# 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**, PhD in Computer Science -- Princeton, NJSept 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**, BS in Computer Engineering -- Istanbul, TürkiyeSept 2014 – June 2018 - GPA: 3.97/4.00, Valedictorian - Fulbright Scholarship recipient for graduate studies # Experience ## **Co-Founder & CTO**, Nexus AI -- San Francisco, CA June 2023 – present - 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 ## **Research Intern**, NVIDIA Research -- Santa Clara, CA May 2022 – Aug 2022 - Designed sparse attention mechanism reducing transformer memory footprint by 4.2x - Co-authored paper accepted at NeurIPS 2022 (spotlight presentation, top 5% of submissions) ## **Research Intern**, Google DeepMind -- London, UK May 2021 – Aug 2021 - 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) ## **Research Intern**, Apple ML Research -- Cupertino, CA May 2020 – Aug 2020 - Created on-device neural network compression pipeline deployed across 50M+ devices - Filed 2 patents on efficient model quantization techniques for edge inference ## **Research Intern**, Microsoft Research -- Redmond, WA May 2019 – Aug 2019 - 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.