tiennm99/cv
1
0
Fork 0
mirror of https://github.com/tiennm99/cv.git synced 2026-09-03 18:16:43 +00:00
Code Issues Packages Projects Releases Wiki Activity

chore: init

Browse Source
This commit is contained in:
tiennm99
2026-02-20 08:53:49 +07:00
parent c1a78e01ea
commit 5c4c5fd2d0
8 changed files with 6677 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+248 -2
View File
@@ -1,2 +1,248 @@
# cv2
My new CV written with RenderCV
# miti99's CV
- Email: [john.doe@email.com](mailto:john.doe@email.com)
- Location: San Francisco, CA
- Website: [rendercv.com](https://rendercv.com/)
- LinkedIn: [rendercv](https://linkedin.com/in/rendercv)
- GitHub: [rendercv](https://github.com/rendercv)
# Welcome to RenderCV
RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.
See the [documentation](https://docs.rendercv.com) for more details.
# Education
## **Princeton University**, Computer Science
**PhD**
Princeton, NJ
Sept 2018 – May 2023
- Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment
- Advisor: Prof. Sanjeev Arora
- NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)
## **Boğaziçi University**, Computer Engineering
**BS**
Istanbul, Türkiye
Sept 2014 – June 2018
- GPA: 3.97/4.00, Valedictorian
- Fulbright Scholarship recipient for graduate studies
# Experience
## **Nexus AI**, Co-Founder & CTO
San Francisco, CA
June 2023 – present
2 years 9 months
- Built foundation model infrastructure serving 2M+ monthly API requests with 99.97% uptime
- Raised $18M Series A led by Sequoia Capital, with participation from a16z and Founders Fund
- Scaled engineering team from 3 to 28 across ML research, platform, and applied AI divisions
- Developed proprietary inference optimization reducing latency by 73% compared to baseline
## **NVIDIA Research**, Research Intern
Santa Clara, CA
May 2022 – Aug 2022
4 months
- Designed sparse attention mechanism reducing transformer memory footprint by 4.2x
- Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5% of submissions)
## **Google DeepMind**, Research Intern
London, UK
May 2021 – Aug 2021
4 months
- Developed reinforcement learning algorithms for multi-agent coordination
- Published research at top-tier venues with significant academic impact
- ICML 2022 main conference paper, cited 340+ times within two years
- NeurIPS 2022 workshop paper on emergent communication protocols
- Invited journal extension in JMLR (2023)
## **Apple ML Research**, Research Intern
Cupertino, CA
May 2020 – Aug 2020
4 months
- Created on-device neural network compression pipeline deployed across 50M+ devices
- Filed 2 patents on efficient model quantization techniques for edge inference
## **Microsoft Research**, Research Intern
Redmond, WA
May 2019 – Aug 2019
4 months
- Implemented novel self-supervised learning framework for low-resource language modeling
- Research integrated into Azure Cognitive Services, reducing training data requirements by 60%
# Projects
## **[FlashInfer](https://github.com/)**
Jan 2023 – present
Open-source library for high-performance LLM inference kernels
- Achieved 2.8x speedup over baseline attention implementations on A100 GPUs
- Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors
## **[NeuralPrune](https://github.com/)**
Jan 2021
Automated neural network pruning toolkit with differentiable masks
- Reduced model size by 90% with less than 1% accuracy degradation on ImageNet
- Featured in PyTorch ecosystem tools, 4,200+ GitHub stars
# Publications
## **Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter Models**
July 2023
*John Doe*, Sarah Williams, David Park
[10.1234/neurips.2023.1234](https://doi.org/10.1234/neurips.2023.1234) (NeurIPS 2023)
## **Neural Architecture Search via Differentiable Pruning**
Dec 2022
James Liu, *John Doe*
[10.1234/neurips.2022.5678](https://doi.org/10.1234/neurips.2022.5678) (NeurIPS 2022, Spotlight)
## **Multi-Agent Reinforcement Learning with Emergent Communication**
July 2022
Maria Garcia, *John Doe*, Tom Anderson
[10.1234/icml.2022.9012](https://doi.org/10.1234/icml.2022.9012) (ICML 2022)
## **On-Device Model Compression via Learned Quantization**
May 2021
*John Doe*, Kevin Wu
[10.1234/iclr.2021.3456](https://doi.org/10.1234/iclr.2021.3456) (ICLR 2021, Best Paper Award)
# Selected Honors
- MIT Technology Review 35 Under 35 Innovators (2024)
- Forbes 30 Under 30 in Enterprise Technology (2024)
- ACM Doctoral Dissertation Award Honorable Mention (2023)
- Google PhD Fellowship in Machine Learning (2020 – 2023)
- Fulbright Scholarship for Graduate Studies (2018)
# Skills
**Languages:** Python, C++, CUDA, Rust, Julia
**ML Frameworks:** PyTorch, JAX, TensorFlow, Triton, ONNX
**Infrastructure:** Kubernetes, Ray, distributed training, AWS, GCP
**Research Areas:** Neural architecture search, model compression, efficient inference, multi-agent RL
# Patents
1. Adaptive Quantization for Neural Network Inference on Edge Devices (US Patent 11,234,567)
1. Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)
1. Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)
# Invited Talks
1. Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)
1. Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)
1. From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)
1. Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)
# Any Section Title
You can use any section title you want.
