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2022

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2022

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A profile about the people, projects and experiences behind their work. Discover their career, contributions and connections.

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2022

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Arya Tschand

Arya Tschand leads inference research at Modal, where he works on improving how machine-learning models run. His research focuses on hardware-aware kernel optimization across GPUs and TPUs, including SwizzlePerf and JAXBench; he previously pursued a computer science PhD at Harvard after studying electrical and computer engineering and computer science at Duke.

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Sep 2026 — Present

Inference Research

Harvard University

Computer Science PhD

Duke

double major in Electrical & Computer Engineering and Computer Science

Products & media

aryatschand.com

Comparing Nvidia GPU vs AMD GPU vs Google TPU Architectures from a Programmer's Perspective

A programmer-focused comparison of accelerator architectures and how their design choices shape kernel optimization.

Writing

aryatschand.com

FantasyFootballBench: Evaluating LLM Negotiation and Long-Horizon Decision Making with Fantasy Football

An account of a benchmark that evaluates language models through a season-long fantasy football competition.

Writing

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Arya Tschand

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Harvard University

Duke

Products & media

Comparing Nvidia GPU vs AMD GPU vs Google TPU Architectures from a Programmer's Perspective

FantasyFootballBench: Evaluating LLM Negotiation and Long-Horizon Decision Making with Fantasy Football

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Arya Tschand

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Harvard University

Duke

Products & media

Comparing Nvidia GPU vs AMD GPU vs Google TPU Architectures from a Programmer's Perspective

FantasyFootballBench: Evaluating LLM Negotiation and Long-Horizon Decision Making with Fantasy Football

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