The path to production is a journey that necessitates applying software engineering practices to mathematical modeling. Learn about ways to demystify and streamline that process in this panel discussion.
What does agility look like in the decision science space? Learn about the factors that accelerate decision model development and prototyping, ensure quality, and deliver project value.
Behind every optimization project, there’s a compelling origin story to be told. Learn about tough (and valuable!) lessons in practicing OR from the folks who’ve lived them.
How are open source projects being used today? How should the community think about adoption and participation? Hear from members of the Pyomo and HiGHS teams.
Human review and feedback is part of any good decision workflow. Learn what decision algorithm developers and teams can do to increase trust and build confidence.
Carolyn and Alison reflect on trends and observations from the INFORMS Analytics+ 2026 conference.
An overview of the emerging, foundational pieces required to leverage artificial intelligence within decision systems in a safe, reliable, effective, and consistent way.
Carolyn Mooney from Nextmv and Alison Cozad from Aimpoint Digital discuss evolving landscape of decision science ahead of the INFORMS Analytics+ 2026 conference.
How robust is your path to production? Can you own the end-to-end decision process? From handling input to removing hard-coded parameters, managing versions, and rollout testing, we’ll cover the top best practices for building, testing, and deploying decision models efficiently.
From greedy algorithms to sophisticated mathematical optimization models, Swiggy's VP of Data Science discusses the evolution of their decision systems in mobility and commerce.
NVIDIA GPU-accelerated decision optimization has elevated the conversation around decision intelligence. Watch a Q&A conversation with the NVIDIA cuOpt team to learn more.
“I wouldn’t pair these products.” “How much better is this optimized schedule than mine?” Human review and feedback is part of any decision workflow...
Historian and philosopher Hannah Arendt once said, “Storytelling reveals meaning without committing the error of defining it.” While good stories of operations research and data science can come from practitioners of all kinds...
The operations research and decision science space has a diverse portfolio of open source projects, including Pyomo and HiGHS. Recently, new momentum is building around project adoption...
“I’d like help deploying this decision model. Does this .ipynb work? Or would you prefer .zip?” If you work on decision science projects, it is possible you’ve asked or been asked this question...
Good. Fast. Cheap. Pick two, they say. But (true to form) decision intelligence teams strive to maximize for all three — project success often hinges on it. Balancing these objectives often comes down to a team’s agility ability. What does this look like? And how is it put into practice?
Two operations research PhDs with varied real-world experiences explore skillsets, actions, and considerations for entering into and practicing in OR and decision science in industry analytics settings.
Integrated, holistic optimization approaches are critical for long-term success — without them your project will fail. Learn what these workflows look like through a batch production scheduling example.
“What is your AI strategy? How are you investing in AI? Where are you incorporating AI into your everyday workflows?” Before you think “GenAI” and “LLMs”, have you considered optimization?
Learn how to leverage data and AI for better operational decisions.
How do optimization teams get decision models live into business processes faster as managed services? We explore this through the lens of dedicated DecisionOps workflows.
What approaches are available to decision scientists and operations researchers to incorporate more randomness and uncertainty into their models? We explore this, ML + OR, and stochastic optimization with Nextmv and Seeker.
The CEOs and founders of two startups sit down with Carolyn Mooney to discuss logistics and automation, navigating the evolving world of AI technology, and the benefits of efficiency and sustainability.
Is optimization a solved problem? How does it fit into modern business models such as on-demand delivery? What does it mean to model like an operator? We’ll ask Dr. Hoffman these questions and more.
