Khosrow Hassibi
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Short Bio:
Dr. Hassibi is an expert, practitioner, and thought leader in the areas of data mining, machine learning, and statistical pattern recognition. His expertise is based on twenty years of design, R&D, consulting/sales, and management in applying these technologies to hard real-world business problems such as real-time fraud detection, hand-print OCR, marketing, risk, preventive maintenance, and transactional customer behavior analysis. Dr. Hassibi is recognized for his contributions to real-time payment card fraud detection and has been a part of four machine learning startups focused on new data products and analytics-based business solutions. Most recently, he has been with SAS Institute and Cablevision with his main interest focused on big data analytics and machine learning applications in financial and telecommunication industries.
Altice USA
Job Title:
Senior Principal, Head of Data Science
Software, Financial, Telco, ...
LinkedIn Profile:
Finding a new position, Networking, New venture, Recruiting, Other

Khosrow Hassibi's Blog

Achieving Analytics Sophistications

Posted on November 20, 2017 at 8:30am 0 Comments


In today’s world, a successful organization has to achieve data and analytics competencies to stay…


Data Science Best Practices

Posted on November 20, 2017 at 8:30am 0 Comments



1. Introduction

2. The Four Pillars of Success        in Analytics

3. Analytics…


Image Recognition Revolution and Applications

Posted on September 21, 2017 at 8:30am 0 Comments

Data, in particular, unstructured data has been growing at a very fast pace since mid-2000’s. Eighty percent of all data generated is unstructured multimedia content which fails to get focus in organizations’ big data initiatives. A good portion of this multimedia content is images and videos. Readily available smart wireless devices along with the rising popularity…


Machine Learning vs. Traditional Statistics: Different philosophies, Different Approaches

Posted on October 28, 2016 at 7:00am 5 Comments

"Machine Learning (ML)" and "Traditional Statistics(TS)" have different philosophies in their approaches. With "Data Science" in the forefront getting lots of attention and interest, I like to dedicate this blog to discuss the differentiation between the two. I often see discussions and arguments between statisticians and data miners/machine learning practitioners on the definition of "data science" and its coverage and the required skill sets. All is needed, is just paying attention to the…


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