EECS500 Fall 2016 Department Colloquium

Khosrow Hassibi, Ph.D.
What is Data Science and How to Succeed as A Data Scientist
White 411
11:30 AM - 12:30 PM
November 15, 2016

The use of machine learning and data mining to create value from corporate or public data is nothing new. It is not the first time that these technologies are in the spotlight. Many remember the late ‘80s and the early ‘90s when machine learning techniques—in particular neural networks—had become very popular. Data mining was at a rise. There were talks everywhere about advanced analysis of data for decision making. Even the popular android character in “Star Trek: The Next Generation” had been named appropriately as “Data.” Data science has been the cornerstone of many data products and applications for more than two decades, e.g., in finance, Telco, and retail. Credit scores have been in use for decades to assess credit worthiness of people when applying for credit or loan. Sophisticated real-time fraud scores based on individual’s transaction spending patterns have been used since early ‘90s to protect credit card holders from a variety of fraud schemes. However, the popularity of web products from the likes of Google, Linked-in, Amazon, and Facebook has helped analytics become a household name. Every new technology comes with lots of hype and many new buzzwords. Often, fact and fiction get mixed-up making it impossible for outsiders to assess the technology’s true relevance. Due to the exponential growth of data, today there is an ever increasing need to process and analyze big data which has required a rethinking of every aspect of the data science life cycle, from data management, to data mining and analysis, to deployment. The purpose of this talk is first to describe what data science is and how it has evolved historically. Second, I share my own experiences as a data scientist across different industries and through time with the audience emphasizing the challenges and rewards. 


Professional Work

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.



Dr. Hassibi holds a Ph.D. in ECS from Case Western Reserve University.  His graduate work concentrated on Statistical Pattern Recognition, Neural Networks (AI), and Multi-sensor Robotics systems.  His interest in Neural Networks was a result of his work at Case Western Center for Automation and Intelligent Systems Research. On the business management side, he is a graduate of “executive program in management” at UCLA Anderson School of Management and the Leadership and Management program at UCSD.