Ayoub Ghriss

Ayoub Ghriss

Postdoctoral Researcher · MVRL, WashU

I am a postdoctoral researcher in the Multimodal Vision Research Laboratory (MVRL) at Washington University in St. Louis, working with Nathan Jacobs and Caleb Robinson.

I earned my PhD at the University of Colorado Boulder, where I was supervised by Claire Monteleoni and defended my dissertation in May 2026. My PhD focused on representation learning for graph clustering, with an emphasis on efficient machine learning and GPU-accelerated implementations.

I previously graduated from École Polytechnique (Computational Mathematics) and ENS Paris-Saclay (MVA: Mathematics, Computer Vision, and Learning), and worked on hierarchical reinforcement learning and state representation at RIKEN AIP, Tokyo, with Masashi Sugiyama.

CV Open PDF • TeX Source

Research

Probabilistic Graph Cuts and Structured Sparse Training for Frugal Downstream Adaptation With an Application to Sea Level Modeling
Ayoub Ghriss — PhD dissertation, University of Colorado Boulder, May 2026
Thesis
Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones
Ayoub Ghriss and Sourav Chakraborty, Arxiv:2607.17419
Paper • Code
S$^3$: Structured Sparsity Specification
Ayoub Ghriss — Preprint, Arxiv:2604.11315
Paper • Code • Docs
Beyond Spectral Clustering: Probabilistic Cuts for Differentiable Graph Partitioning
Ayoub Ghriss — AISTATS 2026
Paper • Code • Docs
Crowdsourced Information Authentication: A Graph-based Model from the Science of Hadith
Ayoub Ghriss — Muslims in ML Workshop, ICML 2025
ICML • OpenReview • Slides
Deep Clustering via Probabilistic Ratio-Cut Optimization
Ayoub Ghriss, Claire Monteleoni — AISTATS 2025
Paper • Code
Predicting Serious Injury and Fatality Exposure using Machine Learning in Construction Projects
E.D. Oguz Erkal, M.R. Hallowell, A. Ghriss, S. Bhandari — J. Construction Engineering and Management, 2024
Paper
Sentiment-aware Automatic Speech Recognition Pre-training for Enhanced Speech Emotion Recognition
A. Ghriss, B. Yang, V. Rozgic, E. Shriberg, C. Wang — ICASSP 2022
Paper
Insights from an Autism Imaging Biomarker Challenge: Promises and Threats to Biomarker Discovery
N. Traut et al. (incl. A. Ghriss) — NeuroImage, 2022
Paper
Deep Learning on Satellite Imagery For Air Quality Modeling
K. Klein, A. Ghriss — Google SWE PhD Internship, 2020
Code
Reinforcement Learning with Options and State Representation
A. Ghriss, M. Sugiyama — RIKEN AIP, 2018
Paper

Software

PGCuts

Differentiable graph cuts with GPU-accelerated hypergeometric kernels. sklearn-compatible API.

Code • Docs

SparseKit

Structured sparsity specification (S3) kit: carve LLM weights the way you wish.

Code • Docs

Sphedron

Refinable polyhedral meshes on the sphere for GNN-based geospatial ML.

Code • Docs

Qwantize

Optimal quantization for block-scaled low-precision formats (NVFP4, MXFP4).

Code • Docs

Tritonix

Triton GPU kernels with an optimized autotuning framework.

Code

Embedata

Dataset and embedding loader for reproducible ML experiments.

Code