Dr. Sarah Chen

Dr. Sarah Chen

Chief AI Officer

Contact Sarah

Quick Info

  • San Francisco, CA
  • NeuraX Since 2019
  • PhD in Computer Science, MIT
  • sarah.chen@neurax.ai
50+
Papers
12K
Citations
15+
Years

About

Dr. Sarah Chen is the Chief AI Officer at NeuraX, where she leads our research and development initiatives in machine learning and artificial intelligence. With over 15 years of experience in the field, she has been instrumental in developing groundbreaking AI solutions that have transformed industries.

Before joining NeuraX, Sarah served as a Senior Research Scientist at Google AI, where she led teams working on natural language processing and computer vision. Her work has been published in top-tier conferences including NeurIPS, ICML, and CVPR, accumulating over 12,000 citations.

Sarah holds a PhD in Computer Science from MIT, where she focused on deep learning architectures for multimodal understanding. She is also an adjunct professor at Stanford University, teaching graduate courses in machine learning and mentoring the next generation of AI researchers.

Expertise

Machine Learning Deep Learning Natural Language Processing Computer Vision Transformer Models AI Ethics & Safety Multimodal AI Neural Architecture

Experience

Chief AI Officer

NeuraX • 2019 - Present

Leading AI research and development initiatives, managing a team of 50+ researchers and engineers. Spearheading breakthrough projects in multimodal AI and large language models.

Senior Research Scientist

Google AI • 2014 - 2019

Led research on NLP and computer vision, published 20+ papers in top venues. Key contributor to Google's BERT and Vision Transformer projects.

PhD Researcher

MIT CSAIL • 2010 - 2014

Research on deep learning architectures for multimodal understanding. Thesis: "Neural Approaches to Visual and Linguistic Reasoning" - awarded Best Dissertation.

Selected Publications

Attention Is All You Need: A Comprehensive Survey

NeurIPS 2023 • Co-authored with J. Smith, M. Johnson • 1,200+ citations

Multimodal Learning with Vision-Language Transformers

ICML 2022 • Co-authored with A. Brown • Best Paper Award

Ethical AI: Principles and Practices for Responsible Development

Nature AI 2021 • Solo author • Featured in Nature Spotlight

Recognition

Awards & Honors

2024

AI Researcher of the Year

World AI Summit

2023

Best Paper Award

ICML Conference

2022

Top 100 AI Leaders

Forbes Technology

2014

Best PhD Dissertation

MIT CSAIL

Education

PhD in Computer Science

Massachusetts Institute of Technology

2010 - 2014

Focus: Deep Learning & Multimodal AI. Thesis: "Neural Approaches to Visual and Linguistic Reasoning"

M.S. in Computer Science

Stanford University

2008 - 2010

Specialization in Artificial Intelligence and Machine Learning

B.S. in Computer Science

UC Berkeley

2004 - 2008

Graduated Summa Cum Laude, Phi Beta Kappa

Certifications

Google Cloud Professional ML Engineer

Google Cloud

2023

AWS Machine Learning Specialty

Amazon Web Services

2022

Deep Learning Specialization

DeepLearning.AI

2021

Technical Proficiency

Skills & Expertise

Machine Learning

Deep Learning / Neural Networks98%
Natural Language Processing95%
Computer Vision92%
Transformer Architecture97%

Programming & Tools

Python / PyTorch / TensorFlow96%
JAX / Flax88%
Cloud ML (GCP, AWS, Azure)90%
MLOps / Kubeflow85%
Public Speaking

Talks & Conferences

Keynote

The Future of Multimodal AI: Beyond Text and Images

NeurIPS 2024

Dec 2024 Vancouver
Workshop

Building Responsible AI: Ethics in Practice

Google AI Summit

Oct 2024 San Francisco
Panel

Women in AI: Breaking Barriers in Tech Leadership

World Economic Forum

Jan 2024 Davos
Talk

Scaling Language Models: Lessons from the Frontier

ICML 2023

Jul 2023 Honolulu
Lecture

Introduction to Transformers (Stanford CS224N)

Stanford University

Ongoing Palo Alto
Podcast

AI & Society: Navigating the Transformation

Lex Fridman Podcast

Mar 2023 2.5M views

Other Team Members