You can choose any entry type for the section: `TextEntry`, `ExperienceEntry`, `EducationEntry`, `PublicationEntry`, `BulletEntry`, `NumberedEntry`, or `ReversedNumberedEntry`.
Markdown syntax is supported everywhere.
The `design` field in YAML gives you control over almost any aspect of your CV design.
See the [documentation](https://docs.rendercv.com) for more details.
index.html
+256
View File
@@ -0,0 +1,256 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta http-equiv="X-UA-Compatible" content="ie=edge">
<title>
</title>
<link rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.5.1/github-markdown-light.min.css"
integrity="sha512-Pmhg2i/F7+5+7SsdoUqKeH7UAZoVMYb1sxGOoJ0jWXAEHP0XV2H4CITyK267eHWp2jpj7rtqWNkmEOw1tNyYpg=="
crossorigin="anonymous" referrerpolicy="no-referrer" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.12/dist/katex.min.css" integrity="sha384-PDbUeNCuE6bOPudPOgFyIUEy3UJawJVwr3XlGO90FIuf5qNIoTLSgOJo/dC2ZXV/" crossorigin="anonymous">
<!-- The loading of KaTeX is deferred to speed up page rendering -->
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.12/dist/katex.min.js" integrity="sha384-VkqWq8xtm5YQk1BBXczQ8/Sx+DlCzF8cuS43bZwmtVXzRFtyLTqTCdP7MKmKo+KN" crossorigin="anonymous"></script>
<!-- To automatically render math in text elements, include the auto-render extension: -->
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.12/dist/contrib/auto-render.min.js" integrity="sha384-hCXGrW6PitJEwbkoStFjeJxv+fSOOQKOPbJxSfM6G5sWZjAyWhXiTIIAmQqnlLlh" crossorigin="anonymous"
onload="renderMathInElement(document.body, {delimiters: [{ left: '$$', right: '$$', display: false }]});"></script>
<style>
.markdown-body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
}
@media (max-width: 767px) {
.markdown-body {
padding: 15px;
}
}
</style>
</head>
<body>
<article class="markdown-body">
<h1>miti99's CV</h1>
<ul>
<li>Email: <a href="mailto:john.doe@email.com">john.doe@email.com</a></li>
<li>Location: San Francisco, CA</li>
<li>Website: <a href="https://rendercv.com/">rendercv.com</a></li>
<li>LinkedIn: <a href="https://linkedin.com/in/rendercv">rendercv</a></li>
<li>GitHub: <a href="https://github.com/rendercv">rendercv</a></li>
</ul>
<h1>Welcome to RenderCV</h1>
<p>RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.</p>
<p>See the <a href="https://docs.rendercv.com">documentation</a> for more details.</p>
<h1>Education</h1>
<h2><strong>Princeton University</strong>, Computer Science</h2>
<p><strong>PhD</strong></p>
<p>Princeton, NJ</p>
<p>Sept 2018 – May 2023</p>
<ul>
<li>
<p>Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment</p>
</li>
<li>
<p>Advisor: Prof. Sanjeev Arora</p>
</li>
<li>
<p>NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)</p>
</li>
</ul>
<h2><strong>Boğaziçi University</strong>, Computer Engineering</h2>
<p><strong>BS</strong></p>
<p>Istanbul, Türkiye</p>
<p>Sept 2014 – June 2018</p>
<ul>
<li>
<p>GPA: 3.97/4.00, Valedictorian</p>
</li>
<li>
<p>Fulbright Scholarship recipient for graduate studies</p>
</li>
</ul>
<h1>Experience</h1>
<h2><strong>Nexus AI</strong>, Co-Founder &amp; CTO</h2>
<p>San Francisco, CA</p>
<p>June 2023 – present</p>
<p>2 years 9 months</p>
<ul>
<li>
<p>Built foundation model infrastructure serving 2M+ monthly API requests with 99.97% uptime</p>
</li>
<li>
<p>Raised $18M Series A led by Sequoia Capital, with participation from a16z and Founders Fund</p>
</li>
<li>
<p>Scaled engineering team from 3 to 28 across ML research, platform, and applied AI divisions</p>
</li>
<li>
<p>Developed proprietary inference optimization reducing latency by 73% compared to baseline</p>
</li>
</ul>
<h2><strong>NVIDIA Research</strong>, Research Intern</h2>
<p>Santa Clara, CA</p>
<p>May 2022 – Aug 2022</p>
<p>4 months</p>
