General Information

Carlos Ordonez
Associate Professor
Department of Computer Science
University of Houston
Houston TX, 77204
firstname AT uh DOT edu(to avoid spam)

My main research goal is to develop scalable and parallel algorithms available as libraries, tools and programs to analyze large data sets and big data in general. I am interested in both multi-threaded processing on a multicore CPU and multi-node processing with distributed memory and distributed storage. Target analytics span machine learning (e.g. clustering, classification, regression, dimensionality reduction, variable/feature selection, time series, deep neural networks) and graphs (paths, connectivity, clique detection, vertex neighborhood). After visiting MIT and working with Mike Stonebraker (Turing Award winner) I became interested in parallel systems with columnar and array storage. During a sabbatical at ATT Labs (formerly ATT Bell Labs) I worked with Divesh Srivastava on the R language runtime, analytics with R on streams and networking data. On the "Big Data Analytics" Hadoop side I have worked with MapReduce and Spark. Among other science applications I have worked on superconductivity, solar power, water pollution, microarray data, heart disease imaging and green computing. On the corporate side, I have extensive experience on telecommunication, insurance and retail data.

Research: Parallel Systems, Data Science

Bio: Carlos Ordonez studied at UNAM 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 with scalable processing techniques, getting the PhD in 2000. Carlos worked at NCR from 1998 to 2006, collaborating in the optimization of machine learning and cube query processing algorithms on the Teradata parallel database system. In 2006 Carlos joined the Department of Computer Science at the University of Houston, where he currently leads the Parallel Data Systems lab. From 2013 to 2015 Carlos collaborated with Michael Stonebraker, regularly visiting MIT. From July 2014 to July 2015 Carlos worked as a visiting researcher with ATT Labs-Research (formerly ATT Bell Labs), where he conducted research on stream analytics, the R language and data quality with Divesh Srivastava. His research projects have been funded by 3 NSF grants.
Research topics (overview):
  • Parallel and scalable algorithms (machine learning, graphs).
  • Eliminating RAM limitations from programming languages used in big data analytics (Python, R).
  • Analytics inside parallel systems (SQL, Spark, multi-threaded processing).
  • Query processing: recursive queries, joins on graphs, cubes, skylines, pivoting. `
  • Big data: large text files, web pages, documents, ontologies, semantic web.
  • Software engineering: ER database models, workflows, data quality, querying and debugging source code.
My scientific articles are listed on: DBLP. My citations are tracked in: Google Scholar.


  • B.Sc. in Applied Mathematics, UNAM University, Mexico, 1992.
  • M.S. in Computer Science, UNAM University, Mexico, 1996.
  • Ph.D. in Computer Science, Georgia Institute of Technology, USA, 2000.

International Academic Participation and Service

  • Associate Editor IEEE Transactions on Knowledge and Data Engineering (TKDE) 2018-2020.
  • Associate Editor Intelligent Data Analysis (IDA) 2010-2013.
  • Associate Editor Data & Knowledge Engineering (DKE) 2019-2021.
  • Program Chair: DOLAP 2010, DOLAP 2015.
  • Program Chair: DaWaK 2018, DaWaK 2019.
  • Program Chair: MEDI 2018.
  • PC member: DOLAP 2008-2020.
  • PC member: SIGMOD 2016, SIGMOD 2017.
  • PC member: AMW 2015, AMW 2016, AMW 2019, AMW 2020.
  • PC member: ADBIS 2020.
  • PC member: KDD 2014, KDD 2015.
  • PC member: IEEE Big Data 2016.