Mike Casey has over 25 years of financial and operational experience with a proven track record of execution, having served as CFO in leading public software and technology companies including MAPICS, iXL Enterprises, Manhattan Associates and IQ Software.
Prior to joining TechCFO, Mike was the CFO at MAPICS, a publicly traded, global enterprise resource planning software provider for the manufacturing industry. At MAPICS, he helped restructure operations, acquire and restructure a competitor which drove revenue and earnings growth leading to its sale to Infor Global Solutions for more than $350 million.
Prior to joining MAPICS, Mike served as CFO and Executive Vice President Finance and Administration for iXL Enterprises, Inc., a publicly traded, global Internet services consulting firm, where he directed all financial, human resource, IT, facilities and administrative functions during its restructuring and its merger with Scient, Inc.
Previously, Mike served as CFO of Manhattan Associates, Inc., a publicly traded developer of supply chain execution systems, during rapid growth and its successful initial public offering. Mike's experience also includes serving as CFO for IQ Software Corporation, a publicly traded developer of business intelligence solutions, during its growth and successful initial public offering.
Mike began his career as a CPA at Arthur Andersen, where he specialized in serving the technology and communications industries. Mike earned a Bachelor of Business Administration degree in Accounting from the University of Georgia.
Alexander Gray received bachelor's degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University, and worked in the Machine Learning Systems Group of NASA's Jet Propulsion Laboratory for 6 years. He currently directs the FASTlab (Fundamental Algorithmic and Statistical Tools Laboratory) at Georgia Tech, consisting of 25 people including 14 PhD students, which works on the problem of how to perform machine learning/data mining/statistics on massive datasets, and related problems in scientific computing and applied mathematics. Employing a multi-disciplinary array of technical ideas (from machine learning, nonparametric statistics, convex optimization, linear algebra, discrete algorithms and data structures, computational geometry, computational physics, Monte Carlo methods, distributed computing, and automated theorem proving), the lab has developed the current fastest algorithms for several fundamental statistical methods, and also develops new machine learning methods for difficult aspects of real-world data, such as in astrophysics and biology. This work has enabled high-profile scientific results which have been featured in Science and Nature, and has received an NSF CAREER award, two best paper awards, and two best paper award nominations. He has given tutorials for the field and invited talks on efficient algorithms for machine learning at venues including ICML, NIPS, SIAM Data Mining, as well as in applied mathematics and astronomy.