linkedin.com/in/robin-wu
github.com/Kiwimaru Robin Wu
Framingham, MA
adbc8t@r.postjobfree.com
EDUCATION
University of Massachusetts Amherst Amherst, MA
M.S. in Computer Science, Expected: May 2020 GPA: 4.00
● Concentration: Data Science
● Coursework: Machine Learning, Neural Networks, Database Design University of Massachusetts Amherst Amherst, MA
B.S. in Computer Science, 2018 GPA: 3.67
● Member of Commonwealth Honors College, Dean’s List Honors
● Coursework: Algorithms for Data Science, Data Visualization, NLP TECHNICAL SKILLS
Languages: Python, Java, R, SQL, HTML/CSS/JavaScript Tools:GitHub, Jupyter Notebook, Google Colab, Tableau, Microsoft Office Frameworks: Numpy, Pandas, Scikit-Learn, Tensorflow, Keras, Matplotlib, NLTK, Flask, Shiny WORK EXPERIENCE
Intralinks - Data Science Intern Python, Java, MongoDB, S3 Bucket May - Aug 2019
● Engineered prototype that can scan merger-acquisition documents and redact sensitive information such as SNN, names, emails, phone numbers, addresses, etc..
● Created end-to-end pipeline to automate document operations - scanning, processing, highlighting, redacting on thousands of documents
● Developed the core intelligence using regex and machine learning models
● Wrote a custom tool to modify PDFs without losing existing formatting
● Implemented modules of the project as microservices and created a RESTful API
● Presented to executives and 140+ employees and presented at Global AI Models Deep Dive First-Year Seminar Instructor Sep - Dec 2019
● Instruct a class of Computer Science students on the ethics to be good computer scientists
● Guide and advise students to acclimate with events, resources, and culture of the university Grader for Machine Learning Course Sep - Dec 2019
● Grade assignments for a graduate level machine learning course with 150+ students PROJECT
Cat Disguiser Python
● Superimposed accessories (e.g. glasses, mustaches, hats) onto a variable number of cat faces within images and videos in real time
● Trained deep learning models using MobileNetv2 and YOLO for transfer learning
● Achieved MAE of 2.13 for facial landmarking and IOU of 0.81 on bounding box regression Conversation Analyzer Python
● Developed machine learning models to analyze vocal activities in two-man conversations
● Collected 3000 conversational audio clips for speech, laughter, filler words, and noise
● Extracted audio features to train K-Means Clustering and Support Vector Machine
● Resulted in F1 Score of 89% in speaker classification and 87% of speech type