Khuyen Tran - AI/ML Curation Advisor - ADEPT Artificial Intelligence, Machine Learning and Data Science
We found Khuyen as one of her 100K+ viewers per month on AI, Machine Learning and Data Science articles. Her ability to identify trends, distill and explain difficult topics in prose or by visualization, showcases her unique insights and communication abilities. Beyond her technical writing she is accomplished at identifying and trying out new methods, technologies and approaches in Artificial Intelligence, Machine Learning and Data Science. Her hands-on skills combined with a keen eye to trending AI/ML topics makes her a valuable advisor for ADEPT AI, Machine Learning and Data Science curation.
Khuyen’s education is highly project based via hands-on programs as well as academically in maintaining a cumulative 4.0 GPA in Applied Math with Honors from Southern Illinois University Edwardsville (SIUE) where she achieved a Mathematical Tools for Data Science in CIMAT.
Khuyen demonstrates a commendable interest in giving back to the community through her leadership, mentoring, writing and presentation of complex topics in clear and understandable ways. Here are just a few examples:
Leadership
We found Khuyen as one of her 100K+ viewers per month on AI, Machine Learning and Data Science articles. Her ability to identify trends, distill and explain difficult topics in prose or by visualization, showcases her unique insights and communication abilities. Beyond her technical writing she is accomplished at identifying and trying out new methods, technologies and approaches in Artificial Intelligence, Machine Learning and Data Science. Her hands-on skills combined with a keen eye to trending AI/ML topics makes her a valuable advisor for ADEPT AI, Machine Learning and Data Science curation.
Khuyen’s education is highly project based via hands-on programs as well as academically in maintaining a cumulative 4.0 GPA in Applied Math with Honors from Southern Illinois University Edwardsville (SIUE) where she achieved a Mathematical Tools for Data Science in CIMAT.
Khuyen demonstrates a commendable interest in giving back to the community through her leadership, mentoring, writing and presentation of complex topics in clear and understandable ways. Here are just a few examples:
Leadership
- Mentored over 10 students to improve from course grade C to A in statistics and mathematics
- Mentored over 60 students of diverse learning backgrounds to master calculus knowledge
- Devised strategic plans with other leaders and professors to deliver complex concepts in comprehensible ways
- Towards Data Science technical reviewer authorizing more than 40 articles in AI, machine learning and data science
- Simplified complex mathematical and programming concepts with insights and interactive visualizations.
- Authorized more than 40 articles on topics of Natural Language Processing, web scraping, data science tools, and mathematical programming with more than 110k views a month
- Presented complex topics via visualizations and other means of clarification
- NLP/Neural Network
- Worked with Dr. Andrew Trapp from Worcester Polytechnic Institute (WPI) to develop a scheduling app with Machine Learning and Integer Programming to predictively identify the most effective schedule and maximize productivity.
- Tools: integer programming with Pulp-optimization in Python, Gurobi and natural language processing for task classification.
- Formulated metaheuristic time efficient method to provide optimal solution for incomplete information, with Dr.Chew of SIUE Dept of Mathematics and Statistics, which is especially useful for neural network training
- Achieved 10-6 accuracy when tested on multidimensional functions
- Developed Gender and Language Predictor for Twitter. Experimented with 3 different ML models and achieved f1-score of up to .81 for gender prediction and .84 for language variety.
- Tools: NLTK (natural language toolkit) for preprocessing raw texts, scikit-learn (machine learning in Python), BERT (natural language processing), and neural network for model training and prediction.
- Worked with Dr. Andrew Trapp from Worcester Polytechnic Institute (WPI) to develop a scheduling app with Machine Learning and Integer Programming to predictively identify the most effective schedule and maximize productivity.
Yassine B., PH.D. - Natural Language Processing and Deep Learning Expert
• Adjunct Professor of NLP at GWU
• Ph.D. in AI with special focus on NLP
• Active researcher and reviewer in the most prestigious NLP conferences/journals
• More than 12 years of experience in the field
• Worked with renowned research teams in IBM T.J. Watson, Philips Research North America and Thomson Reuters.
