Kia Teymourian
Assistant Professor of Instruction
Kia Teymourian's core research interest is focusing on the challenges of real-time information extraction from big data streams. Prior to coming to UT, he was an Assistant Professor at Boston University, and before that, he was a postdoctoral researcher at Rice University. He holds a PhD in Computer Science from Freie Universität Berlin where he was a researcher and Ph.D. student from 2008 to 2014.
Research
Research Interests:
- Database Systems
- Data Analytics
- Distributed Event-Based Systems
- Data Stream Processing
- Knowledge-based Complex Event Processing
- Large-scale Semantic-Enabled Distributed Information Systems
Select Publications
Sambasiva Rao Gangineni, Harshad Reddy Nalla, Saeed Fathollahzadeh, and Kia Teymourian. 2019. Real-Time Object Recognition from Streaming LiDAR Point Cloud Data. pp. 214–219.
Jia Zou, R. Matthew Barnett, Tania Lorido-Botran, Shangyu Luo, Carlos Monroy, Sourav Sikdar, Kia Teymourian, Binhang Yuan, and Chris Jermaine. 2018. PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development.
Sourav Sikdar, Kia Teymourian, and Chris Jermaine. 2017. An experimental comparison of complex object implementations for big data systems.
Dimitrije Jankov, Sourav Sikdar, Rohan Mukherjee, Kia Teymourian, and Chris Jermaine. 2017. Real-time High Performance Anomaly Detection over Data Streams: Grand Challenge.
Jia Zou, R. Matthew Barnett, Tania Lorido-Botran, Shangyu Luo, Carlos Monroy, Sourav Sikdar, Kia Teymourian, Binhang Yuan, and Chris Jermaine. 2017. PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development.
Awards & Honors
2019 -
Best Grand Challenge Solution Award at 13th ACM International Conference on Distributed and EventBased Systems