<ul>
<li>
<p>Designed sparse attention mechanism reducing transformer memory footprint by 4.2x</p>
</li>
<li>
<p>Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5% of submissions)</p>
</li>
</ul>
<h2><strong>Google DeepMind</strong>, Research Intern</h2>
<p>London, UK</p>
<p>May 2021 – Aug 2021</p>
<p>4 months</p>
<ul>
<li>
<p>Developed reinforcement learning algorithms for multi-agent coordination</p>
</li>
<li>
<p>Published research at top-tier venues with significant academic impact</p>
</li>
<li>
<p>ICML 2022 main conference paper, cited 340+ times within two years</p>
</li>
<li>
<p>NeurIPS 2022 workshop paper on emergent communication protocols</p>
</li>
<li>
<p>Invited journal extension in JMLR (2023)</p>
</li>
</ul>
<h2><strong>Apple ML Research</strong>, Research Intern</h2>
<p>Cupertino, CA</p>
<p>May 2020 – Aug 2020</p>
<p>4 months</p>
<ul>
<li>
<p>Created on-device neural network compression pipeline deployed across 50M+ devices</p>
</li>
<li>
<p>Filed 2 patents on efficient model quantization techniques for edge inference</p>
</li>
</ul>
<h2><strong>Microsoft Research</strong>, Research Intern</h2>
<p>Redmond, WA</p>
<p>May 2019 – Aug 2019</p>
<p>4 months</p>
<ul>
<li>
<p>Implemented novel self-supervised learning framework for low-resource language modeling</p>
</li>
<li>
<p>Research integrated into Azure Cognitive Services, reducing training data requirements by 60%</p>
</li>
</ul>
<h1>Projects</h1>
<h2><strong><a href="https://github.com/">FlashInfer</a></strong></h2>
<p>Jan 2023 – present</p>
<p>Open-source library for high-performance LLM inference kernels</p>
<ul>
<li>
<p>Achieved 2.8x speedup over baseline attention implementations on A100 GPUs</p>
</li>
<li>
<p>Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors</p>
</li>
</ul>
<h2><strong><a href="https://github.com/">NeuralPrune</a></strong></h2>
<p>Jan 2021</p>
<p>Automated neural network pruning toolkit with differentiable masks</p>
<ul>
<li>
<p>Reduced model size by 90% with less than 1% accuracy degradation on ImageNet</p>
</li>
<li>
<p>Featured in PyTorch ecosystem tools, 4,200+ GitHub stars</p>
</li>
</ul>
<h1>Publications</h1>
<h2><strong>Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter Models</strong></h2>
<p>July 2023</p>
<p><em>John Doe</em>, Sarah Williams, David Park</p>
<p><a href="https://doi.org/10.1234/neurips.2023.1234">10.1234/neurips.2023.1234</a> (NeurIPS 2023)</p>
<h2><strong>Neural Architecture Search via Differentiable Pruning</strong></h2>
<p>Dec 2022</p>
<p>James Liu, <em>John Doe</em></p>
<p><a href="https://doi.org/10.1234/neurips.2022.5678">10.1234/neurips.2022.5678</a> (NeurIPS 2022, Spotlight)</p>
<h2><strong>Multi-Agent Reinforcement Learning with Emergent Communication</strong></h2>
<p>July 2022</p>
<p>Maria Garcia, <em>John Doe</em>, Tom Anderson</p>
<p><a href="https://doi.org/10.1234/icml.2022.9012">10.1234/icml.2022.9012</a> (ICML 2022)</p>
<h2><strong>On-Device Model Compression via Learned Quantization</strong></h2>
<p>May 2021</p>
<p><em>John Doe</em>, Kevin Wu</p>
<p><a href="https://doi.org/10.1234/iclr.2021.3456">10.1234/iclr.2021.3456</a> (ICLR 2021, Best Paper Award)</p>
<h1>Selected Honors</h1>
<ul>
<li>
<p>MIT Technology Review 35 Under 35 Innovators (2024)</p>
</li>
<li>
<p>Forbes 30 Under 30 in Enterprise Technology (2024)</p>
</li>
<li>
<p>ACM Doctoral Dissertation Award Honorable Mention (2023)</p>
</li>
<li>
<p>Google PhD Fellowship in Machine Learning (2020 – 2023)</p>
</li>
<li>
<p>Fulbright Scholarship for Graduate Studies (2018)</p>
</li>
</ul>
<h1>Skills</h1>
<p><strong>Languages:</strong> Python, C++, CUDA, Rust, Julia</p>
<p><strong>ML Frameworks:</strong> PyTorch, JAX, TensorFlow, Triton, ONNX</p>
