<aside> <img src="/icons/invitation_gray.svg" alt="/icons/invitation_gray.svg" width="40px" /> [email protected]
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<aside> <img src="notion://custom_emoji/ce377c57-cfb3-4608-9fa1-d1fd69e3e798/186f45bc-446e-8029-bbb4-007a1c006d26" alt="notion://custom_emoji/ce377c57-cfb3-4608-9fa1-d1fd69e3e798/186f45bc-446e-8029-bbb4-007a1c006d26" width="40px" /> raymondsun118
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<aside> <img src="attachment:988e8830-453d-4203-afc7-cafa04722df1:linkedin_logo.png" alt="attachment:988e8830-453d-4203-afc7-cafa04722df1:linkedin_logo.png" width="40px" /> LinkedIn
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👁️ Computer Vision
📊 Data Mining
🎯 Marketing
🔗 Prototyping
💭 Asking Questions
<aside> <img src="attachment:e4dae53b-13fc-45e6-b45f-1fac31b4d87f:cfa.png" alt="attachment:e4dae53b-13fc-45e6-b45f-1fac31b4d87f:cfa.png" width="40px" /> Chartered Financial Analyst (CFA) Level 1
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<aside> <img src="attachment:b4b71bdd-d9c7-4dce-97cb-8ededcab1ea4:Screenshot_2025-01-25_at_5.33.27_PM.png" alt="attachment:b4b71bdd-d9c7-4dce-97cb-8ededcab1ea4:Screenshot_2025-01-25_at_5.33.27_PM.png" width="40px" /> AWS Certified Cloud Practitioner
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<aside> <img src="attachment:5dc0c94e-ac1a-47f7-ad70-42b48e9504d8:jhu_logo.png" alt="attachment:5dc0c94e-ac1a-47f7-ad70-42b48e9504d8:jhu_logo.png" width="40px" /> Johns Hopkins University — MSE in Applied Mathematics and Statistics
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🏸 Badminton
🏊 Swimming
🥊 Boxing
🏋️ Body Building
I'm a Data Scientist & Algorithm Developer with expertise in Computer Vision for the Automotive Industry.
Passionate about leveraging cutting-edge technology to solve challenges and deliver impactful solutions.
Always here to help tackle any problem with dedication and creativity!
<aside> <img src="attachment:c3d4ee14-dfcb-40b9-9876-a344d115c14b:download.png" alt="attachment:c3d4ee14-dfcb-40b9-9876-a344d115c14b:download.png" width="40px" /> Data Scientist / Computer Vision Algorithm Developer in Advanced Interior Sensing ****Magna Electronics (Feb 2023 - Till Now)
• Driver Posture Classification: Developed 94% accuracy and 99% recall rate Posture Classification system by fine-tuning YOLO-Pose and PoseNet in PyTorch and integrating real-time camera streams in ROS 2 using C++ to detect scenarios unsuitable for airbag deployment and applied appropriate airbag suppression
• Intoxication Classification System: Developed 96% accuracy driver intox level classification system based on driving performance by training LSTM-based time-series models, leveraging T-Learner in Causal Inference to real-time detect driver driving situation
• Distraction Detection while Driving: Achieved 91% accuracy and 96% cellphone usage recall rate on cellphone detection system by training and fine-tuning Vision Transformer to alert driver when distraction happens thus enhance safety
• AWS Data Engineer: Designed and built ETL data pipeline, from collecting data from sensors in C++, health-check and uploading into S3 bucket, applying Lambda to batch-process data, to train models in EC2 and SageMaker
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<aside> <img src="attachment:d17d3f28-1fac-4c45-b46d-315f84707f56:nasdaq.jpg" alt="attachment:d17d3f28-1fac-4c45-b46d-315f84707f56:nasdaq.jpg" width="40px" /> Machine Learning Engineer in AI Core Lab ****Nasdaq (May 2022 - Aug 2022)
• Catastrophe Classification Model: Attained over 85% accuracy, 1 hour MAE, and 50K estimated annual revenue by estimating Catastrophe Model runtime with linear regression and XGBoost model using Python to allow customer to plan model operation time in advance • Data Imbalanced Problem: Boosted 30% accuracy by bootstrapping, over-sampling, and SMOTE methods for data imbalance problem: lower MAE by dividing and building separate models for significant outlier measures • AWS and Team Cooperation: Cooperated effectively with other colleagues on GitHub and uploaded models to AWS EC2 instance, S3 Bucket, SageMaker, Lambda for training, testing, and deploying automatically; Made presentation twice a week to give feedback and determine feature details with non-tech business partners
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<aside> <img src="attachment:f1400f3b-8300-4729-a03d-4b152dce1c52:download_(1).png" alt="attachment:f1400f3b-8300-4729-a03d-4b152dce1c52:download_(1).png" width="40px" /> UX Product Manager DIDI Global (Jan 2021 - Aug 2021)
• Bad Pax Experience: Reduced 10% Bad Pax Experience by creating new App features through costumer surveys, competitor analysis, statistical testing, algorithm producing and A/B Testing • Cancellation Rate: Increased 8% Order Completion Rate and decreased 10% cancellation rate by improving pricing strategy through data analysis, making math model, and simulating real-world situation • Competitor Analysis: Independently completed competitive product research of Uber and Lyft
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