cv
Basics
Name | Yike Tan |
Label | Machine Learning Engineer |
yiketn@gmail.com | |
Phone | +1-412-583-0350 |
Url | http://likegiver.github.io |
Summary | Graduate student at Carnegie Mellon University specializing in Artificial Intelligence Engineering and Information Security with a strong foundation in machine learning and software development. |
Work
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2024.07 - 2024.08 Machine Learning Engineer Intern
Epoching AI
Developed an end-to-end watermark removal service and optimized training pipelines for e-commerce imagery.
- Fine-tuned pre-trained ViT model, achieving 30% accuracy improvement.
- Deployed scalable FastAPI/Docker service with Prometheus monitoring.
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2023.09 - 2024.03 Machine Learning Engineer Intern
Ytell Network Technology Co., Ltd.
Designed and implemented multimodal knowledge bots and automated pipelines for order processing.
- Integrated VLMs with backend, improving response accuracy by 10%.
- Developed automated ETL workflows processing 1,000+ daily requests with 95% accuracy.
Education
-
2023.08 - 2025.12 Pittsburgh, PA
Master of Science
Carnegie Mellon University
Artificial Intelligence Engineering - Information Security
- Large Language Models
- AI Systems and Tool Chains
- Computer Systems
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2020.08 - 2024.07 Beijing, China
Bachelor of Engineering
University of International Business and Economics
Data Science and Big Data Technology
- Machine Learning
- Data Structure and Algorithm
- Natural Language Processing
- Big Data Analysis
Projects
- 2024.10 - 2024.10
Memory Allocator
Implemented a high-performance dynamic memory allocator in C with tools for debugging and optimization.
- Achieved 75%+ memory utilization through boundary tag coalescing.
- Integrated GDB debugging tools for block alignment validation.
- 2024.09 - 2024.09
Transformer Decoder
Built a decoder-only Transformer with custom tokenizer and training pipeline.
- Published trained model to HuggingFace Hub.
- Integrated Weights & Biases for experiment tracking.
- 2024.05 - 2024.06
Self-LLM: Open-Source LLMs Deployment Guide
Collaborated on deployment guides for GLM4, enabling developers to build advanced LLM applications.
- 10k+ GitHub stars for deployment guides.
- - Present
Keep Knowledge in Perception: Zero-shot Image Aesthetic Assessment
Developed a novel zero-shot framework for fine-grained aesthetic assessment, outperforming SOTA approaches.
- ICASSP 2024 Oral Presentation.
- Created a large-scale aesthetic dataset with 175k expert critiques.
Skills
Programming Languages | |
Python | |
C | |
C++ | |
Java | |
JavaScript | |
SQL | |
R |
Packages and Frameworks | |
React | |
Node.js | |
Django | |
Flask | |
NumPy | |
Pandas | |
PyTorch | |
TensorFlow | |
Jax | |
SparkML |
Tools | |
Git | |
Docker | |
Kafka | |
Neo4j | |
Google Cloud Platform | |
Unix | |
LaTeX |
Languages
English | |
Fluent |
Chinese | |
Native |