<p><strong>Infrastructure:</strong> Kubernetes, Ray, distributed training, AWS, GCP</p>
<p><strong>Research Areas:</strong> Neural architecture search, model compression, efficient inference, multi-agent RL</p>
<h1>Patents</h1>
<ol>
<li>
<p>Adaptive Quantization for Neural Network Inference on Edge Devices (US Patent 11,234,567)</p>
</li>
<li>
<p>Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)</p>
</li>
<li>
<p>Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)</p>
</li>
</ol>
<h1>Invited Talks</h1>
<ol>
<li>
<p>Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)</p>
</li>
<li>
<p>Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)</p>
</li>
<li>
<p>From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)</p>
</li>
<li>
<p>Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)</p>
</li>
</ol>
<h1>Any Section Title</h1>
<p>You can use any section title you want.</p>
<p>You can choose any entry type for the section: <code>TextEntry</code>, <code>ExperienceEntry</code>, <code>EducationEntry</code>, <code>PublicationEntry</code>, <code>BulletEntry</code>, <code>NumberedEntry</code>, or <code>ReversedNumberedEntry</code>.</p>
<p>Markdown syntax is supported everywhere.</p>
<p>The <code>design</code> field in YAML gives you control over almost any aspect of your CV design.</p>
<p>See the <a href="https://docs.rendercv.com">documentation</a> for more details.</p>
</article>
</body>
</html>
miti99.pdf
+5398
View File
File diff suppressed because it is too large Load Diff
miti99.typ
+407
View File
@@ -0,0 +1,407 @@
// Import the rendercv function and all the refactored components
#import "@preview/rendercv:0.1.0": *
// Apply the rendercv template with custom configuration
#show: rendercv.with(
name: "miti99",
footer: context { [#emph[miti99 -- #str(here().page())\/#str(counter(page).final().first())]] },
top-note: [ #emph[Last updated in Feb 2026] ],
locale-catalog-language: "en",
page-size: "us-letter",
page-top-margin: 0.7in,
page-bottom-margin: 0.7in,
page-left-margin: 0.7in,
page-right-margin: 0.7in,
page-show-footer: true,
page-show-top-note: true,
colors-body: rgb(0, 0, 0),
colors-name: rgb(0, 79, 144),
colors-headline: rgb(0, 79, 144),
colors-connections: rgb(0, 79, 144),
colors-section-titles: rgb(0, 79, 144),
colors-links: rgb(0, 79, 144),
colors-footer: rgb(128, 128, 128),
colors-top-note: rgb(128, 128, 128),
typography-line-spacing: 0.6em,
typography-alignment: "justified",
typography-date-and-location-column-alignment: right,
typography-font-family-body: "Source Sans 3",
typography-font-family-name: "Source Sans 3",
typography-font-family-headline: "Source Sans 3",
typography-font-family-connections: "Source Sans 3",
typography-font-family-section-titles: "Source Sans 3",
typography-font-size-body: 10pt,
typography-font-size-name: 30pt,
typography-font-size-headline: 10pt,
typography-font-size-connections: 10pt,
typography-font-size-section-titles: 1.4em,
typography-small-caps-name: false,
typography-small-caps-headline: false,
typography-small-caps-connections: false,
typography-small-caps-section-titles: false,
typography-bold-name: true,
typography-bold-headline: false,
typography-bold-connections: false,
typography-bold-section-titles: true,
links-underline: false,
links-show-external-link-icon: false,
header-alignment: center,
header-photo-width: 3.5cm,
header-space-below-name: 0.7cm,
header-space-below-headline: 0.7cm,
header-space-below-connections: 0.7cm,
header-connections-hyperlink: true,
header-connections-show-icons: true,
header-connections-display-urls-instead-of-usernames: false,
header-connections-separator: "",
header-connections-space-between-connections: 0.5cm,
section-titles-type: "with_partial_line",
section-titles-line-thickness: 0.5pt,
section-titles-space-above: 0.5cm,
