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cv:
  name: John Doe
  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:
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locale:
  language: english
  # last_updated: Last updated in
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settings:
  current_date: '2025-12-22'
  render_command:
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    pdf_path: rendercv_output/NAME_IN_SNAKE_CASE_CV.pdf
    markdown_path: rendercv_output/NAME_IN_SNAKE_CASE_CV.md
    html_path: rendercv_output/NAME_IN_SNAKE_CASE_CV.html
    png_path: rendercv_output/NAME_IN_SNAKE_CASE_CV.png
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  bold_keywords: []