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|
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
Jen Dunham, CFE
Principal Solution Specialist
Fraud & Security Intelligence Division
As a Principal Solution Specialist and Certified Fraud Examiner (CFE), Jen is focuses on providing subject matter expertise across the world in addressing various security risks such as Insider Threat, Targeting, Cyber Crime, Threat Intelligence, All-Source (Fusion) Analysis, and similar applications.
Other areas of expertise include Occupational Fraud, Procurement Fraud, and Prescription Drug Monitoring Analytics.
Previously, Jen served as an all-source intelligence analyst in the United States Army for seven years. Jen has a unique and comprehensive view in the Global Defense, Intelligence, and Law Enforcement communities with experience working in mission areas such as investigations, counterterrorism, counterespionage, counternarcotics, and all-source intelligence analysis.
Jen’s contributions have earned her numerous accommodations such two Army Commendation Medals, two Army Achievement Medal, a NATO medal, and a Certificate of Appreciation from FBI Director Robert Mueller.
Jen holds a Bachelor of Science Degree in Business Marketing and recently completed the MIT Management Sloan School Executive Program for Artificial Intelligence: Implications for Business Strategy. Jen resides in the Washington DC area and has been employed with SAS Institute since 2011.
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 deferent in a complex and ever changing environment. By combining analytic methodologies with automation and dynamic behavioral based scoring, risks can be surfaced early and monitored, to reduce losses or mitigate events.
Keirsten & Paul Brager
Biographies: Coming Soon
Abstract: Coming Soon
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.
Director of Strategic Planning
Yoel has In-depth expertise in strategic business planning as an industry-leading innovator and global entrepreneur. He has 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. His areas of expertise are entrepreneurial leadership, data modelling and forecasting, data and cloud solutions and building predictive models and simulators. He holds a MS Degree in Analytics from Texas A&M, a Master of Aplied Design Thinking and Innovation from Stanford University and a Bachelor of Science from the PanAmerican University in Mexico.
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.
Space is limited RSVP today.
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