section-titles-space-below: 0.3cm,
sections-allow-page-break: true,
sections-space-between-text-based-entries: 0.3em,
sections-space-between-regular-entries: 1.2em,
entries-date-and-location-width: 4.15cm,
entries-side-space: 0.2cm,
entries-space-between-columns: 0.1cm,
entries-allow-page-break: false,
entries-short-second-row: true,
entries-summary-space-left: 0cm,
entries-summary-space-above: 0cm,
entries-highlights-bullet: "•" ,
entries-highlights-nested-bullet: "•" ,
entries-highlights-space-left: 0.15cm,
entries-highlights-space-above: 0cm,
entries-highlights-space-between-items: 0cm,
entries-highlights-space-between-bullet-and-text: 0.5em,
date: datetime(
year: 2026,
month: 2,
day: 20,
),
)
= miti99
#connections(
[#connection-with-icon("location-dot")[San Francisco, CA]],
[#link("mailto:john.doe@email.com", icon: false, if-underline: false, if-color: false)[#connection-with-icon("envelope")[john.doe\@email.com]]],
[#link("https://rendercv.com/", icon: false, if-underline: false, if-color: false)[#connection-with-icon("link")[rendercv.com]]],
[#link("https://linkedin.com/in/rendercv", icon: false, if-underline: false, if-color: false)[#connection-with-icon("linkedin")[rendercv]]],
[#link("https://github.com/rendercv", icon: false, if-underline: false, if-color: false)[#connection-with-icon("github")[rendercv]]],
)
== Welcome to RenderCV
RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.
See the #link("https://docs.rendercv.com")[documentation] for more details.
== Education
#education-entry(
[
#strong[Princeton University], Computer Science
- Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment
- Advisor: Prof. Sanjeev Arora
- NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)
],
[
Princeton, NJ
Sept 2018 – May 2023
],
degree-column: [
#strong[PhD]
],
)
#education-entry(
[
#strong[Boğaziçi University], Computer Engineering
- GPA: 3.97\/4.00, Valedictorian
- Fulbright Scholarship recipient for graduate studies
],
[
Istanbul, Türkiye
Sept 2014 – June 2018
],
degree-column: [
#strong[BS]
],
)
== Experience
#regular-entry(
[
#strong[Nexus AI], Co-Founder & CTO
- Built foundation model infrastructure serving 2M+ monthly API requests with 99.97\% uptime
- Raised \$18M Series A led by Sequoia Capital, with participation from a16z and Founders Fund
- Scaled engineering team from 3 to 28 across ML research, platform, and applied AI divisions
- Developed proprietary inference optimization reducing latency by 73\% compared to baseline
],
[
San Francisco, CA
June 2023 – present
2 years 9 months
],
)
#regular-entry(
[
#strong[NVIDIA Research], Research Intern
- Designed sparse attention mechanism reducing transformer memory footprint by 4.2x
- Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5\% of submissions)
],
[
Santa Clara, CA
May 2022 – Aug 2022
4 months
],
)
#regular-entry(
[
#strong[Google DeepMind], Research Intern
- Developed reinforcement learning algorithms for multi-agent coordination
- Published research at top-tier venues with significant academic impact
- ICML 2022 main conference paper, cited 340+ times within two years
- NeurIPS 2022 workshop paper on emergent communication protocols
- Invited journal extension in JMLR (2023)
],
[
London, UK
May 2021 – Aug 2021
4 months
],
)
#regular-entry(
[
#strong[Apple ML Research], Research Intern
- Created on-device neural network compression pipeline deployed across 50M+ devices
- Filed 2 patents on efficient model quantization techniques for edge inference
],
[
Cupertino, CA
May 2020 – Aug 2020
4 months
],
)
#regular-entry(
[
#strong[Microsoft Research], Research Intern
- Implemented novel self-supervised learning framework for low-resource language modeling
- Research integrated into Azure Cognitive Services, reducing training data requirements by 60\%
