Carlos Ordonez studied at UNAM University in Mexico (top research university in Mexico), getting a B.Sc. in applied mathematics and an M.S. in computer science. He continued PhD studies at the Georgia Institute of Technology advised by Edward Omiecinski, focusing on accelerating machine learning algorithms and removing main memory limitations with query processing and external memory algorithms. During his PhD, Carlos joined NCR Corporation collaborating in the optimization of machine learning and cube query processing algorithms on the Teradata parallel DBMS, under an SMP architecture with distributed storage. After working 8 years at NCR, Carlos joined the Department of Computer Science at the University of Houston, where he currently leads the Scalable Algorithms group. During 2012 and 2013 Carlos regularly visited MIT, collaborating with Michael Stonebraker, working on new-generation parallel DBMSs (columnar, array, lockfree transactions) to solve large-scale linear algebra and graph problems. Carlos worked as a visiting researcher with ATT Labs-Research (formerly ATT Bell Labs), where he conducted research on stream analytics on a massive data lake. His research projects have been funded by NSF and NIH grants.