Learn about analytics, predictive modeling, and optimization from top trainers and business solution providers!

Access free online learning from the world’s largest association of operations research, analytics, and data science professionals. Learn from leaders of innovation how optimization, predictive analytics, modeling, machine learning, and other critical areas impact success. INFORMS Sponsored Webinar Series provides insight from top educators and cutting-edge businesses that are delivering real-world applications, and research-based solutions.

Don’t miss these free webinars and opportunity to build your knowledge. 

  • Learn new skills
  • Find solutions to problems
  • Grow your career

Register below for each individual webinar and check back for upcoming webinars and recordings.  

Watch our previous webinars on-demand

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Solving Bilinear Programming Problems

Join Gurobi Optimization for an in-depth review of three Bilinear Programming Problems that are common across many industries. These problems include: Quadratic Programming Problems, Quadratic Assignment Problems, and Mixed Integer Quadratically Constrained Programming Problems. The presentation will provide practical examples for solving problems to maximize revenue and improve efficiencies.

Participants of this webinar will learn and discuss: 

  • Implementation of the Bilinear Programming Problems formulation using the Gurobi Python API
  • Traditional linearization approaches to tackle Quadratic Assignment Problems and comparison with direct formulation without linearization
  • A tight formulation of the Pooling Problem


Pano Santos 

Dr. Santos is a Sr. Technical Content Manager at Gurobi Optimization. Santos retired from Hewlett-Packard Enterprise as Distinguished Technologist. During his 23 years at HP Labs, he developed and implemented several decision support tools of mathematical programming applications for workforce planning in the services industry, supply chain planning, CRM, transportation and logistics, and operating room scheduling. Santos has a Bachelor’s degree in Applied Mathematics from the University of Mexico (UNAM), and Masters and PhD degrees in Operations Research from the University of Waterloo in Canada.


Workforce Optimization: A Use Case for Quantum Inspired Computing Power 

There is a near-universal need for organizations across industry sectors to reimagine workforce and resource optimization, due to the rapidly evolving dynamics that are redefining today’s work environment.  With teleworking trends on the rise, social distancing mandates in place and greater focus on people-centric initiatives, organizations find themselves at a tipping point. The balance between output and outcome, efficiency and effectiveness, productivity and satisfaction, is growing narrower by the day.

Join this webinar to learn how:

  • An innovative quantum inspired architecture capable of breaking through the limits of conventional computers is helping to achieve results and address key business challenges in the workforce optimization space
  • The key characteristics of quantum inspired computing, enable swift evaluation of massive data combinations to find the optimal solution in seconds translating into profitability gains, cost savings and employee engagement


Nicholas Lee, Sr. Director, AI and Quantum, Fujitsu Intelligence Technology

Georges Baydoun, Operations Research Specialist, Fujitsu Intelligence Technology


How Python Modelers Can Increase ROI and Business User Engagement with FICO Xpress Insight

  1. How to deploy a Python model in FICO Xpress Insight
  2. Customization options for Python models
  3. Practical demonstrations of several common use cases

For our latest free webinar, join speakers Oliver Bastert, Johannes Mueller, and Vladimir Roitch as they introduce you to an enterprise-ready solution that integrates with Python to convert your analytics into a useable, interactable form: FICO Xpress Insight.

Python is one of the world's most popular languages, yet developers often fail to deliver their analytics solutions to end-users. If you work with Python, don’t miss this chance to learn how to overcome all of the "last-mile” obstacles and actually generate applications that allow for what-if analyses, reporting, user management, load balancing, drag-and-drop UI creation capabilities, and more. We’ll show you:

  • How to deploy a Python model in FICO Xpress Insight in 5 minutes
  • Customization options for Python models
  • Practical demonstrations of several common use cases


Oliver Bastert, Johannes Mueller, and Vladimir Roitch



Adding Optimization to Your Applications: AMPL's Model-Based Approach to Fast Development and Reliable Deployment

In this webinar, you’ll learn:

  1. An understanding of the optimization modeling lifecycle and the components of the modern optimization toolchain.
  2. An appreciation for the benefits of model-based optimization, including the advantages of modeling and modeling languages over programming and programming languages for optimization.
  3. Case studies of successful model-based optimization applications across a variety of business types.


Robert Fourer
Northwestern University
Website with CV

Robert Fourer is co-founder and President of AMPL Optimization Inc. and Professor Emeritus of Industrial Engineering and Management Sciences at Northwestern University. In collaboration with colleagues in Computing Science Research at Bell Laboratories, he initiated the design and development of AMPL, a widely used optimization language and system, and wrote a popular book on optimization modeling in AMPL. Additionally, he has been a key contributor to the NEOS Server project to make optimization software available over the Internet, and has supported development of open-source software for operations research through his service on the board of the COIN-OR Foundation. He shared the Beale-Orchard-Hays Prize for NEOS, and the INFORMS Impact Prize in recognition of the influence of algebraic modeling languages for optimization.


The Princeton 20 for AI Projects: A Framework to Manage Project Risks and Successfully Deploy Solutions

Princeton Consultants has had the privilege of working with innovators across a wide range of industries to improve their decision-making using advanced analytics. Over 40 years, this work has worn many labels. What has historically been called expert systems, decision support, and operations research is today often simply called Artificial Intelligence (AI). During this time, the core techniques have vastly expanded – from bases in statistics, simulation, and mathematical optimization, to include machine learning and new classes of hybrid algorithms. These advances have been multiplied by the exponential improvements in supporting hardware.

We have found consistent and repeatable factors that govern whether a given analytics project will make it into successful production. These factors also apply to the latest AI projects. We have codified these learnings into the “Princeton 20” – a list of 10 environmental and 10 technical risk factors that will help you anticipate issues and increase the likelihood of a successful transition from promising research to production deployment and wide usage.

Join Princeton Consultants Founder and CEO Steve Sashihara for a webinar that is ideal for practitioners and team leaders who deploy solutions into production. In this webinar, you’ll learn:

  1. 10 business risk factors and 10 technical risk factors to monitor in AI projects.
  2. How to score project risk factors.
  3. Tips and techniques to overcome obstacles and deploy solutions.


Steve Sashihara, Founder and CEO

Princeton Consultants


The Predictive Modeling Workflow

October 8, 2020
3pm EDT

The webinar will move through the full life cycle of a predictive analytics project. Steps shown includes data cleaning and preparation, initial exploration and analyses, model fitting, evaluating and comparing model performance, final model selection, and deployment into a production environment. Modeling techniques shown will include Multiple Linear Regression, Regression trees, Neural Nets, and K-Nearest Neighbors.

In this webinar you’ll learn:

  1. Best practices in how to approach a predictive modeling effort.
  2. Some of the common mistakes analyst make in a predictive modeling effort.
  3. Why using a point-and-click interactive software environment can be more efficient.


Kevin Potcner



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