The little engine that could generate INFORMS 2018 talks

(Originally posted in the INFORMS 2018 blog).

There are many ways to contemplate data. Shabbir Ahmed just tweeted a word cloud based on talks of the INFORMS 2018 Annual Meeting in Phoenix:

In this post, I will use a random walk instead. I wrote a piece of code that traverses talk titles to compute the frequency that each work is the first, the last, or the immediate successor of another word. This Markovian model can easily generate random titles but it is arguably not state-of-art, so why not using something more ellaborate like an LSTM? I wanted something complex enough to make sense while simple enough to generate unexpected outcomes. Otherwise, it would be boring to read them!

I believe that I can roughly classify the results that I got in four categories, which by decreasing frequency are the following:

  1. Non-sense.
  2. Awkward or boring statements.
  3. Long titles mixing the most commonly used terms (see “The buzzword bingo”).
  4. Somewhat plausible but unexpected titles (see “The interesting talks you will not see at INFORMS”).

The interesting talks you will not see at INFORMS

These are the types of talks that I am looking for when I arrive every year:

  • A Spatial Branch-and-Bound Algorithm via Retrospective Study
  • A Vehicle Routing Problem Becomes My Dishwasher: Selective, Manufacturing
  • Active Surveillance Monitoring of Large SDPs
  • Ambulance Emergency Response by a Social Media Network Component
  • An Integrated Train Timetabling Using Social Networks
  • An Optimization Competition in Retail: Evidence from Conflict Constraints and Implications
  • Asymptotic Optimality and International Gold Price, a Dual Reoptimization
  • Expanding a Dual is Less Irritating: Inducing Fresh Produce Supply Chain Management Science
  • Grounding Frequent Flyers: A Constraint
  • Managing the Expected Utility Models for More Sustainable, Swimming and Energy Bilateral Ratings in Bike Sharing
  • Optimal Cooperative Game: A Machine Learning Teaching Analytics Modeling and Improvement Projects
  • Optimal Solutions Revisited: The Impact Of Nonlinear Programs for Resource Allocation in Dynamic Decentralized Customization With 3d Printing
  • Optimizing Faculty Summer Research on Alibaba
  • Person Name Detection Using the Mean Field ExperimentalEvidence from Bike-sharing Economy on Different Textual Components
  • Predicting and Working Harder: The Impact on Modularization Design for the Optimal Steady State of Repairable Spare Parts Networks
  • Risk-sharing Agreements for Strongly Convex Risk Management in Platforms
  • Robust Contingency Constrained Optimization of Autonomous Driving: Can Voluntary Time-of-use Tariffs be Recommended?
  • Stochastic Analysis of Semidefinite Optimization
  • Stochastic Programming of Relativity
  • Triple-bottom-line Approach to Identify Smoking Status

The buzzword bingo

When the conference is over and I get overwhelmed, this is roughly what I recollect:

  • A Branch-and-price Algorithm for Fault Diagnosis for Using Data Analytics to Preferred Boarding Patients with Reusable Products in Hospitals? Evidence from an Urban Function Selection Under Competition in a Newsvendor Analysis of Gaussians via Accelerated Minibatch Coordinate Descent
  • A Bilevel Framework for Liver Exchange Intelligence and Morel Hazard
  • A Minimum Cost of First-order Optimization for Stochastic Mixed-integer Recourse via Machine Learning
  • A New Algorithm for the FEMA National Airspace Operations Training System Performance in a Medical Knowledge Gradient Descent Method for Extending Drone Assisted Devices and Water Distribution Networks
  • A Novel, Depth, Mixed-integer Programming Approach to Speculate on Hospitals’ Risk Analysis in Hydropower Plants Using Probability Dominance Information Asymmetry and Supply Chain Choice Model for Resources in Humanitarian Supply Chain Performance Trade-offs when Customers
  • Appointment Scheduling of Wind Along the Use of Outliers: Market Sensing from Digital Gamification Systems for Mislabeled Classification for Machine Learning
  • Blockchain Adoption in Continuous-time Markov Decision Making Economic Assessment for New Product Perishability
  • Blockchain can Increase Value of Doubly Stochastic Gradient Descent: A Machine Learning
  • Conditional Gradient Method of Customer Churn Prediction in Online Experiments on Multi-agent Based on Retail: Evidence from Bike-sharing Systems via Uncertainty
  • Dynamic Integer Programming Approach to Robust PCA by Eliminating Payment Models
  • Dual Bounds for Reinforcement Learning Heuristics to AC Optimal Power and Firm Innovation
  • Efficient Computational General Equilibrium Models for theTraveling Salesman Problem for Network Flow Control of Hospital Waiting Time Delay in Matlab
  • Healthcare Plan for the Use of Renewable Electricity and How Frequent Flyers Choose Their Affiliates
  • Lift-and-project Lower Bounds for the Heterogeneous Marginal Price Optimization in Bangladesh: An Empirical Analysis of Innovation in the Service
  • Mothership and Efficiency Investment in Construction of the Gig Economy Workers
  • Optimal Service Plan Model for High Dimensional Covariates and Quality in What We Forget About Blockchain Technology
  • Queueing Design using the Strongest Influence of Personalized Advertising
  • Realizing the Participatory Exploratory Modelling the Hitchcock- Koopmans Problem Solving Generalizations of an Academic Science at the Boston Public Transportation Platform Selection using Discrete Probability Computation of B2C E-commerce Age of Gamification on Detour Distances
  • Second-order Decomposition for Large-scale P2P Ride-Hailing Networks
  • Social Media Network Using Bilevel Mixed-integer Recourse Strategies: Facilitate P2P Platform ? Evidence from Ford

Wrapping up

It will be no surprise if you found two or three consecutive words above that you used for your talk. To the best that I could, I tried avoiding examples that were too similar to any of the talks in the program. I hope that I managed to keep it that way.

Now, why did I call this piece of code an engine? Marketability! The following tweet is from a talk by Rama Ramakrishnan at a seminar in the MIT Operations Research Center:

Last but not least, INFORMS made my life a lot easier by sending me the data I needed. Special thanks to Mary Leszczynski!

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