Speaker "Alok Aggarwal" Details Back



Moving from SAS to R: Do's and Dont's - A case Study


In this talk we describe one methodology for moving from SAS to R. We also discuss what to watch out for and how it can often be more tedious and time consuming that data analysts often think.


Brief Bio-data of Dr. Alok Aggarwal Dr. Alok Aggarwal received his B. Tech. from IIT Delhi in 1980 in Electrical Engineering and his Ph. D. from Johns Hopkins University in Electrical Engineering and Computer Science in 1984, after which he joined IBM’s T. J. Watson Research Center in New York. Since then, he has published 95 research articles and has been granted 8 patents from the US Patents and Trademark Office. During 1984 and 1993, he also won two innovation awards from IBM. Futhermore, during 1984 and 1996, Dr. Aggarwal served as a program chairman for a number of conferences, including Symposium on Theory of Computing, Foundations of Computer Science, and Symposium on Computational Geometry. He also served as a Chairperson of the IEEE Computer Society's Technical Committee on Mathematical Foundations of Computing and was on the editorial boards of SIAM Journal of Computing, Algorithmica, and Journal of Symbolic Computation. Finally, during the fall of 1988 and 1989, he was on sabbatical from IBM and taught two courses at the Massachusetts Institute of Technology (MIT) and supervised two Ph.D. students. During 1993 and 1996, along with other researchers at IBM, he built and sold a "Supply Chain Management Solution" for paper mills and steel mills. By optimizing schedules for paper machines, trimmers, winders, storage in warehouses, loading of trucks and rail-cars, and transportation, their solution was able to save a typical paper mill around 1.75% of revenue in operating costs. Hence, they published a seminal paper titled, “Cooperative Multi-objective Decision Support for the Paper Industry,” for which they won the Daniel H. Wagner prize for Excellence in Operations Research Practice from INFORMS in 1998. In July 1997, Dr. Aggarwal "founded" IBM’s India Research Laboratory, which he set-up inside the Indian Institute of Technology (IIT), Delhi. This Lab was inaugurated by the Indian Minister for Human Resources, Dr. Alagh, and the US Ambassador to India, Mr. Richard Celeste. Dr. Aggarwal started this Laboratory from "ground zero" and grew it to a 70-member team (with 35 PhDs and 35 Masters in Electrical Engineering, Computer Science, and in Business Administration) by July 2000. In August 2000, Dr. Aggarwal became the Director of Emerging Business Opportunities for IBM Research Division worldwide, and in this capacity, his responsibilities included converting technology innovations into business models and taking them to market to create profitable ventures. During 1998-2000, Dr. Aggarwal was a member of Executive Committee on Information Technology of the Confederation of the Indian Industry (CII) and the Telecom Committee of Federation of Indian Chamber of Commerce and Industry (FICCI). During 2002-2005, he was a charter member of The Indus Entrepreneur (TiE) organization and on the executive board of its New York chapter. In 2008, he received Distinguished Alumnus Award from IIT Delhi. In December 2000, he “co-founded” Evalueserve - a company that provides research, intellectual property, and analytics services to clients in North America, Europe and Asia Pacific from its five research centers in Delhi-Gurgaon, India; Shanghai, China; Cluj, Romania, Santiago-Valparaiso, Chile; and Raleigh, North Carolina. He was Evalueserve’s chairman until December 2013, and in 2003, along with his colleagues at Evalueserve, he pioneered the concept of “Knowledge Process Outsourcing (KPO)” and wrote the first article regarding KPO. Today, KPO is a well-known term in the outsourcing industry and there are more than 200 KPO companies in India alone; hence, Dr. Aggarwal has been quoted extensively by Wall Street Journal and other newspapers and magazines. In February 2014, Dr. Aggarwal founded Scry Analytics, a company that codifies different kinds of work-flows in various industries and uses artificial intelligence, machine learning, data mining as well as statistical analysis to improve their efficiency with respect to timeliness, quality, money earned, customer experience, compliance and aggregated risks.