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Python, Machine Learning, SQL, Java, R, Tableau

Location:
Framingham, MA
Posted:
January 20, 2020

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Resume:

linkedin.com/in/robin-wu

github.com/Kiwimaru Robin Wu

774-***-****

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



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