Machine Learning Computer Science

Yi-Ting (Elane) Han

413-***-**** # ****@*****.*** ï Yi-Ting (Elane) Han § elanehan Ð Personal Website Education

University of Massachusetts Amherst Sept. 2023 – Expected May 2025 M.S in Computer Science; GPA: 3.9/4.0 Amherst, MA

• Courses: Theory and Practice of Software Engineering, Advanced Algorithm, Computer & Network Security, Reinforcement Learning, Game Programming

National Chung Cheng University (CCU) Sept. 2018 – Jan. 2022 B.S in Computer Science and Information Engineering; GPA: 3.8/4.0 Chiayi, Taiwan

• Two times Academic Presidential Awards (Top 5% students of the year), Undergraduate Research Fellowship

• Courses: Objected-Oriented Programming, Operating System, Data Structure, Algorithms, Machine Learning Skills

Programming Languages: Python, JavaScript/TypeScript, HTML/CSS, SQL, Java, C/C++, C#, R, MATLAB Tools/Frameworks: Git, Numpy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, Docker, Shell Script, Keras, PyTorch Work Experience

Rescue Lab Umass Amherst Feb. 2024 – Present

Software Engineer Intern Amherst, MA

• Integrated computer vision models with Nest.js-based backend of microservices application to pinpoint tattoos potentially linked to criminal affiliations.

• Developed new REST API endpoint to trigger machine learning models, handling data processing and result delivery.

• Built interactive frontend components through HTML, CSS, TypeScript, and Node.js to run models and display the results in user-friendly manner.

• Boosted efficiency by organizing services with Docker, making it easy to deploy and scale them smoothly. Institute of Information Science, Academia Sinica Feb. 2022 – July. 2023 Data Engineer/Research Assistant Taipei, Taiwan

• Enhanced cancer detection by 5% across 49k+ sequences in 4 species by improving deep learning algorithm for eccDNA.

• Reduced processing time by 84% for 14+ TB of sequence data through streamlining eccDNA detection pipeline using data cleaning, parallel processing, and batch scheduling techniques.

• Pioneered the development of Python tool for efficient detection of eccDNAs in single-cell sequencing data.

• Developed the first PostgreSQL database compiling eccDNAs from 1.4k+ Taiwanese individuals through collaboration with biotech team.

• Collaborated with cross-functional team of engineers and biologists to explore the relationship between eccDNAs and flu/PM2.5 across over 74k+ cells using single-cell multimodal sequencing data by leveraging data science techniques including regression analysis and hypothesis testing. National Chung Cheng University Sep. 2019 – June 2020 Teaching Assistant Chiayi, Taiwan

• Provided guidance to 40+ Program Design students on weekly assignments, resulting in positive feedback from students regarding the effectiveness of the advice.

• Created interactive web pages to aid instructors in visually representing key concepts for weekly assignments. Projects

Dream Singer: A Web Platform for AI Song Cover JavaScript, Django, REST API Mar. 2024 – Present

• Developed full-stack features for unique music system such as user registration, profile management, media uploads/downloads, and historical record access.

• Implemented backend services and deployed deep learning model for generating song covers using Django.

• Achieved seamless integration of backend and frontend via REST API, ensuring a smooth user experience from profile creation to song download.

Real-Time Product Information App in Mixed Reality Python, YOLOv5, Unity Feb. 2021 – Dec. 2021

• Led team to design real-time product recognition system using Unity for mixed reality application.

• Improved YOLOv5 model and reached mean Average Precision score above 99% on 8k+ product images.

• Won Undergraduate Research Fellowship for excellent students awarded based on written research proposals. Memory Management in the Linux System Linux, Bash, C June 2021

• Implemented First-In-First-Out (FIFO) page replacement algorithm within Linux environment.

• Achieved comprehensive grasp of FIFO page replacement policy implementation, discerning nuances between page faults and replacements through practical experimentation and analysis.

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