Hassan Hamad

Hassan Hamad

Applied AI Researcher @ AWS

I'm an applied AI researcher at AWS. I post-train LLMs, build environments and benchmarks that stress-test AI agents, and engineer the harnesses that turn models into real agentic products.

Previously, I completed my PhD at USC on training efficiency in deep learning, and worked on synthetic data for NLP tasks like named entity recognition and relation extraction.

Latest News

Oct 2026 📄 Plan-and-Patch, on diffusion language models for agentic planning, accepted at the NeurIPS 2026 Workshop on Diffusion Language Models Read paper →
Mar 2026 🚀 AWS Security Agent (now part of AWS Continuum) is generally available for on-demand penetration testing. Proud to be part of the team that built it! Learn more →
Dec 2025 🚀 AWS Security Agent launched in preview at re:Invent 2025.
Nov 2025 📝 New blog post: Speculative Decoding: Fast LLM Inference Without Quality Loss Check here →
Oct 2025 📄 New arXiv pre-print on Neural Network Training with Log Number Format Read paper →
Oct 2025 📄 New arXiv pre-print on Correcting LLM Tool Call Mistakes Read paper →
Sep 2025 📝 New blog post: Forward vs Reverse KL Divergence: Why the Direction Matters Check here →
Jan 2025 🚀 Joined AWS Agentic AI as an AI Researcher!
Nov 2024 🎓 Successfully defended my PhD thesis at USC!
Jun 2024 📄 FIRE dataset paper published at NAACL 2024 Read paper →

Research

2026

Plan-and-Patch: Diffusion Language Models for Agentic Planning

S. Kumar, J. Guo, Hassan Hamad, H. Kobayashi, Y. Xiang, Y. Yang, Y. Qi, D. Bonadiman, J. Jiang

NeurIPS 2026 Workshop on Diffusion Language Models

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2025

ToolCritic: Detecting and Correcting Tool-Use Errors in Dialogue Systems

Hassan Hamad, Y. Xu, L. Zhao, W. Yan, N. Gyanchandani

arXiv:2510.17052

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2025

Bitwidth-Specific Logarithmic Arithmetic for Future Hardware-Accelerated Training

Hassan Hamad, Y. Qiu, P.A. Beerel, K.M. Chugg

arXiv:2510.17058

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2024

FIRE: A Dataset for Financial Relation Extraction

Hassan Hamad, Abhinav Kumar Thakur, Nijil Kolleri, Sujith Pulikodan, Keith M. Chugg

NAACL 2024 Findings

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2022

Approximation Capabilities of Neural Networks using Morphological Perceptrons

William Chang, Hassan Hamad, Keith M. Chugg

Asilomar Conference 2022

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2019

Joint State Sensing and Communication over Memoryless Multiple Access Channels

Mari Kobayashi, Hassan Hamad, Gerhard Kramer, Giuseppe Caire

IEEE ISIT 2019

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2019

Performance Analysis of Turbo Codes over the Bernoulli-Gaussian Impulsive Noise Channel

Ghassan M Kraidy, Hassan Hamad

CWIT 2019

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2019

Small-Signal Analysis of High-Performance VCSELs

Wissam Hamad, Marwan Bou Sanayeh, Tobias Siepelmeyer, Hassan Hamad, Werner HE Hofmann

IEEE Photonics Journal

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2017

Performance Analysis of Convolutional Codes over the Bernoulli-Gaussian Impulsive Noise Channel

Hassan Hamad, Ghassan M Kraidy

CWIT 2017

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Blog