World Class Data Scientist
Data analytics is a craft. This is where theory meets practice. http://www.datasciencecentral.com/
Taught in Universities and Industry
Reviewed Data Science algorithms and code developed by others
I know that magic is to build an in-depth understanding of the problem domain and available data assets
• Real-world data experience
• Industry experience
• Many accomplishments: Always delivering world-class data science products
• In love with many programming languages
• Many long-term data jobs
• Honest, Great References
Post Doctorate in Data Science
Ryerson University Toronto, ON, Canada 2008
I found a theoretical solution for a Well-Known Statistical Mathematics Problem of Spectrum Calculation for complicated time series
The formulation of a time series non-linear transformation on terms of morphological image processing. In this way I developed a novel solution to solve an important time series processing challenge.
I found, and experimentally proved, a solution to the complex Medical Engineering time series problem of finding physiological changes in brain blood chemical properties due to tobacco smoking. PCA/ICA analysis of near infrared data measured from the brain
SpryLogics Inc. Toronto, ON, Canada 2015- 2016
Machine Learning (Deep Neural Networks and Conventional Machine Learning) for Natural Language Processing and Sport Predictive Analytics (coding in Python and R)
Twitter Sentiment Analysis and Language Identification for Social Media Predictive Analytics.
Vivosonic Inc. Toronto, ON, Canada 2008-2015
Developed and coded in Python worldwide best Neurological Data Science algorithm for physiological data buried in noise, resulting in increase on sales for best product in specific Medical Devices industry
Developed Data Classification Algorithms for Revolutionary monitoring of different stages of sleep and Surgical Anesthesia using Auditory Brain Response (Auditory stimulation and EEG/ERP (event-related EEG) signal processing)
Along with other neurological data algorithms, worked with ABR (auditory brainstem response) and ASSR (auditory steady state response) detection
Machine learning algorithms development to classify EEG data mixed with brain auditory evoked response. Combining several supervised machine learning methods. Data Visualisation, Acquisition, Cleaning and Processing implemented by Python, Matlab and C++.
Brain auditory response extraction from EEG by data de-noising and processing. Statistical inference about how brain auditory responses present in EEG.
Statistical inference (estimation/detection) about buried-in noise physiological signals in non-stationary additive noise – Physiological Radar Data Statistical Inference.
- deep machine learning
- Statistical Inference
- Natural Language Processing NLP
- Analytical and Numerical Statistical Analysis
- Analytical and Numerical Optimization
- Stochastic Data Computer Simulation
- Predictive Analytics
- Data Visualization and Summarization techniques for conveying key findings
- Algorithms Developed and Coded by Myself