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256 lines
11 KiB
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<article class="markdown-body">
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<h1>miti99's CV</h1>
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<ul>
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<li>Email: <a href="mailto:john.doe@email.com">john.doe@email.com</a></li>
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<li>Location: San Francisco, CA</li>
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<li>Website: <a href="https://rendercv.com/">rendercv.com</a></li>
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<li>LinkedIn: <a href="https://linkedin.com/in/rendercv">rendercv</a></li>
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<li>GitHub: <a href="https://github.com/rendercv">rendercv</a></li>
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</ul>
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<h1>Welcome to RenderCV</h1>
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<p>RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.</p>
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<p>See the <a href="https://docs.rendercv.com">documentation</a> for more details.</p>
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<h1>Education</h1>
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<h2><strong>Princeton University</strong>, Computer Science</h2>
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<p><strong>PhD</strong></p>
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<p>Princeton, NJ</p>
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<p>Sept 2018 – May 2023</p>
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<ul>
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<li>
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<p>Thesis: Efficient Neural Architecture Search for Resource-Constrained Deployment</p>
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</li>
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<li>
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<p>Advisor: Prof. Sanjeev Arora</p>
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</li>
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<li>
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<p>NSF Graduate Research Fellowship, Siebel Scholar (Class of 2022)</p>
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</li>
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</ul>
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<h2><strong>Boğaziçi University</strong>, Computer Engineering</h2>
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<p><strong>BS</strong></p>
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<p>Istanbul, Türkiye</p>
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<p>Sept 2014 – June 2018</p>
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<ul>
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<li>
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<p>GPA: 3.97/4.00, Valedictorian</p>
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</li>
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<li>
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<p>Fulbright Scholarship recipient for graduate studies</p>
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</li>
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</ul>
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<h1>Experience</h1>
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<h2><strong>Nexus AI</strong>, Co-Founder & CTO</h2>
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<p>San Francisco, CA</p>
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<p>June 2023 – present</p>
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<p>2 years 9 months</p>
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<ul>
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<li>
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<p>Built foundation model infrastructure serving 2M+ monthly API requests with 99.97% uptime</p>
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</li>
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<li>
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<p>Raised $18M Series A led by Sequoia Capital, with participation from a16z and Founders Fund</p>
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</li>
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<li>
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<p>Scaled engineering team from 3 to 28 across ML research, platform, and applied AI divisions</p>
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</li>
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<li>
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<p>Developed proprietary inference optimization reducing latency by 73% compared to baseline</p>
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</li>
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</ul>
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<h2><strong>NVIDIA Research</strong>, Research Intern</h2>
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<p>Santa Clara, CA</p>
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<p>May 2022 – Aug 2022</p>
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<p>4 months</p>
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<ul>
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<li>
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<p>Designed sparse attention mechanism reducing transformer memory footprint by 4.2x</p>
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</li>
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<li>
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<p>Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5% of submissions)</p>
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</li>
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</ul>
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<h2><strong>Google DeepMind</strong>, Research Intern</h2>
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<p>London, UK</p>
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<p>May 2021 – Aug 2021</p>
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<p>4 months</p>
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<ul>
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<li>
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<p>Developed reinforcement learning algorithms for multi-agent coordination</p>
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</li>
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<li>
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<p>Published research at top-tier venues with significant academic impact</p>
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</li>
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<li>
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<p>ICML 2022 main conference paper, cited 340+ times within two years</p>
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</li>
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<li>
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<p>NeurIPS 2022 workshop paper on emergent communication protocols</p>
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</li>
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<li>
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<p>Invited journal extension in JMLR (2023)</p>
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</li>
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</ul>
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<h2><strong>Apple ML Research</strong>, Research Intern</h2>
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<p>Cupertino, CA</p>
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<p>May 2020 – Aug 2020</p>
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<p>4 months</p>
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<ul>
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<li>
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<p>Created on-device neural network compression pipeline deployed across 50M+ devices</p>
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</li>
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<li>
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<p>Filed 2 patents on efficient model quantization techniques for edge inference</p>
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</li>
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</ul>
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<h2><strong>Microsoft Research</strong>, Research Intern</h2>
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<p>Redmond, WA</p>
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<p>May 2019 – Aug 2019</p>
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<p>4 months</p>
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<ul>
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<li>
