Ben Grimmer

Assistant Professor @JohnsHopkinsAMS, Optimization, PhD @Cornell_ORIE
Mostly here to share pretty maths/3D prints, sometimes sharing my research

[Victor Reis @vetohaze]

I spent a year of my PhD stuck on a 2002 problem of Schechtman. GPT 5.5-Pro helped me finish: vector balancing for zonotopes (shadows of a cube)!

For any zonotope Z ⊂ ℝᵈ, v₁,...,vₙ ∈ Z, there are signs x₁,...,xₙ ∈ {-1, 1} with x₁v₁+...+xₙvₙ ∈ O(√d) Z, sharp. [1/4]

[Ben Grimmer @prof_grimmer]

@CsabaSzepesvari @TaeHo_Y00N Excellent questions, alas with primarily open answers.
Everything in our theory is limited to two norms. I'm doubtful our techniques generalize gracefully. Similarly, it's quite tricky to formulate a noncontractive family of problems where distance bounds are tractable

[Ben Grimmer @prof_grimmer]

I'm excited to share some joint work done with @TaeHo_Y00N.
We considered algorithm design for fixed-point problems.
This area models gradient descent, minimax optimization, and more.
Below I give the wild ride of this paper.
Mathematically, it is gorgeous.

[Ben Grimmer @prof_grimmer]

Discovering these results with TaeHo has been a delightful experience. I learned a lot.
We drew on tools from combinatorics, spectral graph theory, performance estimation, and more. For those interested, the paper is here:
https://t.co/ONz1KoR1eQ

[Ben Grimmer @prof_grimmer]

Taking this duality, one may ask for methods that are self-dual. Recursively building a maximally self-dual method gives a simple fractal arc diagram and a new Fractal Self-Dual Method.
This FSDM nicely balances anytimeness and robustness; see the paper for details.

[Peter Richtarik @peter_richtarik]

My star postdoc Sarit Khirirat (https://t.co/g9u9mcBUMA) is leaving my Optimization & Machine Learning Lab (https://t.co/J2OIFKtkza) to become an Assistant Professor in his home country (Thailand).

As you can see, he is completely checked out, enjoying social media (my guess) and matcha at Zed's at KAUST. 😜😎🤟

This means I have an opening for another star postdoc! If you love mathematics, foundations, optimization and machine learning -- and have outstanding track record in highest quality research -- apply!

https://t.co/SOJ0k2p59Q

[Alvaro Lozano-Robledo @mathandcobb]

"We found that resurrecting the ancient practice of logarithm tables to be useful" -- Terry Tao @ICERM

[George Lan @GeorgeLan7]

Walking with Tianjiao and Caleb：Tianjiao will join IBM Research as a Goldstine Fellow before heading to the University of Wisconsin–Madison, and Caleb will join the University of Tennessee, Knoxville as faculty. Congratulations to both!

[Jonathan Shi @jtnshi]

We just put out a 180-page paper on sampling from the SK model! One big surprise we ran into: the Hessian Ascent algorithms investigated for non-convex optimization have been diffusion models in disguise the whole time! https://t.co/V8cYKRxiLH

@JuspreetS @oldheneel

[Ben Grimmer @prof_grimmer]

Fresh arxiv paper on our method and analysis:
https://t.co/LSxfGnQ95d

[Ben Grimmer @prof_grimmer]

Last year, @mateodd25 and Ian McPherson began searching for provably good nonsmooth optimization methods on manifolds.
Oh boy, did I quickly learn the hard subtleties of numerical work on manifolds, especially combined with finicky subgradients.

[Ben Grimmer @prof_grimmer]

If you retract/transport vectors by projections or via Taylor approx, you can not trust the resulting subgradients give valid lower bounds.
After a year of pushing, we found a bundle method form that (provably) works despite this.

I'll comment a link if you want to read more.

[arXiv math.OC Optimization and Control @mathOCb]

Man, Machine, and Mathematics
arXiv:2604.27052

Nonsmooth Riemannian optimization with inexact manifold primitives v...
arXiv:2604.27078

Explainable Artificial Intelligence for Financial Integral Equations...
arXiv:2604.27127

[Shiqian Ma @ShiqianMa]

My lab will present two papers at ICLR 2026 in Rio de Janeiro, Brazil: "Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains" and "Adaptive Gradient Descent on Riemannian Manifolds and Its Applications to Gaussian Variational Inference"