• Researched and developed NLP frameworks in the military, clinical, legal, financial and market research domains
• Expertise in training, testing and deploying Deep Learning models for real world applications
• Experience managing a team of data scientists and managing large projects from idea to a running pipeline
Bernard O. - Data Science and Machine Learning champion and evangelist
• Part-time instructor at NYC Data Science Academy
• Certified Data Scientist involved in data strategy and hands on expertise on machine learning, big data, advanced statistics and analytics, development, and visualization.
• Ranked #1 in the Kaggle Consumer Credit Default challenge (closed competition) to accurately predict probability of consumer going into financial distress after 2 years.
• Ranked Top 2% in the Kaggle Higgs Boson challenge (closed competition) leading a team of Data Scientists through the implementation of advanced Python machine learning model ensembles, stacking, and feature engineering demonstrating high accuracy rates in predictive analytics
• Expertise in modeling/machine learning platforms using R and Python
• Classification methods (Neural Net, Logistic Regression, Decision Trees, KNN, SVM, Random Forest)
• Regression methods (Linear, Nonlinear, Boosted Regression Trees )
• Clustering methods (K-means, Fuzzy C-means, Hierarchical Clustering, Mixture Modelling)
• Part-time instructor at NYC Data Science Academy
• Certified Data Scientist involved in data strategy and hands on expertise on machine learning, big data, advanced statistics and analytics, development, and visualization.
• Ranked #1 in the Kaggle Consumer Credit Default challenge (closed competition) to accurately predict probability of consumer going into financial distress after 2 years.
• Ranked Top 2% in the Kaggle Higgs Boson challenge (closed competition) leading a team of Data Scientists through the implementation of advanced Python machine learning model ensembles, stacking, and feature engineering demonstrating high accuracy rates in predictive analytics
• Expertise in modeling/machine learning platforms using R and Python
• Classification methods (Neural Net, Logistic Regression, Decision Trees, KNN, SVM, Random Forest)
• Regression methods (Linear, Nonlinear, Boosted Regression Trees )
• Clustering methods (K-means, Fuzzy C-means, Hierarchical Clustering, Mixture Modelling)
Jonathan M. - Computer scientist: Artificial Intelligence, machine learning and data science
Co-founder and CEO of an artificial intelligence company focusing on natural language processing. The flagship product is a grammar checker based on deep learning. Our product recognizes that a good grammar checker must understand what we intended to write to know if it was written correctly.
Adjunct Professor
Taught the undergraduate honors artificial intelligence class. We covered search, game trees, Bayesian inference, reinforcement learning, neural networks, particle filters, hidden Markov models, Bayesian networks, and planning.
Post-Doctoral Fellow - Carnegie Mellon University
Jonathan's research focused on using machine learning to help users specify privacy and other policies. Led a team in the maintenance and development of Locaccino, a social network for location tracking. Locaccino runs on a user's smartphone and allows a user to broadcast his or her location subject to privacy constraints. I mentored students and performed research on using machine learning to help user's specify privacy policies for Locaccino.
Co-founder and CEO of an artificial intelligence company focusing on natural language processing. The flagship product is a grammar checker based on deep learning. Our product recognizes that a good grammar checker must understand what we intended to write to know if it was written correctly.
Adjunct Professor
Taught the undergraduate honors artificial intelligence class. We covered search, game trees, Bayesian inference, reinforcement learning, neural networks, particle filters, hidden Markov models, Bayesian networks, and planning.
Post-Doctoral Fellow - Carnegie Mellon University
Jonathan's research focused on using machine learning to help users specify privacy and other policies. Led a team in the maintenance and development of Locaccino, a social network for location tracking. Locaccino runs on a user's smartphone and allows a user to broadcast his or her location subject to privacy constraints. I mentored students and performed research on using machine learning to help user's specify privacy policies for Locaccino.
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