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Ryan Chan is listed as Machine Learning PhD Student @ UPenn | NSF Graduate Research Fellow | UPenn Dean's Fellow | Previous AIML Intern, Health AI @ Apple based in Philadelphia, Pennsylvania, United States. AeroLeads shows a work email signal at jhu.edu and a matched LinkedIn profile for Ryan Chan.
Ryan Chan previously worked as AI/ML Intern, Health AI at Apple and Machine Learning Researcher at Lawrence Livermore National Laboratory. Ryan Chan holds Doctor Of Philosophy - Phd, Machine Learning from University Of Pennsylvania.
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For my most recent updates about my publications and projects, please visit my personal website: https://ryanchankh.github.ioI am a fourth-year Electrical and Computer Engineering PhD student at University of Pennslyvania, advised by Prof. René Vidal. I am also a NSF Graduate Research Fellow and Dean's Fellow. I received my BA in Applied Mathematics from University of California, Berkeley and was a research assistant at Prof. Yi Ma's group. After my undergraduate studies, I was also a machine learning researcher at Lawrence Livermore National Lab.My main interests lie in the intersection of theory and application for interpretable machine learning. The goal is to develop frameworks that simultaneously provide the flexibility to define what is semantic or interpretable to the user and achieve competitive performance against state-of-the-art on complex Vision and Language tasks. I also have strong interests in applying interpretable machine learning to biomedical domains. Through theoretically-sounded and empirically-verified algorithms, we may develop methods that are not only safe and fair for practical uses, but also insightful and explanable for future scientific explorations.
Listed skills include Mathematics, Microsoft Office, Tutoring, Python, and 29 others.
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Seattle, Washington, United States
- Focused on building an multi-modal, interpretable and editable recommendation system that leverages Multi-modaland Large Language Models for making predictions with production level scalability and speed.- Interacted with different production-level datasets which the method will be applied for real-world applications.
Livermore, California, United States
Project 1: COVID-19 Patient Risk Stratification under Cost Constraints- A fast, easy-to-use and explainable patient risk score prediction model that aided doctors to calculate patients’ need for hospitalization and ventilation or chance of death based on their electronic health records and costs of lab tests- Implemented data imputation, feature.
Berkeley, California, United States
- Designed skeleton and graded all of students’ homework and final presentations- Provided help to students through online forum by answering questions related to lecture or logistics daily
Berkeley, California, United States
- Research Focus: Interpretable Deep Learning, Sparse and Low-rank Methods, and Computer Vision.- Two publications on Maximal Coding Rate Reduction (See Google Scholar for paper)
Berkeley, CA
Project: Subspace Locally Competitive Algorithm- Published as second author (check Google Scholar for link)- Analyzed plots and contributed to insights in weekly research meetings- Contributed to mentor’s open source deep learning repository on GitHub- Compared code in open source library Tensorflow and verified outputs with different implementations
Shenzhen, Guangdong, China
Project: Understanding Facial Recognition Models through Local Feature Attacks- Deployed an end-to-end AI pipeline and systems for real-life applications- Created an API for testing model robustness to diverse input images and adversarial examples- Presented to other researchers on new adversarial attacks and security flaws in deep learningProject: Model.
Shanghai City, China
Project: Neural Network Parser for Resume Recommendation System- Improvised and applied concepts from multiple academic papers to improve recommendations- Processed 100k+ Chinese sentences to speed up data processing by 100-150%- Developed server-pressure tests for product’s runtime and memory performance analysis- Set up server configuration and defined.
Pasadena, California
- Taught K1-K12 student mathematics ranging from Basic Arithmetic to Single Variable Calculus- Finished Mathnasium's tutoring training and trained specifically to teach "number sense"- Practiced time management skills and social skills by interacting with younger students
Math Success Center
- Provided math tutoring for 30 students each week- Tutored Math level Basic Arithmetic to Linear Algebra- Provided academic advises to educational and career planning
Advised by Professor René Vidal (UPenn)
Advised by Professor René Vidal (JHU), Professor Yi Ma (UC Berkeley) and Professor Donald Geman (JHU)
Advised by Professor Yi Ma
Activities and Societies: Math Lecture Series
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Ryan Chan is listed as Machine Learning PhD Student @ UPenn | NSF Graduate Research Fellow | UPenn Dean's Fellow | Previous AIML Intern, Health AI @ Apple.
AeroLeads has found 1 work email signal at @jhu.edu for Ryan Chan.
Ryan Chan is based in Philadelphia, Pennsylvania, United States.
Ryan Chan has worked for Apple, Lawrence Livermore National Laboratory, Uc Berkeley Electrical Engineering & Computer Sciences (Eecs), Helen Wills Neuroscience Institute At Uc Berkeley, and Huawei.
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Ryan Chan holds Doctor Of Philosophy - Phd, Machine Learning from University Of Pennsylvania.
Ryan Chan is listed with skills including Mathematics, Microsoft Office, Tutoring, Python, Social Media, Community Outreach, Java, and Research.
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