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<h1 class="text-4xl sm:text-5xl lg:text-6xl font-bold leading-tight mb-6">
<span class="gradient-text">Dr. Alex Chen</span><br>
<span class="text-gray-800">AI Researcher & Machine Learning Expert</span>
</h1>
<p class="text-lg text-gray-600 mb-8 max-w-lg">
Pioneering research at the intersection of deep learning, computer vision, and natural language processing.
Committed to advancing AI for social good.
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Explore Research
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Contact Me
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<!-- About Section -->
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<h2 class="text-3xl font-bold text-center mb-12 gradient-text">About Me</h2>
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<h3 class="text-2xl font-semibold text-gray-800 mb-6">Biography</h3>
<p class="text-gray-600 mb-6">
I am an AI researcher with a passion for developing innovative machine learning solutions to complex real-world problems.
My research focuses on advancing the frontiers of artificial intelligence through interdisciplinary approaches.
</p>
<p class="text-gray-600 mb-6">
Currently, I lead the AI Research Lab at Stanford University, where my team explores novel architectures for
multimodal learning, interpretable AI, and robust machine learning systems.
</p>
<p class="text-gray-600">
My work has been recognized with several awards including the NSF CAREER Award and the AAAI Outstanding Paper Award.
I'm committed to mentoring the next generation of AI researchers and promoting ethical AI development.
</p>
<div class="mt-8">
<h4 class="text-lg font-semibold text-gray-800 mb-4">Research Interests</h4>
<div class="flex flex-wrap gap-3">
<span class="px-3 py-1 bg-blue-50 text-blue-600 rounded-full text-sm">Deep Learning</span>
<span class="px-3 py-1 bg-purple-50 text-purple-600 rounded-full text-sm">Computer Vision</span>
<span class="px-3 py-1 bg-green-50 text-green-600 rounded-full text-sm">NLP</span>
<span class="px-3 py-1 bg-yellow-50 text-yellow-600 rounded-full text-sm">Multimodal Learning</span>
<span class="px-3 py-1 bg-red-50 text-red-600 rounded-full text-sm">Interpretable AI</span>
<span class="px-3 py-1 bg-indigo-50 text-indigo-600 rounded-full text-sm">Robust ML</span>
</div>
</div>
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<h3 class="text-2xl font-semibold text-gray-800 mb-6">Education & Experience</h3>
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<h4 class="text-lg font-semibold text-gray-800">PhD in Computer Science</h4>
<p class="text-gray-600">Stanford University | 2015-2019</p>
<p class="text-gray-500 mt-1">Thesis: "Advancing Multimodal Learning Through Neural Architecture Search"</p>
</div>
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<h4 class="text-lg font-semibold text-gray-800">AI Research Scientist</h4>
<p class="text-gray-600">Google Brain | 2019-2021</p>
<p class="text-gray-500 mt-1">Led research on self-supervised learning for computer vision applications</p>
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<h4 class="text-lg font-semibold text-gray-800">Assistant Professor</h4>
<p class="text-gray-600">Stanford University | 2021-Present</p>
<p class="text-gray-500 mt-1">Director of the AI Research Lab, focusing on interpretable and robust AI systems</p>
</div>
</div>
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<!-- Skills -->
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<h3 class="text-2xl font-semibold text-center text-gray-800 mb-8">Technical Expertise</h3>
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<span class="mt-2 text-sm font-medium text-gray-700">Python</span>
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<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/pytorch/pytorch-original.svg" class="w-12 h-12 tech-icon" alt="PyTorch">
<span class="mt-2 text-sm font-medium text-gray-700">PyTorch</span>
</div>
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<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/tensorflow/tensorflow-original.svg" class="w-12 h-12 tech-icon" alt="TensorFlow">
<span class="mt-2 text-sm font-medium text-gray-700">TensorFlow</span>
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<span class="mt-2 text-sm font-medium text-gray-700">NumPy</span>
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<span class="mt-2 text-sm font-medium text-gray-700">Pandas</span>
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<span class="mt-2 text-sm font-medium text-gray-700">Docker</span>
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</section>
<!-- Research Section -->
<section id="research" class="py-20 px-4 sm:px-6 lg:px-8 bg-gray-50">
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<h2 class="text-3xl font-bold text-center mb-12 gradient-text">Research Areas</h2>
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<!-- Research Card 1 -->
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<div class="h-48 bg-gradient-to-r from-blue-400 to-blue-600 flex items-center justify-center">
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Multimodal Learning</h3>
<p class="text-gray-600 mb-4">
Developing novel architectures that effectively combine vision, language, and other modalities
for more comprehensive AI understanding.