],
[
Redmond, WA
May 2019 – Aug 2019
4 months
],
)
== Projects
#regular-entry(
[
#strong[#link("https://github.com/")[FlashInfer]]
#summary[Open-source library for high-performance LLM inference kernels]
- Achieved 2.8x speedup over baseline attention implementations on A100 GPUs
- Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors
],
[
Jan 2023 – present
],
)
#regular-entry(
[
#strong[#link("https://github.com/")[NeuralPrune]]
#summary[Automated neural network pruning toolkit with differentiable masks]
- Reduced model size by 90\% with less than 1\% accuracy degradation on ImageNet
- Featured in PyTorch ecosystem tools, 4,200+ GitHub stars
],
[
Jan 2021
],
)
== Publications
#regular-entry(
[
#strong[Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter Models]
#emph[John Doe], Sarah Williams, David Park
#link("https://doi.org/10.1234/neurips.2023.1234")[10.1234\/neurips.2023.1234] (NeurIPS 2023)
],
[
July 2023
],
)
#regular-entry(
[
#strong[Neural Architecture Search via Differentiable Pruning]
James Liu, #emph[John Doe]
#link("https://doi.org/10.1234/neurips.2022.5678")[10.1234\/neurips.2022.5678] (NeurIPS 2022, Spotlight)
],
[
Dec 2022
],
)
#regular-entry(
[
#strong[Multi-Agent Reinforcement Learning with Emergent Communication]
Maria Garcia, #emph[John Doe], Tom Anderson
#link("https://doi.org/10.1234/icml.2022.9012")[10.1234\/icml.2022.9012] (ICML 2022)
],
[
July 2022
],
)
#regular-entry(
[
#strong[On-Device Model Compression via Learned Quantization]
#emph[John Doe], Kevin Wu
#link("https://doi.org/10.1234/iclr.2021.3456")[10.1234\/iclr.2021.3456] (ICLR 2021, Best Paper Award)
],
[
May 2021
],
)
== Selected Honors
- MIT Technology Review 35 Under 35 Innovators (2024)
- Forbes 30 Under 30 in Enterprise Technology (2024)
- ACM Doctoral Dissertation Award Honorable Mention (2023)
- Google PhD Fellowship in Machine Learning (2020 – 2023)
- Fulbright Scholarship for Graduate Studies (2018)
== Skills
#strong[Languages:] Python, C++, CUDA, Rust, Julia
#strong[ML Frameworks:] PyTorch, JAX, TensorFlow, Triton, ONNX
#strong[Infrastructure:] Kubernetes, Ray, distributed training, AWS, GCP
#strong[Research Areas:] Neural architecture search, model compression, efficient inference, multi-agent RL
== Patents
+ Adaptive Quantization for Neural Network Inference on Edge Devices (US Patent 11,234,567)
+ Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)
+ Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)
== Invited Talks
#reversed-numbered-entries(
[
+ Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)
+ Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)
+ From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)
+ Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)
],
)
== Any Section Title
You can use any section title you want.
You can choose any entry type for the section: `TextEntry`, `ExperienceEntry`, `EducationEntry`, `PublicationEntry`, `BulletEntry`, `NumberedEntry`, or `ReversedNumberedEntry`.
Markdown syntax is supported everywhere.
The `design` field in YAML gives you control over almost any aspect of your CV design.
See the #link("https://docs.rendercv.com")[documentation] for more details.
miti99.yml
+368
View File
@@ -0,0 +1,368 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/rendercv/rendercv/refs/tags/v2.6/schema.json
cv:
name: miti99
headline:
location: San Francisco, CA
email: john.doe@email.com
photo:
phone:
website: https://rendercv.com/
social_networks:
- network: LinkedIn
username: rendercv
- network: GitHub
username: rendercv
custom_connections:
sections:
Welcome to RenderCV:
- RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.
- See the [documentation](https://docs.rendercv.com) for more details.
