BOSTON, MA - HOUSTON, TX
DAta science & AI Conference
Hosted on youtube
Event Starts In
The week of Nov 30/Dec 1, 2019
About The Conference
This conference is the only of its kind in Data Science . It is the unique opportunity for you to attend a Data Science conference that is streamed over Youtube. Participants can join from anywhere without spending big amount on air tickets, hotels, transportation. The conference is chaired across United States in two large cities Boston and Houson.
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Nguyet Nguyen, Ph.D
Assistant Professor, Youngstown State University
Dr. Nguyen received a Ph.D degree in Financial Mathematics at Florida State University in 2014.
Joe Lucibello is a Lead Data Scientist at What If Media Group. Joe is a hands-on leader that directs a team of data scientists as they develop data-driven solutions using machine learning and modeling techniques. Joe’s work drives the future advertising brain at What If Media Group.
Prior to working at What If Media Group, Joe was a Senior Manager, Data Scientist at WWE and held multiple titles, including, Manager of Data Science at ESPN
Software Engineer, Symantec, Pune, India
Akshay is a Google Expert Developer. He has been giving talks at various Data Science & Machine Learning Conferences
Economics Data Scientist, University of Connecticut
Shiyi Chen is a Ph.D Candidate in Economics with interests in Applied Machine Learning. She expects to receive a Ph.D degree in 2020.
Nam Nguyen, Ph.D
Senior Data Scientist, Schlumberger
Dr. Nguyen received a Ph.D degree in Electrical & Computer Engineering at University of Houston in 2013.
SOLOMON BERHE, PH.D
Data Science Software Engineer
Dr. Berhe received a Ph.D degree in Computer Science from the University of Connecticut.
Hien Nguyen, Ph.D
Lecturer, University of Information Technology, VNU-HCM
Economics Data Scientist, University of Connecticut
Haiyang is a Ph.D Candidate in Economics with interests in Applied Machine Learning. Haiyang teaches a Data Science Boot Camp at the university. He expects to receive a Ph.D degree in 2020.
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Unsupervised Machine-Learning Hidden Markov Model for Global Stock Trading
In this talk, we introduce a multi-step procedure for using HMM to select stocks from the global stock market. We show that HMM outperforms the benchmark global index and the equal weight model, being an efficient approach for global stock trading
Speaker: Nguyet Nguyen, Ph.D, Assistant Professor at Youngstown State University, OH, USA
Related paper for code downloads: https://www.soa.org/resources/research-reports/2017/2017-hidden-markov-model-portfolio-mgmt/
Resource Utilization as a Metric for Machine Learning
The advent of machine learning along with its integration with big data has enabled users to efficiently to develop solutions for innumerable use cases. A machine learning model consists of an algorithm which draws some meaningful correlation between the data without being tightly coupled to a specific set of rules. It's crucial to explain the subtle nuances of the network along with the use-case we are trying to solve. With the advent of technology, the quantity of data has increased which in turn has increased the need for resources to process the data while building a model. The main question, however, is to discuss the need to develop lightweight models keeping the performance of the system intact. To connect the dots, we will talk about the development of these applications specifically aimed to provide equally accurate results without using much of the resources. This is achieved by using image processing techniques along with optimizing the network architecture.
Speaker: Akshay Bahadur, Google Expert Developer, Software Engineer at Symantec, Pune, India
Fail Fast: How the 80-20 Rule Dominates Data Science
Ever wonder why the most polished model doesn’t make it into production? Why the person most well-versed in modeling techniques doesn’t always generate the greatest impact? Why simple heuristics or business rules stand in place of more accurate complex modeling techniques? The Pareto Principle, which posits that 20% of your work accounts for 80% of your results, dictates the impact data science can have on an enterprise. How can a data scientist differentiate themselves in the ever-growing homogeneous data science market? Fail fast and get results!
Speaker: Joseph Lucibello, Lead Data Scientist at What If Media Group, USA
Some Measures to Detect the Influencers on Social Networks based on Information Propagation
We propose a model representing a social network. The network includes two main objects: Users and Tags. This model can represent relationships between these objects clearly. We build influence measures for the ability of the influence on other people by relationships between users and the concern to user’s tags, and the speed of the user’s tag propagation on the social network. We solve the problem about determining the influencer of a specific brands/products/news on online social networks.
Speaker: Hien D. Nguyen, Ph.D, Lecturer at University of Information Technology, Vietnam
Stacked Regression for Prediction Tasks
In this talk, I will explain various modern regression techniques that have proved their effectiveness in Machine Learning competitions on Kaggle. I describe intuitively the ideas behind powerful techniques such as bagging, boosting, and stacking regressions, and the main difference between them.
Speaker: Nam Nguyen, Ph.D, Senior Data Scientist at Schlumberger, Houston, TX
Building a Recommender System
In this talk, I will discuss several types of recommender systems, including Content-Based Recommender Systems, and Collaborative Filtering Recommender Systems, and walk you through examples using Python.
Speaker: Haiyang Kong, Economics Data Scientist