I attended INFORMS (for the first time!) 2023, hosted in Phoenix, Arizona 🥵 . It was a nice experience overall! I mostly attended the Applied Probability Society sessions during the conference.

About INFORMS

The Institute for Operations Research and the Management Sciences, or INFORMS, is the world’s largest professional society dedicated to operations research and analytics. With a mission to promote the scientific approach to decision-making, INFORMS plays a critical role in connecting researchers, practitioners, and educators, fostering a vibrant community dedicated to advancing related fields: operations research, statistics, computer science, mathematics, and so on. I also learned a fair bit about what the fields of revenue and supply-chain management are about. The 4-day program had 84 tracks, 11 major tutorials, and hundreds of sessions (one of which I chaired).

Applied Probability Society (APS)

The society is “concerned with the application of probability theory to systems that involve random phenomena” and “members include practitioners, educators, and researchers with backgrounds in business, engineering, statistics, mathematics, economics, computer science, and other applied sciences.” I attended the APS business meeting, where the inaugural Blackwell Award was presented (David Blackwell was an INFORMS fellow) and other APS-specific issues were discussed.

APS Session on “Optimization over Probability Distributions”

I chaired a session with the following talks:

1) Abdul Canatar (Flatiron Institute) on “Out-of-Distribution Generalization in Kernel Regression” https://arxiv.org/abs/2106.02261

2) Prayaag Venkat (Harvard) on “Near-optimal fitting of ellipsoids to random points” https://arxiv.org/abs/2208.09493

3) Ellen Vitercik (Stanford) on “Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty” https://arxiv.org/abs/2302.09700

4) R. Srikant (UIUC) on “Crowdsourcing with Hard and Easy Tasks”

5) Daniel Alabi (Columbia) on “Degree Distribution Identifiability of Stochastic Kronecker Graphs” https://arxiv.org/abs/2310.00171

Until the conference, I hadn’t heard the speakers talk about these specific works. So the APS session was a direct way to learn about what they have been up to recently. Overall, I learned a lot from the conference and I’m looking forward to attending future iterations.

Recently, I have been asked at least three times about resources for learning spectral techniques. Three is my threshold that warrants a short blog post 🙂

First, what are spectral techniques? It is a set of mathematical methods and tools that involve the analysis of the eigenvalues and eigenvectors of matrices associated with a given mathematical or data structure. Example of such data structures could be the adjacency matrix of a graph or the unitary matrix representing common quantum logic gates.

The use of spectral techniques are generally popular within fields (e.g., math, physics) that deal with data decompositions or linear transformations. Here are some resources (in mostly random order) for learning about spectral techniques:

1. NetworkX: A python playground for network analysis in Python.
2. The book on “Spectral Graph Theory” by Fan R. K. Chung which covers the mathematical basics of spectral graph theory.
3. The reference ”Spectral and Algebraic Graph Theory” by Daniel A. Spielman which contains background material and applications of spectral graph theory. It also has some accompanying code to further reinforce understanding! This is a huge plus.