Open to opportunities

Yunhan Hu

@yunhanhu

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Ph.D. candidate specializing in generative models and machine learning.

United States

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What I'm looking for

I am seeking a role that fosters innovation and collaboration, where I can apply my expertise in machine learning and contribute to impactful projects.

I am a dedicated Ph.D. candidate at the University of Wisconsin-Madison, specializing in generative models and end-to-end pipelines. My research focuses on solving complex inverse design problems, where I have successfully architected a novel generative model that significantly reduces design lifecycles. My work has led to first-author publications in top-tier journals, showcasing my commitment to advancing the field of machine learning and its applications in science.

Throughout my research experience, I have developed a deep generative network that enhances candidate generation efficiency and engineered a robust data processing pipeline that achieves high accuracy. I thrive in collaborative environments, having led a team of junior researchers and worked closely with cross-functional teams to translate complex scientific requirements into actionable machine learning specifications. My contributions have had a tangible impact on real-world applications, demonstrating my ability to bridge the gap between theory and practice.

Experience

Work history, roles, and key accomplishments

Current

Research Assistant

Current

University of Wisconsin-Madison

Aug 2021 - Present (3 years 11 months)

Architected a novel generative model (VAE-GAN inspired) to solve high-dimensional inverse design problems, reducing the design lifecycle by over 90%. Engineered an end-to-end data processing and automated labeling pipeline for large-scale simulation data, achieving over 95% accuracy with a custom classifier.

PyTorch CUDA Generative Models VAE GAN Data Processing Machine Learning Python Matlab Git

Education

Degrees, certifications, and relevant coursework

University of Wisconsin-Madison

Ph.D., Electrical and Computer Engineering

Grade: 3.86 / 4.00

Pursued a Ph.D. in Electrical and Computer Engineering with a focus on generative models and end-to-end pipelines. Achieved a GPA of 3.86 / 4.00, demonstrating strong academic performance in advanced engineering concepts.

University of Wisconsin-Madison

M.S., Computer Science

Completed a Master of Science degree in Computer Science. This program deepened my understanding of core computer science principles and advanced topics.

Northwestern University

M.S., Electrical Engineering

Grade: 3.86 / 4.00

Earned a Master of Science degree in Electrical Engineering with a GPA of 3.86 / 4.00. Focused on advanced electrical engineering principles and research methodologies.

Zhejiang University

B.S., Physics

Grade: 3.68 / 4.00

Obtained a Bachelor of Science degree in Physics with a GPA of 3.68 / 4.00. Developed a strong foundation in fundamental physics concepts and analytical problem-solving.