Resume

Sammy Thomas

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Education

2021—Present

Ph.D, UT Austin, 4th year.

Working with James Bornholt. Studying compilers for hardware design from a programming languages perspective

2016—2020

B.S, Cornell University, Ithaca.

Major in Computer Science, Concetration in Linguistics

2012—2016

High School Diploma, John Marshall HS, Los Angeles.

Graduated with High Honors

Publications

ASPLOS '24Best Paper!
ASPLOS '21

A Compiler Infrastructure for Accelerator Generators

Samuel Thomas, Rachit Nigam, Zhijing Li, Adrian Sampson

[ Github ] [ Website ]

PLDI '20

Predictable Accelerator Design with Time-Sensitive Affine Types

Rachit Nigam, Sachille Atapattu, Samuel Thomas, Zhijing Li, Ted Bauer, Yuwei Ye, Apurva Koti, Adrian Sampson, Zhiru Zhang

[ Github ] [ Website ]

Experience

2019 Summer—2021

Capra, Cornell.

Lead the Calyx project, a novel intermediate language and infrastructure that separates the structure of a program from the control of the program to enable more simple construction of software-hardware compilers. https://github.com/cucapra/calyx.

  • First author on paper submitted to ASPLOS 2021.
  • Developed working infrastructure that generate synthesizable RTL.
  • Organize a team of six people.
  • Work closely with undergraduate researchers to help introduce them to Computer Science research.

Worked on Dahlia, a programming language that uses affine types to model hardware resources.

  • Helped to write the paper was accepted to PLDI 2020.
  • Ran extensive experiments comparing Dahlia to other HLS tools.
  • Helped write the Dahlia compiler.
2018—2020

Teaching Assistant, Cornell.

Teaching assistant for CS 3110, a class on functional programming in OCaml, for three semesters. Taught a discussion section for two semesters. Helped rewrite a major assignment for the class.

2018 Summer

Information Science Institute, USC.

Worked with Greg Ver Steeg on meta machine learning problems. https://github.com/sgpthomas/sklearn-pmlb-benchmarks.

  • Design a system to scalably run machine learning experiments across hundreds of machines.
  • Reproduce the results from the Penn ML Benchmark suite.
  • Extend the metrics gathered from the Penn ML Benchmark suite to enable analysis of generalization error in machine learning algorithms.

Used the Penn ML Benchmark to gather large amounts of data on the performance of different machine learning algorithms.

2017 Summer

Network Systems Laboratory, USC.

Worked with Wyatt Loyd on DSEF, the Distributed Systems Experimental Framework, a framework for improving the reproducability of Distributed Systems experiments. https://github.com/DSEF.