</p>
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Learn more <i class="fas fa-arrow-right ml-2"></i>
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Interpretable AI</h3>
<p class="text-gray-600 mb-4">
Creating methods to make complex neural networks more transparent and explainable
without sacrificing performance.
</p>
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Learn more <i class="fas fa-arrow-right ml-2"></i>
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Robust ML</h3>
<p class="text-gray-600 mb-4">
Building machine learning systems that maintain performance under distribution shifts,
adversarial attacks, and real-world noise.
</p>
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Language Understanding</h3>
<p class="text-gray-600 mb-4">
Advancing neural language models with better contextual understanding,
reasoning capabilities, and multilingual performance.
</p>
<a href="#" class="text-yellow-600 font-medium inline-flex items-center">
Learn more <i class="fas fa-arrow-right ml-2"></i>
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<!-- Research Card 5 -->
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<i class="fas fa-robot text-white text-6xl"></i>
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Embodied AI</h3>
<p class="text-gray-600 mb-4">
Developing AI systems that learn through interaction with physical environments,
bridging the gap between virtual and real-world learning.
</p>
<a href="#" class="text-red-600 font-medium inline-flex items-center">
Learn more <i class="fas fa-arrow-right ml-2"></i>
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<!-- Research Card 6 -->
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<div class="h-48 bg-gradient-to-r from-indigo-400 to-indigo-600 flex items-center justify-center">
<i class="fas fa-heartbeat text-white text-6xl"></i>
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<div class="p-6">
<h3 class="text-xl font-semibold text-gray-800 mb-3">AI for Social Good</h3>
<p class="text-gray-600 mb-4">
Applying cutting-edge AI techniques to address global challenges in healthcare,
education, environmental sustainability, and more.
</p>
<a href="#" class="text-indigo-600 font-medium inline-flex items-center">
Learn more <i class="fas fa-arrow-right ml-2"></i>
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</div>
</div>
</div>
</div>
</section>
<!-- Publications Section -->
<section id="publications" class="py-20 px-4 sm:px-6 lg:px-8 bg-white">
<div class="max-w-7xl mx-auto">
<h2 class="text-3xl font-bold text-center mb-12 gradient-text">Selected Publications</h2>
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<!-- Publication 1 -->
<div class="project-card relative h-64">
<div class="project-inner relative w-full h-full">
<div class="project-front absolute w-full h-full bg-gradient-to-br from-blue-50 to-purple-50 rounded-xl p-6 shadow-md flex flex-col justify-between">
<div>
<h3 class="text-xl font-semibold text-gray-800 mb-2">Cross-Modal Attention for Vision-Language Integration</h3>
<p class="text-gray-600 text-sm">NeurIPS 2022</p>
</div>
<div class="flex justify-between items-center">
<span class="px-3 py-1 bg-blue-100 text-blue-800 rounded-full text-xs font-medium">Computer Vision</span>
<span class="text-gray-500 text-sm">Citations: 142</span>
</div>
</div>
<div class="project-back absolute w-full h-full bg-gradient-to-br from-blue-100 to-purple-100 rounded-xl p-6 shadow-md">
<h3 class="text-xl font-semibold text-gray-800 mb-3">Cross-Modal Attention for Vision-Language Integration</h3>
<p class="text-gray-600 text-sm mb-4">
We propose a novel attention mechanism that dynamically learns to attend to relevant
information across vision and language modalities, achieving state-of-the-art results
on multiple benchmarks.
