Research Assistant Resume Boston - Hire IT People

SUMMARY

Machine Learning Software Engineer, skills in Computer Vision, AI, and Deep Learning
Coding skills: Python; C/C++, TensorFlow; Java, MATLAB, JavaScript, and SQL
Knowledge of Agile SDLC, RDBMS, and scikit - learn, NumPy, Pandas libraries

TECHNICAL SKILLS

Programming: Java, Python, MATLAB, C/C++, JavaScript, HTML, CSS, TypeScript, JSP

Deep Learning/Machine Learning: TensorFlow, scikit-learn, NumPy, Pandas, Plotting libraries

Database: SQL, MySQL, SQL Server, ORACLE, DynamoDB, FileMaker Pro

Other: Eclipse, GIT, JIRA, SQL Developer, Anaconda, IntelliJ, Windows, Mac OS, Ubuntu

PROFESSIONAL EXPERIENCE

Research Assistant

Confidential, Boston

Responsibilities:

Researching how to include nanotechnology in high school and post-secondary education.
Developed lego based AFM, and programmed it using LAB view.

Software Developer Intern

Confidential, Boston

Responsibilities:

Created and provided regular updates for University website and intranet network. In corporate object-oriented techniques for conceptual and technical design.
Implemented Agile practices based on project requirements; coded application components using test-driven approach.
Assisted integration of front end and back end applications, along with data migration and data management solutions. Wrote complex database MySQL queries.
Executed testing and troubleshooting procedures for website applications, and components.

Computer Vision Research Assistant

Confidential, Boston

Responsibilities:

Implemented simple scripting language allowing users without programming experience to set up gaze-contingent text displays. Language displayed text on-screen and defined keywords dat triggered actions when users viewed them.
Actions were specific sound file or bitmap image being displayed at a given position on screen
Conducted experiment on 15 users under static control condition, and 4 conditions displaying 20 written words each.
Results suggest dat users prefer gaze-contingent text enhancement, but instead of presenting identical information in different forms, should provide additional information related to attended words.
Constructed data samples in proper format (feature construction and class label assignment) used as input data for various machine learning algorithms.
Experimental samples were used to implement teh Scalable and Accurate OnLine Approach to feature selection (SAOLA) to get most relevant features.
Trained different classification models on training samples from SAOLA; evaluated them on test samples.