Quentin Anthony

Computer Science · The Ohio State University

Early career · publishing since 2019Rising activity

Advanced Neural Network Applications Topic Modeling Natural Language Processing Techniques Stochastic Gradient Optimization Techniques Parallel Computing and Optimization Techniques

Publications

Citations

1,175

Est. group size

Recurring co-author estimate

Active years

Publishing since 2019

Research summary

AI-generated

Quentin Anthony works on the systems and engineering side of large language model (LLM) research, focusing on how to efficiently train and run these massive AI models across many computers and specialized hardware (like GPUs). His work covers topics such as distributed training methods, hardware-software co-design, model compression, and building large open datasets and benchmarks for training and evaluating LLMs. Prospective students would likely engage with practical, systems-oriented AI infrastructure problems rather than pure theoretical machine learning.

Large-scale distributed training of AI modelsHardware-software co-design for AI systemsModel architecture efficiency (e.g., attention, mixture-of-experts)Open datasets and benchmarks for language modelsCommunication and networking in distributed AI systems

Publication output grew steadily from 2017 through a peak in 2024, followed by a moderate decline in 2025-2026, though overall activity remains substantially higher than a decade ago.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence

Publications per year over the last 10 years — averaging 10.4/year recently

Recent publications

Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design
2026
Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet
arXiv (Cornell University) · 2026
Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet
arXiv (Cornell University) · 2026
Characterizing Communication Patterns in Distributed Large Language Model Inference
IEEE Micro · 2026
Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
arXiv (Cornell University) · 2025
CHIRP: A Fine-Grained Benchmark for Open-Ended Response Evaluation in Vision-Language Models
arXiv (Cornell University) · 2025
Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs
arXiv (Cornell University) · 2025
Compressed Convolutional Attention: Efficient Attention in a Compressed Latent Space
arXiv (Cornell University) · 2025
Characterizing Communication Patterns in Distributed Large Language Model Inference
2025
PyLO: Towards Accessible Learned Optimizers in PyTorch
arXiv (Cornell University) · 2025
Characterizing Communication Patterns in Distributed Large Language Model Inference
arXiv (Cornell University) · 2025
HARVEST Inference: Characterizing Digital Agriculture Workloads across Compute Continuum
2025
RedPajama: an Open Dataset for Training Large Language Models
arXiv (Cornell University) · 2024
Comparative Study of Large Language Model Architectures on Frontier
2024
Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference
2024

Publishes in

arXiv (Cornell University)×30
Lecture notes in computer science×3
IEEE Micro×2
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)×1

Similar researchers

By research-topic overlap

Lang Xu
Computer Science · The Ohio State University
Xupeng Miao
Computer Science · Purdue University West Lafayette
Arghadip Das
Computer Science · Purdue University West Lafayette
Peter Jin
Computer Science · Purdue University West Lafayette
Yunsheng Ma
Computer Science · Purdue University West Lafayette

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 19, 2026.

Claim or correct this profile