miti99's CV

• Email: john.doe@email.com
• Location: San Francisco, CA
• Website: rendercv.com
• LinkedIn: rendercv
• GitHub: rendercv

Welcome to RenderCV

RenderCV reads a CV written in a YAML file, and generates a PDF with professional typography.

See the documentation 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

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

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 (NeurIPS 2023)

Neural Architecture Search via Differentiable Pruning

Dec 2022

James Liu, John Doe

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 (ICML 2022)

On-Device Model Compression via Learned Quantization

May 2021

John Doe, Kevin Wu

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)

2. Dynamic Sparsity Patterns for Efficient Transformer Attention (US Patent 11,345,678)

3. Hardware-Aware Neural Architecture Search Method (US Patent 11,456,789)

Invited Talks

1. Scaling Laws for Efficient Inference — Stanford HAI Symposium (2024)

2. Building AI Infrastructure for the Next Decade — TechCrunch Disrupt (2024)

3. From Research to Production: Lessons in ML Systems — NeurIPS Workshop (2023)

4. Efficient Deep Learning: A Practitioner's Perspective — Google Tech Talk (2022)

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