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<p>Implemented novel self-supervised learning framework for low-resource language modeling</p>
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</li>
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<li>
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<p>Research integrated into Azure Cognitive Services, reducing training data requirements by 60%</p>
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</li>
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</ul>
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<h1>Projects</h1>
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<h2><strong><a href="https://github.com/">FlashInfer</a></strong></h2>
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<p>Jan 2023 – present</p>
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<p>Open-source library for high-performance LLM inference kernels</p>
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<ul>
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<li>
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<p>Achieved 2.8x speedup over baseline attention implementations on A100 GPUs</p>
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</li>
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<li>
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<p>Adopted by 3 major AI labs, 8,500+ GitHub stars, 200+ contributors</p>
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</li>
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</ul>
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<h2><strong><a href="https://github.com/">NeuralPrune</a></strong></h2>
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<p>Jan 2021</p>
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<p>Automated neural network pruning toolkit with differentiable masks</p>
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<ul>
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<li>
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<p>Reduced model size by 90% with less than 1% accuracy degradation on ImageNet</p>
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</li>
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<li>
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<p>Featured in PyTorch ecosystem tools, 4,200+ GitHub stars</p>
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</li>
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</ul>
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<h1>Publications</h1>
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<h2><strong>Sparse Mixture-of-Experts at Scale: Efficient Routing for Trillion-Parameter Models</strong></h2>
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<p>July 2023</p>
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<p><em>John Doe</em>, Sarah Williams, David Park</p>
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<p><a href="https://doi.org/10.1234/neurips.2023.1234">10.1234/neurips.2023.1234</a> (NeurIPS 2023)</p>
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<h2><strong>Neural Architecture Search via Differentiable Pruning</strong></h2>
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<p>Dec 2022</p>
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<p>James Liu, <em>John Doe</em></p>
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<p><a href="https://doi.org/10.1234/neurips.2022.5678">10.1234/neurips.2022.5678</a> (NeurIPS 2022, Spotlight)</p>
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<h2><strong>Multi-Agent Reinforcement Learning with Emergent Communication</strong></h2>
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<p>July 2022</p>
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<p>Maria Garcia, <em>John Doe</em>, Tom Anderson</p>
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<p><a href="https://doi.org/10.1234/icml.2022.9012">10.1234/icml.2022.9012</a> (ICML 2022)</p>
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<h2><strong>On-Device Model Compression via Learned Quantization</strong></h2>
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<p>May 2021</p>
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<p><em>John Doe</em>, Kevin Wu</p>
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<p><a href="https://doi.org/10.1234/iclr.2021.3456">10.1234/iclr.2021.3456</a> (ICLR 2021, Best Paper Award)</p>
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<h1>Selected Honors</h1>
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<ul>
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<li>
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<p>MIT Technology Review 35 Under 35 Innovators (2024)</p>
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</li>
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<li>
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<p>Forbes 30 Under 30 in Enterprise Technology (2024)</p>
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</li>
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<li>
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<p>ACM Doctoral Dissertation Award Honorable Mention (2023)</p>
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</li>
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<li>
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<p>Google PhD Fellowship in Machine Learning (2020 – 2023)</p>
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</li>
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<li>
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<p>Fulbright Scholarship for Graduate Studies (2018)</p>
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</li>
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</ul>
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<h1>Skills</h1>
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<p><strong>Languages:</strong> Python, C++, CUDA, Rust, Julia</p>
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<p><strong>ML Frameworks:</strong> PyTorch, JAX, TensorFlow, Triton, ONNX</p>
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<p><strong>Infrastructure:</strong> Kubernetes, Ray, distributed training, AWS, GCP</p>
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<p><strong>Research Areas:</strong> Neural architecture search, model compression, efficient inference, multi-agent RL</p>
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<h1>Patents</h1>
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<ol>
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<li>
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<p>Adaptive Quantization for Neural Network Inference on Edge Devices (US Patent 11,234,567)</p>
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</li>
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<li>
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<p>Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)</p>
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</li>
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<li>
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<p>Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)</p>
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</li>
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</ol>
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<h1>Invited Talks</h1>
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<ol>
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<li>
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<p>Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)</p>
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</li>
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<li>
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<p>Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)</p>
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</li>
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<li>
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<p>From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)</p>
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</li>
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<li>
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<p>Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)</p>
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</li>
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</ol>
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<h1>Any Section Title</h1>
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<p>You can use any section title you want.</p>
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<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>
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<p>Markdown syntax is supported everywhere.</p>
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<p>The <code>design</code> field in YAML gives you control over almost any aspect of your CV design.</p>
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<p>See the <a href="https://docs.rendercv.com">documentation</a> for more details.</p>
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