Explore the applications of mathematical optimization alongside the AI/ML landscape for predictive analytics, LLMs, and beyond.
This interview explores perspectives on the intersection of ML and OR, challenges and opportunities for data scientists in the optimization space, observations in industry, and why now is a great time for practitioners to expand their skillset.
This interview explores perspectives on growth and adoption of optimization across practitioners, challenges and opportunities in the optimization space, thinking about optimization in broader AI strategies, and DecisionOps + Hexaly.
2024 was the year of the Python modeling experience, optimization integrations, and helping modelers find solutions even faster with parallel runs and interactive visualizations. In 2025, we look forward to combining ML & OR, more data integrations, and decision pipelines for smoother operations.
Increasingly, OR practitioners are seeking to incorporate more real-world uncertainty into decision models instead of only relying on deterministic optimization approaches. In this interview, we’ll explore this topic through the lens of Seeker, a new stochastic optimization solver.
“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. From business leaders to front-line operators, stakeholder trust and buy-in can make or break the momentum behind any given decision intelligence project. So what can decision algorithm developers and teams do to increase the chances of success?
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, this panel discussion encourages you to put your chin in your hands and elbows on the table and listen to the lessons lived and learned from a pair of industry leaders who have seen much and done much.
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 in research and industry contexts and ways to contribute and support to these communities. How are these projects being used in the wild? What’s changed since their initial releases? How should the community think about adoption and participation?
“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. Integrating a decision model into a live business process is a journey that necessitates applying software engineering practices to mathematical modeling in ways that are not always intuitive or straightforward. What are ways to demystify and streamline that process?
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.
