detect and deter fraud and cyber security threats
Fraudsters and cyber criminals rely on weak controls and easily discoverable thresholds in order to circumvent safeguards and commit crime. While these rules-based systems are a good first line of defense, organizations today must adopt a multi layered defense against advanced threats. This session exposes how the use of analytics in the detection of fraud and other security related threats can be extended to be more proactive and a deterrent in complex and ever changing environments.
Keirsten & Paul Brager
Is a lead Security Engineer at a Fortune 500 power utility company, @tribeofhackers contributor, and was one of Dark Reading’s 2018 top women in security quietly changing the game. She is also the author of Secure the InfoSec Bag:Six Figure Career Guide for Women in Security. She produced this resource to help women strategically plan their careers, diversify their incomes, and fire bad bosses. Keirsten holds a M.S. in Cybersecurity and serveral industry certifications, including Splunk, CISSP and CASP. Her professional background includes power utility, retail, and insurance defense industries. As an active member of the Houston security community, Mrs. Brager has participated in a number of panels and public speaking engagements promoting strategies for success. In her free time, she loves blogging, cheering for her beloved Saints team, and convincing women not to quite the industry.
Regarded as a thought leader and expert in the cyber security community for twenty-seven (27) years, Mr. Brager has deep expertise evaluating, securing and defending critical infrastructure and manufacturing assets (ICS, IoT, and IIoT). As a speaker, author, and researcher, Paul seeks to move the conversation forward surrounding industrial control systems (ICS), supply chain and automation cyber and ways to mitigate the attack surface in heterogenous environments. He has provided commentary on several security related podcasts, publications, and webinars that provided guidance and insight into strategies for critical infrastructure protection, IT/OT convergence, and IIoT (industrial internet of things).
Mr. Brager holds a BS in Political Science from Texas A&M University, a MS in Administration of Justice and Security from University of Phoenix and is an Alpha Phi Sigma (National Criminal Justice Honor Society) inductee. Paul has a passion for mentoring and guiding persons of color and advocating for women aspiring to contribute to the advancement of the industry and promoting diversity within the cyber community.
Watching the Gate: Protecting Critical Infrastructure Leveraging Cyber Analytics
The conflicts of the future will not be fought in the trenches, but rather will be a myriad of attacks against nations’ critical infrastructures to disrupt the populations of those nations. By doing so, the chaos introduced can be leveraged as a force multiplier against a potentially stronger adversary. DHS (Department of Homeland Security) as well as security organizations around the globe have been working to bolster the defensive capabilities of critical infrastructures. They are using threat data to predict potential target areas, adversaries, and even date and time events. Access to data is rarely the problem – the ability to provide timely and relevant analysis of the data is crucial to effectively thwarting attacks.
In this discussion, we will examine analytic methodologies used in the detection, identification and response of cyber events. We will provide some of the common tools and tactics used to normalize event data, correlate, and subsequently formulate responses capable of either preventing or containing attacks against critical infrastructures. Lastly, we will provide commentary on the tools and tactics of the future and how advances in analytics can continue to sharpen the cyber analysts’ response to these threats.
Senior Solutions Architect
SAS Energy and Manufacturing business unit
Rob’s focus is on helping customers derive business value with Machine Learning, enabling Citizen Data Scientists, and all things Analytics. Prior to joining SAS he was a Data Scientist in the Automotive industry focused on Customer Experience Analytics. He received his Master of Science in Analytics from LSU as well as his Bachelor of Science in Information Systems and Decision Sciences from LSU.
Empower your data scientists and statisticians with a breadth of analytics capabilities that are easily available from the coding language of their choice. Whether it’s SAS, Python, R, Java, Lua or Scala, analytical professionals can access the power of SAS for data manipulation, interactive data interrogations and advanced analytics. Import open source models into SAS for governance and performance monitoring to achieve the critical deployment step of the Analytics lifecycle. In being an open ecosystem, SAS also includes public REST APIs to all underlying functionality so software developers can add proven SAS Analytics to applications. SAS augments the functionality of open source to create a scalable enterprise analytics platform.
