mirror of
https://github.com/rendercv/rendercv.git
synced 2026-04-23 08:24:21 -04:00
399 lines
10 KiB
Typst
399 lines
10 KiB
Typst
// Import the rendercv function and all the refactored components
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#import "@preview/rendercv:0.3.0": *
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// Apply the rendercv template with custom configuration
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#show: rendercv.with(
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name: "John Doe",
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title: "John Doe - CV",
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footer: context { [#emph[John Doe -- #str(here().page())\/#str(counter(page).final().first())]] },
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top-note: [ #emph[Last updated in Mar 2026] ],
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locale-catalog-language: "en",
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text-direction: ltr,
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page-size: "us-letter",
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page-top-margin: 0.7in,
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page-bottom-margin: 0.7in,
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page-left-margin: 0.7in,
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page-right-margin: 0.7in,
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page-show-footer: true,
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page-show-top-note: true,
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colors-body: rgb(0, 0, 0),
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colors-name: rgb(0, 79, 144),
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colors-headline: rgb(0, 79, 144),
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colors-connections: rgb(0, 79, 144),
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colors-section-titles: rgb(0, 79, 144),
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colors-links: rgb(0, 79, 144),
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colors-footer: rgb(128, 128, 128),
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colors-top-note: rgb(128, 128, 128),
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typography-line-spacing: 0.6em,
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typography-alignment: "justified",
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typography-date-and-location-column-alignment: right,
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typography-font-family-body: "Raleway",
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typography-font-family-name: "Raleway",
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typography-font-family-headline: "Raleway",
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typography-font-family-connections: "Raleway",
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typography-font-family-section-titles: "Raleway",
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typography-font-size-body: 10pt,
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typography-font-size-name: 30pt,
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typography-font-size-headline: 10pt,
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typography-font-size-connections: 10pt,
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typography-font-size-section-titles: 1.4em,
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typography-small-caps-name: false,
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typography-small-caps-headline: false,
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typography-small-caps-connections: false,
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typography-small-caps-section-titles: false,
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typography-bold-name: false,
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typography-bold-headline: false,
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typography-bold-connections: false,
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typography-bold-section-titles: false,
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links-underline: false,
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links-show-external-link-icon: false,
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header-alignment: left,
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header-photo-width: 3.5cm,
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header-space-below-name: 0.7cm,
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header-space-below-headline: 0.7cm,
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header-space-below-connections: 0.7cm,
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header-connections-hyperlink: true,
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header-connections-show-icons: true,
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header-connections-display-urls-instead-of-usernames: false,
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header-connections-separator: "",
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header-connections-space-between-connections: 0.5cm,
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section-titles-type: "with_full_line",
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section-titles-line-thickness: 0.5pt,
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section-titles-space-above: 0.5cm,
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section-titles-space-below: 0.3cm,
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sections-allow-page-break: true,
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sections-space-between-text-based-entries: 0.3em,
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sections-space-between-regular-entries: 1.2em,
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entries-date-and-location-width: 4.15cm,
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entries-side-space: 0.2cm,
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entries-space-between-columns: 0.1cm,
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entries-allow-page-break: false,
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entries-short-second-row: false,
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entries-degree-width: 1cm,
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entries-summary-space-left: 0cm,
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entries-summary-space-above: 0.12cm,
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entries-highlights-bullet: "•" ,
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entries-highlights-nested-bullet: "•" ,
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entries-highlights-space-left: 0cm,
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entries-highlights-space-above: 0.12cm,
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entries-highlights-space-between-items: 0.12cm,
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entries-highlights-space-between-bullet-and-text: 0.5em,
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date: datetime(
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year: 2026,
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month: 3,
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day: 20,
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),
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)
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= John Doe
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#connections(
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[#connection-with-icon("location-dot")[San Francisco, CA]],
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[#link("mailto:john.doe@email.com", icon: false, if-underline: false, if-color: false)[#connection-with-icon("envelope")[john.doe\@email.com]]],
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[#link("https://rendercv.com/", icon: false, if-underline: false, if-color: false)[#connection-with-icon("link")[rendercv.com]]],
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[#link("https://linkedin.com/in/rendercv", icon: false, if-underline: false, if-color: false)[#connection-with-icon("linkedin")[rendercv]]],
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[#link("https://github.com/rendercv", icon: false, if-underline: false, if-color: false)[#connection-with-icon("github")[rendercv]]],
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)
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== Welcome to RenderCV
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RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.
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Each section title is arbitrary.
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You can choose any of the 9 entry types for each section.
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Markdown syntax is supported everywhere. This is #strong[bold], #emph[italic], and #link("https://example.com")[link].
