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feat: name research labs and professors (#5)
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‎src/data/experience.ts‎

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@@ -42,13 +42,13 @@ export const publication: Publication = {
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doi,
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paper: `https://doi.org/${doi}`,
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repo: 'https://github.com/UT-SysML/EnerTune',
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line: "EnerTune, from UT Austin's systems-for-ML group. I rebuilt the four GPU-sharing serving systems it is measured against and ported them to our 16-A100 cluster, enabling its 1.4-2.3x lower-energy result, and contributed baseline profiling to its 7.3x average profiling-time reduction."
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line: "EnerTune, from Yadwadkar's SysML Lab at UT Austin. I rebuilt the four GPU-sharing serving systems it is measured against and ported them to our 16-A100 cluster, enabling its 1.4-2.3x lower-energy result, and contributed baseline profiling to its 7.3x average profiling-time reduction."
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};
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export const experience: ExperienceEntry[] = [
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{
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org: 'UT Austin Research',
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role: 'Machine Learning for Systems',
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role: 'Machine Learning for Systems · LDOS Lab · Akella',
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period: 'Aug 2026 - Ongoing',
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track: 'research',
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bullets: [
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},
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{
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org: 'UT Austin Research',
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role: 'Systems for Machine Learning',
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role: 'Systems for Machine Learning · SysML Lab · Yadwadkar',
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period: 'Aug 2025 - May 2026',
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track: 'research',
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bullets: [
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},
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{
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org: 'UT Austin Research',
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role: 'Computer Architecture & FPGAs',
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role: 'Computer Architecture & FPGAs · FAST Lab · Chiou',
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period: 'Mar 2024 - Dec 2024',
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track: 'research',
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bullets: [

‎src/data/projects.ts‎

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@@ -60,7 +60,7 @@ export const projects: Project[] = [
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paper: publication.paper
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},
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description:
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"<strong>Third author on EnerTune (SOSP '26)</strong>, as an undergraduate research assistant in UT Austin's ECE systems-for-ML group. I rebuilt <strong>four GPU-sharing serving systems</strong> (GPULets, Usher, FGD, ParvaGPU) from their papers and ported each onto our <strong>16-A100 cluster</strong>, which is what made a fair comparison possible and enabled EnerTune's <strong>1.4-2.3x lower energy</strong> result. My baseline profiling also fed its <strong>7.3x average</strong> cut in profiling time.",
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"<strong>Third author on EnerTune (SOSP '26)</strong>, as an undergraduate research assistant in Yadwadkar's SysML Lab at UT Austin. I rebuilt <strong>four GPU-sharing serving systems</strong> (GPULets, Usher, FGD, ParvaGPU) from their papers and ported each onto our <strong>16-A100 cluster</strong>, which is what made a fair comparison possible and enabled EnerTune's <strong>1.4-2.3x lower energy</strong> result. My baseline profiling also fed its <strong>7.3x average</strong> cut in profiling time.",
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teamProject: true,
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caseStudy: {
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problem:
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tier: 'archived',
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title: 'Primate-Sim',
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summary:
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"An instruction-accurate RISC-V simulator built in UT's FAST group to validate the Primate compiler.",
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"An instruction-accurate RISC-V simulator built in Chiou's FAST Lab at UT Austin to validate the Primate compiler.",
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date: '2025-01',
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dateDisplay: 'January 2025',
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tags: ['Python', 'RISC-V', 'Simulation', 'FPGA', 'Compilers', 'Research'],
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github: 'https://github.com/FAST-Research-Group/primate-sim'
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},
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description:
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"Contributed to the <strong>FAST Research Group's Primate compiler</strong> work before the group concluded. As a research assistant I worked on an <strong>instruction-accurate RISC-V simulator</strong> the team used to validate the compiler: handling <strong>I/O instructions</strong>, building the tools that checked compiler output against the simulator, and writing a testing app that <strong>removed redundant FPGA synthesis runs</strong> so the hardware and software sides could be developed side by side instead of waiting on each other.",
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"Contributed to <strong>Primate compiler</strong> work in <strong>Chiou's FAST Lab at UT Austin</strong> before the group concluded. As a research assistant I worked on an <strong>instruction-accurate RISC-V simulator</strong> the team used to validate the compiler: handling <strong>I/O instructions</strong>, building the tools that checked compiler output against the simulator, and writing a testing app that <strong>removed redundant FPGA synthesis runs</strong> so the hardware and software sides could be developed side by side instead of waiting on each other.",
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teamProject: true
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},
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{

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