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Currently, I'm a PhD student at Berkeley Sky Computing Lab for machine learning system and cloud infrastructures. I am advised by Prof. Joseph Gonzalez and Prof. Ion Stoica.

My latest focus is building an end to end stack for LLM inference on your own infrastructure:

• vLLM runs LLM inference efficiently.

Previous exploration includes:

• Conex: builds, push, and pull containers fast.
• SkyATC: orchestrate LLMs in multi-cloud and scaling them to zero.

I previously work on Model Serving System @anyscale.

• Ray takes your Python code and scale it to thousands of cores.
• Ray Serve empowers data scientists to own their end-to-end inference APIs.

Before Anyscale, I was a undergraduate researcher @ucbrise.

Publications:

• Under submission: Optimizing LLM Queries in Relational Workloads
• NSDI 2024: Cloudcast: High-Throughput, Cost-Aware Overlay Multicast in the Cloud plan the best network for cloud object store replications.
• VLDB 2024: RALF: Accuracy-Aware Scheduling for Feature Store Maintenance proposes feature update in feature store can be a lot more efficient.
• SoCC 2020: InferLine: ML Inference Pipeline Composition Framework studies how to optimize model serving pipelines.
• VLDB 2020: Towards Scalable Dataframe Systems formalizes Pandas DataFrame.
• SysML Workshop @ Neurips 2018: The OoO VLIW JIT Compiler for GPU Inference tries to multiplex many kernels on the same GPU.

Reach out to me: simon.mo at hey.com