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== Education
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#education-entry(
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[
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#strong[Princeton University], PhD in Computer Science -- Princeton, NJ
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],
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[
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Sept 2018 – May 2023
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],
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main-column-second-row: [
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- Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment
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- Advisor: Prof. Sanjeev Arora
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- NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)
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],
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)
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#education-entry(
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[
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#strong[Boğaziçi University], BS in Computer Engineering -- Istanbul, Türkiye
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],
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[
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Sept 2014 – June 2018
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],
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main-column-second-row: [
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- GPA: 3.97\/4.00, Valedictorian
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- Fulbright Scholarship recipient for Graduate Studies
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],
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)
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== Experience
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#regular-entry(
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[
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#strong[Co-Founder & CTO], Nexus AI -- San Francisco, CA
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],
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[
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June 2023 – present
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],
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main-column-second-row: [
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- Built foundation model infrastructure serving 2M+ monthly API requests with 99.97\% uptime
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- Raised \$18M Series A led by Sequoia Capital, with participation from a16z and Founders Fund
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- Scaled engineering team from 3 to 28 across ML research, platform, and applied AI divisions
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- Developed proprietary inference optimization reducing latency by 73\% compared to baseline
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],
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)
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#regular-entry(
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[
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#strong[Research Intern], NVIDIA Research -- Santa Clara, CA
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],
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[
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May 2022 – Aug 2022
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],
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main-column-second-row: [
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- Designed sparse attention mechanism reducing transformer memory footprint by 4.2x
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- Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5\% of submissions)
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],
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)
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#regular-entry(
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[
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#strong[Research Intern], Google DeepMind -- London, UK
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],
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[
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May 2021 – Aug 2021
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],
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main-column-second-row: [
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- Developed reinforcement learning algorithms for multi-agent coordination
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- Published research at top-tier venues with significant academic impact
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- ICML 2022 main conference paper, cited 340+ times within two years
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- NeurIPS 2022 workshop paper on emergent communication protocols
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- Invited journal extension in JMLR (2023)
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],
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)
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#regular-entry(
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[
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#strong[Research Intern], Apple ML Research -- Cupertino, CA
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],
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[
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May 2020 – Aug 2020
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],
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main-column-second-row: [
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- Created on-device neural network compression pipeline deployed across 50M+ devices
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- Filed 2 patents on efficient model quantization techniques for edge inference
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],
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)
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#regular-entry(
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[
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#strong[Research Intern], Microsoft Research -- Redmond, WA
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],
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[
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May 2019 – Aug 2019
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],
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main-column-second-row: [
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- Implemented novel self-supervised learning framework for low-resource language modeling
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- Research integrated into Azure Cognitive Services, reducing training data requirements by 60\%
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],
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)
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== Projects
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#regular-entry(
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[
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#strong[#link("https://github.com/")[FlashInfer]]
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],
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[
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Jan 2023 – present
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],
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main-column-second-row: [
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#summary[Open-source library for high-performance LLM inference kernels]
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- Achieved 2.8x speedup over baseline attention implementations on A100 GPUs
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- Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors
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],
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)
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#regular-entry(
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[
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#strong[#link("https://github.com/")[NeuralPrune]]
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],
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[
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Jan 2021
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],
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main-column-second-row: [
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#summary[Automated neural network pruning toolkit with differentiable masks]
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- Reduced model size by 90\% with less than 1\% accuracy degradation on ImageNet
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- Featured in PyTorch ecosystem tools, 4,200+ GitHub stars
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],
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)
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== Publications
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#regular-entry(
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[
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#strong[Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter Models]
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],
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[
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July 2023
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],
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main-column-second-row: [
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#emph[John Doe], Sarah Williams, David Park
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#link("https://doi.org/10.1234/neurips.2023.1234")[10.1234\/neurips.2023.1234] (NeurIPS 2023)
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],
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)
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#regular-entry(
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[
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#strong[Neural Architecture Search via Differentiable Pruning]
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],
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[
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Dec 2022
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],
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main-column-second-row: [
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James Liu, #emph[John Doe]
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#link("https://doi.org/10.1234/neurips.2022.5678")[10.1234\/neurips.2022.5678] (NeurIPS 2022, Spotlight)
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],
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)
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#regular-entry(
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[
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#strong[Multi-Agent Reinforcement Learning with Emergent Communication]
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],
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[
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July 2022
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],
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main-column-second-row: [
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Maria Garcia, #emph[John Doe], Tom Anderson
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#link("https://doi.org/10.1234/icml.2022.9012")[10.1234\/icml.2022.9012] (ICML 2022)
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],
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)
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#regular-entry(
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[
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#strong[On-Device Model Compression via Learned Quantization]
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],
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[
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May 2021
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],
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main-column-second-row: [
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#emph[John Doe], Kevin Wu
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#link("https://doi.org/10.1234/iclr.2021.3456")[10.1234\/iclr.2021.3456] (ICLR 2021, Best Paper Award)
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],
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)
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== Selected Honors
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- MIT Technology Review 35 Under 35 Innovators (2024)
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- Forbes 30 Under 30 in Enterprise Technology (2024)
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- ACM Doctoral Dissertation Award Honorable Mention (2023)
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- Google PhD Fellowship in Machine Learning (2020 – 2023)
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- Fulbright Scholarship for Graduate Studies (2018)
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== Skills
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#strong[Languages:] Python, C++, CUDA, Rust, Julia
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#strong[ML Frameworks:] PyTorch, JAX, TensorFlow, Triton, ONNX
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#strong[Infrastructure:] Kubernetes, Ray, distributed training, AWS, GCP
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#strong[Research Areas:] Neural architecture search, model compression, efficient inference, multi-agent RL
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== Patents
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+ Adaptive Quantization for Neural Network Inference on Edge Devices (US Patent 11,234,567)
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+ Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)
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+ Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)
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== Invited Talks
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#reversed-numbered-entries(
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[
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+ Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)
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+ Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)
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+ From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)
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+ Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)
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],
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)
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