Hi, I am Ken ...

• Seeking for Machine Learning and Data Science Summer Internship positions, 2018.

• Ph.D. Student in Computer Science Department at North Carolina State University.

• Data Science and Machine Learning Researcher at the RAISE Lab (NCSU).


During the day I'm a Data Scientist and Machine Learning Researcher in the making. I am passionate in solving real-world problems empirically. My prime areas of interest are Machine Learning, Data Science, and Algorithms specifically in Text Analytics, Computer Vision and Graph Mining.

Beside daily learning and researching, I love reading, running, cooking, playing badminton, and playing board games.

Education

North Carolina State University

PhD in Computer Science May 2016 - May 2019

I work with Dr Tim Menzies on real-time artificial intelligent for software engineering problems. I work on estimating software effort, optimizing requirements engineering problems and analyzing trends in software engineering publications.

Appalachian State University

Bachelor of Science in Computational Mathematics August 2011 - May 2015

Minor: Computer Science

Graduated with Magna Cum Laude, 3.80/4.0. Top 5% of my graduated class.

Work

North Carolina State University

Graduate Teaching and Research Assistant January 2015 - Present

  • Guided by Dr Tim Menzies, a pioneer in automated software engineering, search based software engineering and data mining in software engineering
  • Current Project: studying and analyzing the sentiment and purposes of scientific citations of SE research papers while optimizing and stabilizing the state-of-the-art learners
  • Statistical tests to measure correctness of estimation techniques. ANOVA, A12, Bootstrap and Cliffs-Delta are some of the methods adopted
  • Coordinate with the professor & other Teaching Assistants as a team to consolidate plans, structure the course, design tests, conduct review sessions, facilitate labs, and deliver the lesson effectively

YouNet Corporation

R&D Data Science Intern May 2017 - August 2017

  • Researched and designed scalable data-driven algorithms from machine learning and neural network methodologies.
  • Analyzed the semantics of data, developed model, optimized performance, & deployed projects to solve business problems.
  • Devised and enhanced core products in the system: Facial Beauty Rating, Facebook User’s Age Prediction with Graph Clustering and Text Analytics, & Car Detection.

OverMountain Studios

Web Developer Intern May 2016 - August 2016

  • Designed, built, and maintained client’s application with AngularJS & Bootstrap front-end and a NodeJS backend.
  • Tested functionality of client websites, troubleshoot for issues and re-structured websites for scalability and usage.

University Housing at Appalachian State University

Head Resident Assistant January 2013 - August 2015

  • Organized building wide and cross building wide programs that fostered community for 500+ students
  • Expressed the voice of Resident Assistants and students on campus through composing policy and legislation proposals from Resident Assistant Council to University Housing Leadership
  • Directly advised and collaborated with the new Resident Assistant(s) and Chair of the residence hall council professionally and efficiently to help develop their leadership
  • Ensured the enforcement of legal and university policy through campus wide duty rounds to provide a safe and inclusive living environment for all members of the Appalachian State community.

Projects

Facebook User’s Age Prediction

Mined and investigated unstructured 25k Facebook users’ posts through Vietnamese language processing with traditional machine learning methods, convolutional neural networks, & LSTM to predict the user’s age. Achieved accuracy = 79.6%.

Facial Beauty Rating

Developed a facial attractiveness rater based on the SCUT-FBP dataset, contains images of 500 Asian women and applying open-source OpenFace software to extract facial landmarks as features. With Pearson correlation of 0.86, convolutional neural networks outperform traditional machine learning method such as Random Forest (only achieved 0.64).

Sentiment Analysis

Built a recommendation system with Apache Spark and Alternative Least Square algorithm integrated users’ reviews sentiment analysis along with other attributes on the 4.5+ million reviews and 146k+ businesses Yelp dataset to suggest new businesses that are appropriate to the users’ interests.

Graph Embedding for Recommender System

Implemented DeepWalk as a graph-based technique to recommend the viewer-movie pair by evaluating and preference propagation algorithms (word2vec model) in heterogeneous information networks generated from user-item relationships to recommend the viewer-movie pair.

Developer Triage Bot

Developed a bot on AWS that assigns & suggests suitable tasks for the developer along with recommending experienced developers in the team to help them if needed in real time through Slack interface and Github task tracking.