Gurobi is thrilled to announce our sponsorship and exhibit booth at ODSC East, happening in Boston, Massachusetts, from May 13-15, 2025. This is your exclusive invitation to connect, network, and collaborate with us at the forefront of innovation and technology.
Don’t miss this incredible opportunity to elevate your data science career, gain new insights around mathematical optimization, and be part of a community that’s shaping the future of data science. Whether you’re a seasoned professional or just starting out, there’s something for everyone at ODSC East.
Stop by Booth #23 to connect with the Gurobi team and check out our giveaways!
Don't miss the Gurobi team and our partners in the following presentations:
Expo Talk
Session: Transform Predictions into Actionable, Explainable Decisions using Optimization
Presenters: Jennifer Locke, Manager - Americas Technical Account Management, & Summer Purschke, Technical Account Manager
Location: Expo Hall
Time: 12:15 -12:45 PM
Date: May 13, 2025
Details: Organizations have been investing heavily in traditional machine learning (ML), deep learning, and now generative AI (GenAI). However, truly impactful decision intelligence solves the most complex business problems using more than accurate predictions or multimodal content generation—it demands fast, scalable, and explainable decision-making. By integrating mathematical optimization into their Composite AI frameworks, businesses can unlock provably unmatched efficiency, with the ability to explain decisions and scalability required for smarter, more reliable outcomes.
Workshop
Session: AI + Mathematical Optimization: Build Smarter Decision-Making Models
Presenters: Yash Puranik, Director of Decision Sciences, Aimpoint
Location: Room 209
Time: 2:00 - 3:00 PM
Date: May 13, 2025
Details: In the world of data science, teams are often deeply familiar with statistical and machine learning techniques. With the rapid rise of AI—especially generative AI—the scope of solvable problems has expanded dramatically. However, one powerful yet often overlooked tool remains crucial for tackling complex decision-making challenges: mathematical optimization.
While AI-driven techniques like deep learning and reinforcement learning have dominated recent advancements, mathematical optimization offers a transparent, robust and highly efficient approach to solving constrained decision problems. Unlike traditional machine learning, which focuses on predictive analytics, mathematical optimization is prescriptive— it enables organizations to make optimal decisions while adhering to business constraints and operational rules. Mathematical optimization has been successfully applied across industries for decades, offering robust solutions where AI and machine learning alone may fall short, yet remains underutilized in modern AI-driven workflows.