education:
- institution: Princeton University
area: Computer Science
degree: PhD
date:
start_date: 2018-09
end_date: 2023-05
location: Princeton, NJ
summary:
highlights:
- 'Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment'
- 'Advisor: Prof. Sanjeev Arora'
- NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)
- institution: Boğaziçi University
area: Computer Engineering
degree: BS
date:
start_date: 2014-09
end_date: 2018-06
location: Istanbul, Türkiye
summary:
highlights:
- 'GPA: 3.97/4.00, Valedictorian'
- Fulbright Scholarship recipient for graduate studies
experience:
- company: Nexus AI
position: Co-Founder & CTO
date:
start_date: 2023-06
end_date: present
location: San Francisco, CA
summary:
highlights:
- Built foundation model infrastructure serving 2M+ monthly API requests with 99.97% uptime
- Raised $18M Series A led by Sequoia Capital, with participation from a16z and Founders Fund
- Scaled engineering team from 3 to 28 across ML research, platform, and applied AI divisions
- Developed proprietary inference optimization reducing latency by 73% compared to baseline
- company: NVIDIA Research
position: Research Intern
date:
start_date: 2022-05
end_date: 2022-08
location: Santa Clara, CA
summary:
highlights:
- Designed sparse attention mechanism reducing transformer memory footprint by 4.2x
- Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5% of submissions)
- company: Google DeepMind
position: Research Intern
date:
start_date: 2021-05
end_date: 2021-08
location: London, UK
summary:
highlights:
- Developed reinforcement learning algorithms for multi-agent coordination
- Published research at top-tier venues with significant academic impact
- ICML 2022 main conference paper, cited 340+ times within two years
- NeurIPS 2022 workshop paper on emergent communication protocols
- Invited journal extension in JMLR (2023)
- company: Apple ML Research
position: Research Intern
date:
start_date: 2020-05
end_date: 2020-08
location: Cupertino, CA
summary:
highlights:
- Created on-device neural network compression pipeline deployed across 50M+ devices
- Filed 2 patents on efficient model quantization techniques for edge inference
- company: Microsoft Research
position: Research Intern
date:
start_date: 2019-05
end_date: 2019-08
location: Redmond, WA
summary:
highlights:
- Implemented novel self-supervised learning framework for low-resource language modeling
- Research integrated into Azure Cognitive Services, reducing training data requirements by 60%
projects:
- name: '[FlashInfer](https://github.com/)'
date:
start_date: 2023-01
end_date: present
location:
summary: Open-source library for high-performance LLM inference kernels
highlights:
- Achieved 2.8x speedup over baseline attention implementations on A100 GPUs
- Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors
- name: '[NeuralPrune](https://github.com/)'
date: '2021'
start_date:
end_date:
location:
summary: Automated neural network pruning toolkit with differentiable masks
highlights:
- Reduced model size by 90% with less than 1% accuracy degradation on ImageNet
- Featured in PyTorch ecosystem tools, 4,200+ GitHub stars
publications:
- title: 'Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter Models'
authors:
- '*John Doe*'
- Sarah Williams
- David Park
summary:
doi: 10.1234/neurips.2023.1234
url:
journal: NeurIPS 2023
date: 2023-07
- title: Neural Architecture Search via Differentiable Pruning
authors:
- James Liu
- '*John Doe*'
summary:
doi: 10.1234/neurips.2022.5678
url:
journal: NeurIPS 2022, Spotlight
date: 2022-12
- title: Multi-Agent Reinforcement Learning with Emergent Communication
authors:
- Maria Garcia
- '*John Doe*'
- Tom Anderson
summary:
doi: 10.1234/icml.2022.9012
url:
journal: ICML 2022
date: 2022-07
- title: On-Device Model Compression via Learned Quantization
authors:
- '*John Doe*'
- Kevin Wu
summary:
doi: 10.1234/iclr.2021.3456
url:
journal: ICLR 2021, Best Paper Award
date: 2021-05
selected_honors:
- bullet: MIT Technology Review 35 Under 35 Innovators (2024)
- bullet: Forbes 30 Under 30 in Enterprise Technology (2024)
- bullet: ACM Doctoral Dissertation Award Honorable Mention (2023)
- bullet: Google PhD Fellowship in Machine Learning (2020 – 2023)
- bullet: Fulbright Scholarship for Graduate Studies (2018)
skills:
- label: Languages
details: Python, C++, CUDA, Rust, Julia
- label: ML Frameworks
details: PyTorch, JAX, TensorFlow, Triton, ONNX
- label: Infrastructure
details: Kubernetes, Ray, distributed training, AWS, GCP
- label: Research Areas
details: Neural architecture search, model compression, efficient inference, multi-agent RL
patents:
- number: Adaptive Quantization for Neural Network Inference on Edge Devices (US Patent 11,234,567)
- number: Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)
- number: Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)
invited_talks:
- reversed_number: Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)
- reversed_number: Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)
- reversed_number: 'From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)'
- reversed_number: "Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)"
any_section_title:
- You can use any section title you want.
- 'You can choose any entry type for the section: `TextEntry`, `ExperienceEntry`, `EducationEntry`, `PublicationEntry`, `BulletEntry`, `NumberedEntry`, or `ReversedNumberedEntry`.'
- Markdown syntax is supported everywhere.
- The `design` field in YAML gives you control over almost any aspect of your CV design.
