Arash Behboodi I am a research scientist and Director of Engineering at Qualcomm AI Research. I am interested in general in theoretical foundations of information and computational systems. My research interests include among others information theory, learning theory, machine learning for inverse problems, compressed sensing, geometric deep learning, differentiable simulation and agentic AI.

News and Highlights

• See our NeurIPS 2025 expo demo on: Efficient Reasoning on the Edge.

• New paper: Fundamental bounds on efficiency-confidence trade-off for transductive conformal prediction

• I give a keynote talk about Information Theoretic Perspective on Conformal Prediction at 14th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2025

• Accepted paper at Globecom 2025: ReQuestNet: A Foundational Learning model for Channel Estimation

• Best poster award for ViNE-GATr: scaling geometric algebra transformers with virtual nodes embeddings at ICLR 2025 Workshop on Machine Learning Multiscale Processes (Congrats to Julian Suk, Thomas Hehn, and Gabriele Cesa)

• Accepted paper at ACL 2025: Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving, also presented at ICLR 2025 Workshop on Reasoning and Planning for Large Language Models

• Accepted paper at ICLR 2025: Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits (Spotlight)

• Accepted paper at ICLR 2025: Probabilistic and Differentiable Wireless Simulation with Geometric Transformers

• Accepted paper at TMLR: On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing

• Accepted paper at TMLR: Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach

• Accepted paper at NeurIPs 2024: An Information Theoretic Perspective on Conformal Prediction

• We are organizing the workshop D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers at NeurIPS 2024.

• My chat with Sam Charrington at TWIML AI podcast around Differentiable Simulation, Conformal Prediction, and AI at the Edge .

Selected recent papers

• Arash Behboodi, Alvaro H.C. Correia, Fabio Valerio Massoli, Christos Louizos Fundamental bounds on efficiency-confidence trade-off for transductive conformal prediction, Preprint, 2025

• Julian Suk, Thomas Hehn, Arash Behboodi, Gabriele Cesa ViNE-GATr: scaling geometric algebra transformers with virtual nodes embeddings , ICLR 2025 Workshop on Machine Learning Multiscale Processes (Best Poster Award)

• Sara Rajaee, Kumar Pratik, Gabriele Cesa, Arash Behboodi, Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving, ACL 2025, and ICLR 2025 Workshop on Reasoning and Planning for Large Language Models

• Ashish Khisti, M.Reza Ebrahimi, Hassan Dbouk, Arash Behboodi, Roland Memisevic, Christos Louizos, Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits , ICLR 2025 (Spotlight), also appeared at ICML 2024 Workshop on Theoretical Foundations of Foundation Models

• Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer, Probabilistic and Differentiable Wireless Simulation with Geometric Transformers , ICLR 2025, also appeared at ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling 2024

• Arash Behboodi, Gabriele Cesa, On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing , TMLR, 2025

• Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi, Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach , TMLR 2025, also appeared in Symmetry and Geometry in Neural Representations - NeurReps Workshop, NeurIPS 2023

• Fabio Valerio Massoli, Tim Bakker, Thomas Hehn, Tribhuvanesh Orekondy, Arash Behboodi, Simulating, Fast and Slow: Learning Policies for Black-Box Optimization , Preprint 2024

• Alvaro H.C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi, An Information Theoretic Perspective on Conformal Prediction , NeurIPS 2024

• Fabio Valerio Massoli, Christos Louizos, Arash Behboodi, Variational Learning LISTA , TMLR, July 2024