Bio: Carlos Ordonez studied at UNAM University in Mexico, getting a B.Sc. in actuarial science
(applied math, similar to data science degrees)
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,
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 DBMS.
In 2006 Carlos joined the Department of Computer Science at the University of Houston,
where he currently leads the Big Data Systems (BDS) lab.
From 2013 to 2015 Carlos regularly visited MIT and
collaborated with Michael Stonebraker, working on new-generation parallel DBMSs (columnar, arrays).
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,
extending the R language and data quality with Divesh Srivastava.
His research projects have been funded by 3 NSF grants.
Research topics (overview):
Most of my scientific articles are listed on:
My citations are tracked in:
- Extending and optimizing data science languages, like Python and R, to analyze big data
- Scalable and Parallel algorithms for Big Data analytics: mainly machine learning and graphs.
- Eliminating RAM, interoperability and speed limitations from
data science programming languages (Python, R).
- Big data problems: classifying documents,
information retrieval on bibliography records,
keyword search, large-scale matrix multiplication,
ontology construction, linked data and semantic web.
- Data science applications in medicine:
heart disease diagnosis,
variable selection for cancer,
microarray data analysis,
- Older topics:
extending ER database models to manage data pre-processing,
managing analytic workflows, solving data quality issues, querying source code.
recursive queries, joins on graphs, cubes, skylines, pivoting,
workload optimization, data partitioning.