- See the [documentation](https://docs.rendercv.com) for more details.
design:
theme: classic
# page:
# size: us-letter
# top_margin: 0.7in
# bottom_margin: 0.7in
# left_margin: 0.7in
# right_margin: 0.7in
# show_footer: true
# show_top_note: true
# colors:
# body: rgb(0, 0, 0)
# name: rgb(0, 79, 144)
# headline: rgb(0, 79, 144)
# connections: rgb(0, 79, 144)
# section_titles: rgb(0, 79, 144)
# links: rgb(0, 79, 144)
# footer: rgb(128, 128, 128)
# top_note: rgb(128, 128, 128)
# typography:
# line_spacing: 0.6em
# alignment: justified
# date_and_location_column_alignment: right
# font_family:
# body: Source Sans 3
# name: Source Sans 3
# headline: Source Sans 3
# connections: Source Sans 3
# section_titles: Source Sans 3
# font_size:
# body: 10pt
# name: 30pt
# headline: 10pt
# connections: 10pt
# section_titles: 1.4em
# small_caps:
# name: false
# headline: false
# connections: false
# section_titles: false
# bold:
# name: true
# headline: false
# connections: false
# section_titles: true
# links:
# underline: false
# show_external_link_icon: false
# header:
# alignment: center
# photo_width: 3.5cm
# photo_position: left
# photo_space_left: 0.4cm
# photo_space_right: 0.4cm
# space_below_name: 0.7cm
# space_below_headline: 0.7cm
# space_below_connections: 0.7cm
# connections:
# phone_number_format: national
# hyperlink: true
# show_icons: true
# display_urls_instead_of_usernames: false
# separator: ''
# space_between_connections: 0.5cm
# section_titles:
# type: with_partial_line
# line_thickness: 0.5pt
# space_above: 0.5cm
# space_below: 0.3cm
# sections:
# allow_page_break: true
# space_between_regular_entries: 1.2em
# space_between_text_based_entries: 0.3em
# show_time_spans_in:
# - experience
# entries:
# date_and_location_width: 4.15cm
# side_space: 0.2cm
# space_between_columns: 0.1cm
# allow_page_break: false
# short_second_row: true
# summary:
# space_above: 0cm
# space_left: 0cm
# highlights:
# bullet: •
# nested_bullet: •
# space_left: 0.15cm
# space_above: 0cm
# space_between_items: 0cm
# space_between_bullet_and_text: 0.5em
# templates:
# footer: '*NAME -- PAGE_NUMBER/TOTAL_PAGES*'
# top_note: '*LAST_UPDATED CURRENT_DATE*'
# single_date: MONTH_ABBREVIATION YEAR
# date_range: START_DATE – END_DATE
# time_span: HOW_MANY_YEARS YEARS HOW_MANY_MONTHS MONTHS
# one_line_entry:
# main_column: '**LABEL:** DETAILS'
# education_entry:
# main_column: |-
# **INSTITUTION**, AREA
# SUMMARY
# HIGHLIGHTS
# degree_column: '**DEGREE**'
# date_and_location_column: |-
# LOCATION
# DATE
# normal_entry:
# main_column: |-
# **NAME**
# SUMMARY
# HIGHLIGHTS
# date_and_location_column: |-
# LOCATION
# DATE
# experience_entry:
# main_column: |-
# **COMPANY**, POSITION
# SUMMARY
# HIGHLIGHTS
# date_and_location_column: |-
# LOCATION
# DATE
# publication_entry:
# main_column: |-
# **TITLE**
# SUMMARY
# AUTHORS
# URL (JOURNAL)
# date_and_location_column: DATE
locale:
language: english
# last_updated: Last updated in
# month: month
# months: months
# year: year
# years: years
# present: present
# month_abbreviations:
# - Jan
# - Feb
# - Mar
# - Apr
# - May
# - June
# - July
# - Aug
# - Sept
# - Oct
# - Nov
# - Dec
# month_names:
# - January
# - February
# - March
# - April
# - May
# - June
# - July
# - August
# - September
# - October
# - November
# - December
settings:
current_date: '2026-02-20'
render_command:
design:
locale:
typst_path: NAME_IN_SNAKE_CASE.typ
pdf_path: NAME_IN_SNAKE_CASE.pdf
markdown_path: README.md
html_path: index.html
png_path: NAME_IN_SNAKE_CASE.png
dont_generate_markdown: false
dont_generate_html: false
dont_generate_typst: false
dont_generate_pdf: false
dont_generate_png: false
bold_keywords: []
miti99_1.png
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 730 KiB

miti99_2.png
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 704 KiB

miti99_3.png
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 153 KiB