Director, Predictive Enterprise
Tom has over 28 years’ experience in leading, architecting and developing enterprise solutions on a variety of platforms and environments. Tom has a history of quickly identifying then analyzing business trends, patterns and outliers to assess the operational impacts for strategic and tactical goals. Able to work at all levels of the corporate structure from CXO to Data Center without losing sight of Line of Business priorities. Tom is well versed in data analysis, business problem definition, creating innovative solutions and establishing repeatable processes.
Tom currently leads Predictive Analytics for the Houston market. He is accountable for growing the team and building out repeatable capabilities and solution offerings that are aligned with customer business needs and can provide faster time to value. Tom is also responsible for staying tuned into the fast changing AI/ML market place.
Sales & Marketing Analytical Consultant
Biography: Coming soon
If having an almost unlimited supply of different tools and techniques is driving your analytics today, then how successful can you be without understanding the basic problem(s) you are trying to solve? The core question is, are you introducing an AI/ML strategy or are you incorporating AI/ML into your business strategy? Understanding business drivers, success criteria, ownership, security, locality, governance, relevance, politics, growth, recoverability and more, are all factors to look at before you build you first model.
Entrepreneur and founding partner of Grupo-Piensa
A transformation and change leader, I have in-depth expertise in strategic business planning as an industry-leading innovator and global entrepreneur. I possess a well-established record of achieving transformative results for Fortune 100 companies, conceptualizing and establishing organizations and products, and collaborating with executives to drive revenue and reduce costs. I excel at motivating diverse teams to exceed expectations, foster organizational development, and transform work cultures to multiple models while streamlining processes. Yoel is co-designer of 2 leading collaboration platforms: Mutual Fun and Thinkrite Assistant. He has consulted in the strategy of Coca-Cola Co. Mexico, the U.S. NAVY Innovation hub, KIO Networks expansion, among others. Yoel is also a leader of the CREA Conference Europe, Mindcamp Canada, Mindcamp Chile and has international experience as a speaker in topics of innovation and creativity.
Most cybercrime attacks are happening because of an inside incident, whether on purpose or accidental. Actually over 80% of cyberattacks are the result of an inside job. Yet all cybersecurity solutions are either to block the entrance or to train employees on the importance of secure behaviors. Yet it is proven that being a perpetuator, a victim or a bystander of a cybercrime is related to psychosocial elements: Do I like my job? Collaboration, Relationship with my boss, Financial pressure, Marital situation, etc.
We have created a very simple model based on a psychosocial questionnaire that results in coefficient of risk of sorts which gives weight to a socio-gram. We then use the socio-gram to create a probability tree. In the end we are able to measure the probability of a cybercrime being caused from the inside. Additionally we will show the results of the four profiles and how to use neuroscience to reduce the probability of employees acting badly.
VP, Data Science, Sales Analytics
Pablo Ormachea specializes in designing and executing on data-driven strategy. He holds degrees from Harvard (JD), Texas A&M (MS in analytics), and University of Texas at Austin (BA, double major and double minor) — winning several prizes along the way for his innovative quantitative work. Today, he serves as VP at a financial services company where he is head of Data Science (which builds and deploys industry-leading risk, fraud, and cash flow predictive models using machine learning and deep learning techniques) and Sales Analytics (which empowers executive leader decision making by defining and monitoring key metrics, developing forward-looking forecasts, exploiting data to maximize return on assets across the company, and standing up industry-leading self-service platforms for sales staff).
Join Pablo to explore how to leverage supervised and unsupervised techniques to stop financial fraud in its tracks. We’ll use a case study of real-life fraud event that hit my company for hundreds of thousands, and how we leveraged R and Python for supervised models — from logistic regression using regularization to machine learning with gradient boosting — and unsupervised models (autoencoders) to help us catch this kind of fraud faster than ever before. Even though we’ll focus on financial fraud, this sort of first principles anomaly detection should be useful for all fields.
Want to hear more about our MS Analytics Program at Texas A&M? Click here and one of our Program Administrators will be in touch with you very soon!