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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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alt="Dr. Alex Chen" | |
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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> | |
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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. | |
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<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. | |
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<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. | |
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<h4 class="text-lg font-semibold text-gray-800 mb-4">Research Interests</h4> | |
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<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> | |
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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> | |
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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> | |
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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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<span class="mt-2 text-sm font-medium text-gray-700">PyTorch</span> | |
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<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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<!-- Research Section --> | |
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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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<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> | |
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Creating methods to make complex neural networks more transparent and explainable | |
without sacrificing performance. | |
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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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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">Language Understanding</h3> | |
<p class="text-gray-600 mb-4"> | |
Advancing neural language models with better contextual understanding, | |
reasoning capabilities, and multilingual performance. | |
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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">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> | |
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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">AI for Social Good</h3> | |
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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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<!-- Publications Section --> | |
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<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 --> | |
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<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> | |
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<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> | |
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<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. | |
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<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 --> | |
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<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> | |
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<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> | |
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<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> | |
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Read Paper <i class="fas fa-external-link-alt ml-2"></i> | |
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<!-- Publication 3 --> | |
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<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> | |
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<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> | |
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<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. | |
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<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> | |
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<span class="text-gray-500 text-sm">Citations: 76</span> | |
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<h3 class="text-xl font-semibold text-gray-800 mb-3">Dynamic Neural Networks for Efficient Inference</h3> | |
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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. | |
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