The Decision Intelligence Lab
The Decision Intelligence Lab
Podcast Description
The Decision Intelligence Lab explores practical challenges of applying data science, analytics, and AI to drive real-world business outcomes.
Hosted by Prof. Michael Watson (Northwestern University) and Prof. Vijay Mehrotra (University of San Francisco) — both seasoned entrepreneurs, consultants, and researchers — this podcast delivers real-world insights for data professionals, business leaders, & anyone seeking to leverage data for smarter decision making. Each episode features leaders sharing how smarter decisions are reshaping business and technology. Subscribe to join the conversation.
Podcast Insights
Content Themes
The podcast explores themes like the impact of AI on engineering education, the factors that lead to data science project failures, and risk management in AI projects. Specific episodes have discussed topics such as the importance of resiliency in engineering, organizational maturity's role in data science success, and frameworks for risk mitigation like the Princeton 20.
The Decision Intelligence Lab explores practical challenges of applying data science, analytics, and AI to drive real-world business outcomes.
Hosted by Prof. Michael Watson (Northwestern University) and Prof. Vijay Mehrotra (University of San Francisco) — both seasoned entrepreneurs, consultants, and researchers — this podcast delivers real-world insights for data professionals, business leaders, & anyone seeking to leverage data for smarter decision making. Each episode features leaders sharing how smarter decisions are reshaping business and technology. Subscribe to join the conversation.
Meinolf Sellmann, computer scientist, entrepreneur, and founder/CEO of InsideOpt, joins the Decision Intelligence Lab podcast with hosts Mike Watson and Vijay Mehrotra. Meinolf built MIP solvers at Bell Labs, IBM, and GE before launching his startup, InsideOpt.
The conversation starts with a 1998 story: George Nemhauser named three open challenges in operations research- timely decision support, multi-objective optimization, and decision-making under uncertainty. Nearly 30 years later, according to the 2025 NSF DECIDE workshop, the same problems are critical to national security and competitiveness.
Meinolf explains why machine learning ”hit a nerve” but couldn't deliver perfect forecasts, why optimization is seeing renewed interest, and how primal solvers attack highly combinatorial problems under uncertainty. He covers jettisoning dual bounds, scaling to 1,000 cores with ML-guided search operators, and beating Gurobi by a factor of 1,000 on quadratic assignment (Taillard instances). He shares competition wins (MaxSAT 2016, AI for TSP 2021 at IJCAI), a real coffee-roasting scheduling story, and three principles for better decision-making.
What You'll Learn
– Why George Nemhauser's 1998 challenges remain unsolved today.
– The difference between primal solvers and dual solvers, and why dual bounds limit you.
– Why perfect forecasts are impossible and what to do with residual uncertainty.
– How machine learning guides search
– Why a primal solver scales to 1,000 cores while MIP heuristics stall.
– How Seeker beat Gurobi by 1,000x on quadratic assignment (Taillard instances).
– Why the right tool beats raw algorithmic improvement.
– Bridging the gap between a well-shaped technical problem and the business customer's real problem.
– The coffee-roasting scheduling story — why MIP failed and a primal solver won.
– Three rules for good decision-making
– Why risk mitigation matters more than expected value (gambler's ruin, UPS fleet scenarios).
Timestamps
0:00 – Preview & Introduction
0:52 – Meet Meinolf Sellmann, InsideOpt
1:29 – The 1998 George Nemhauser story: Three OR challenges
3:21 – Multi-objective optimization: the three canonical approaches and why they fail
6:20 – Why renewed focus on decision-making after the AI/ML wave
7:43 – Perfect forecasts are impossible: the sushi example
9:50 – Solving combinatorially complex problems under uncertainty
12:10 – What is a primal solver vs. a dual solver?
14:16 – Technical problem vs. the business customer's real problem
16:45 – Jettisoning bounds; 1,000 cores; ML-guided search
19:22 – Machine learning as counting cards in blackjack
22:05 – Hardware vs. algorithms; beating Gurobi 1,000x on quadratic assignment
23:43 – Why leave the big labs and start a company
25:27 – MaxSAT 2016 win; the self-learning solver
27:29 – Evolving view of good decision-making: three principles
31:20 – Where to find InsideOpt and Seeker
35:40 – The Coffee-Roasting Scheduling Story
Follow the show
Apple: https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064
Spotify: https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b
Connect with guest
Meinolf Sellmann: https://www.linkedin.com/in/meinolf-sellmann-a349636/
InsideOpt: https://insideopt.com/
Connect with hosts
Prof. Vijay Mehrotra (University of San Francisco): https://www.linkedin.com/in/vijay-mehrotra-ba9498/
Prof. Michael Watson (Northwestern University): https://www.linkedin.com/in/michael-watson-07600a1
About the podcast
The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.
For business inquiries, email at [email protected]
Disclaimer
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