</p>
<a href="#" class="text-blue-600 font-medium inline-flex items-center text-sm">
Read Paper <i class="fas fa-external-link-alt ml-2"></i>
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<!-- Publication 2 -->
<div class="project-card relative h-64">
<div class="project-inner relative w-full h-full">
<div class="project-front absolute w-full h-full bg-gradient-to-br from-green-50 to-blue-50 rounded-xl p-6 shadow-md flex flex-col justify-between">
<div>
<h3 class="text-xl font-semibold text-gray-800 mb-2">Interpretable Neural Architecture Search</h3>
<p class="text-gray-600 text-sm">ICML 2021</p>
</div>
<div class="flex justify-between items-center">
<span class="px-3 py-1 bg-green-100 text-green-800 rounded-full text-xs font-medium">NAS</span>
<span class="text-gray-500 text-sm">Citations: 89</span>
</div>
</div>
<div class="project-back absolute w-full h-full bg-gradient-to-br from-green-100 to-blue-100 rounded-xl p-6 shadow-md">
<h3 class="text-xl font-semibold text-gray-800 mb-3">Interpretable Neural Architecture Search</h3>
<p class="text-gray-600 text-sm mb-4">
This work introduces a novel NAS framework that not only discovers high-performing architectures
but also provides interpretable insights into why certain architectural choices perform better.
</p>
<a href="#" class="text-green-600 font-medium inline-flex items-center text-sm">
Read Paper <i class="fas fa-external-link-alt ml-2"></i>
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<!-- Publication 3 -->
<div class="project-card relative h-64">
<div class="project-inner relative w-full h-full">
<div class="project-front absolute w-full h-full bg-gradient-to-br from-purple-50 to-pink-50 rounded-xl p-6 shadow-md flex flex-col justify-between">
<div>
<h3 class="text-xl font-semibold text-gray-800 mb-2">Robust Self-Supervised Learning</h3>
<p class="text-gray-600 text-sm">CVPR 2020</p>
</div>
<div class="flex justify-between items-center">
<span class="px-3 py-1 bg-purple-100 text-purple-800 rounded-full text-xs font-medium">Self-Supervised</span>
<span class="text-gray-500 text-sm">Citations: 210</span>
</div>
</div>
<div class="project-back absolute w-full h-full bg-gradient-to-br from-purple-100 to-pink-100 rounded-xl p-6 shadow-md">
<h3 class="text-xl font-semibold text-gray-800 mb-3">Robust Self-Supervised Learning</h3>
<p class="text-gray-600 text-sm mb-4">
We present a new framework for self-supervised learning that is robust to distribution shifts
and adversarial perturbations, significantly outperforming previous approaches in challenging
real-world scenarios.
</p>
<a href="#" class="text-purple-600 font-medium inline-flex items-center text-sm">
Read Paper <i class="fas fa-external-link-alt ml-2"></i>
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<!-- Publication 4 -->
<div class="project-card relative h-64">
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<div class="project-front absolute w-full h-full bg-gradient-to-br from-yellow-50 to-orange-50 rounded-xl p-6 shadow-md flex flex-col justify-between">
<div>
<h3 class="text-xl font-semibold text-gray-800 mb-2">Dynamic Neural Networks for Efficient Inference</h3>
<p class="text-gray-600 text-sm">AAAI 2019</p>
</div>
<div class="flex justify-between items-center">
<span class="px-3 py-1 bg-yellow-100 text-yellow-800 rounded-full text-xs font-medium">Efficiency</span>
<span class="text-gray-500 text-sm">Citations: 76</span>
</div>
</div>
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Dynamic Neural Networks for Efficient Inference</h3>
<p class="text-gray-600 text-sm mb-4">
This paper introduces a new class of dynamic neural networks that adapt their computation
based on input complexity, achieving significant efficiency gains without sacrificing accuracy.
</p>
<a href="#" class="text-yellow-600 font-medium inline-flex items-center text-sm">
Read Paper <i class="fas fa-external-link-alt ml-2"></i>
</a>
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</div>
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<h2 class="text-3xl font-bold text-center mb-12 gradient-text">Get In Touch</h2>
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