MDC Faculty

My Background and Interests

Dr. Lee serves as part of the Data Analytics faculty at Miami Dade College (MDC) School of Engineering and Technology (EnTech).  In this capacity, he teaches several Data Analytics courses and serves the college through various committees. 

Dr. Lee's philosophy of education is that all students are unique and must have a stimulating educational environment where they can grow as a person. It is this desire to create an inclusive atmosphere where all students can safely and confidently share their ideas, take risks, and ultimately meet their full potential.  Dr. Lee is a strong believer that a diverse, equitable, and inclusive classroom is a strength.

"I believe that there are five essential elements that are conducive to learning.

(1) The professor's role is to act as a guide.

(2) Students must have access to hands-on activities.

(3) Students should be able to have choices and let their curiosity direct their learning.

(4) Students need the opportunity to practice skills in a safe environment.

(5) Although technology is integral to the learning process, the focus is on teaching skills (Teach the student but use Data Analytics as the tool)"

Dr. Lee's 30 years of industry experience includes, but is not limited to, roles such as Lead Research Scientist, Lead Data Scientist, Sr. Data Analyst, Blockchain Architect, Cloud Engineer, and Enterprise Architect.  His primary areas of expertise are as follows:

• Data Science (Deep Learning and Machine Learning)
• Data Analytics (Tableau and PowerBI)
• Data Wrangling
• Blockchain Development (HyperLedger Fabric and Ethereum)

Published Books:

• Data Analytics with Python
• Apache Spark (Cloud Computing, Big Data Series)
• Apache Kafka (Cloud Computing, Big Data)
• R Programming for Data Scientists and Analysts
• Many others

Published Academic Journals:

• Sentiment analysis and topic modeling on tweets about online education during COVID-19: Journal of Applied Sciences
• How do we build trust in machine learning models?: SSRN
• Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model: Scientific Reports
• Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model: IEEE Access
• Integrating Learning Analytics and Collaborative Learning for Improving Student’s Academic Performance: IEEE Access
• Malicious traffic detection in iot and local networks using stacked ensemble classifier: Computers, Materials, and Continua
• The Usefulness Of Visual Cryptography Techniques: Webology
• Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach: Journal of Advances in Astronomy
• Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model: IEEE Access
• Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning: Journal of Sensors
• Analysis and evaluation of barriers influencing blockchain implementation in Moroccan sustainable supply chain management: an integrated IFAHP-DEMATEL framework: Journal of Mathematics