I am a Professor of Computer Science at the King Abdullah University of Science and Technology (KAUST) and I served as Chair of the Computer Science program from 2014 to 2018. In 2009 I was on sabbatical at Stanford University. Before that, I was an assistant professor at the National University of Singapore (NUS).
In the past, I was involved in the designing and testing of VLSI chips. I also worked in several companies on database designing, e-commerce projects and web applications.
I have served as associate editor for the IEEE Transactions on Knowledge and Data Engineering (TKDE) from 2013 to 2015, and on the editorial board of the VLDB Journal from 2013 to 2017.
I received my Diploma in Computer Engineering from the Univ. of Patras, Greece in 1998, and my PhD from the Computer Science Dept., Hong Kong Univ. of Science and Technology (HKUST) in 2002.
My research interests include Big Data, Parallel and Distributed Systems, Large Graphs and Systems for Machine Learning. I use large computing infrastructures, such as the Shaheen II supercomputer. I am the leader of the InfoCloud @ KAUST group and a member of the Extreme Computing Research Center (ECRC) at KAUST.
Panos Kalnis
Professor of Computer Science, KAUST
Phone:
+966 12 808 0343
Email:
Address:
Building 1, Level 4, Room #4416
King Abdullah University of Science and Technology
4700 KAUST
Thuwal, 23955
Saudi Arabia
Selected publications
A Universal Question-Answering Platform for Knowledge Graphs
Omar R., Dhall I., Kalnis P., Mansour E.
2023
ACM SIGMOD
SLAMB: Accelerated Large Batch Training with Sparse Communication
Xu H., Zhang W., Fei F., Wu Y., Xie T.W., Huang J., Xie Y., Elhoseiny M., Kalnis P.
2023
Int. Conf. on Machine Learning (ICML)
Scaling Distributed Machine Learning with In-Network Aggregation
Sapio A., Canini M., Ho C.Y., Nelson J., Kalnis P., Kim C., Krishnamurthy A., Moshref M., Ports D., Richtarik P.
2021
USENIX Symposium on Networked Systems Design and Implementation (NSDI)
Rethinking Gradient Sparsification as Total Error Minimization
Sahu A.N., Dutta A., Abdelmoniem A., Banerjee T., Canini M., Kalnis P.
2021
Advances in Neural Information Processing Systems (NeurIPS)
GRACE: A Compressed Communication Framework for Distributed Machine Learning
Xu H., Ho C.Y., Abdelmoniem A., Dutta A., Bergou H., Karatsenidis K., Canini M, Kalnis P.
2021
IEEE Int. Conf. on Distributed Computing Systems (ICDCS)
A Sparse-tensor Communication Framework for Federated Deep Learning
Xu H., Kostopoulou K., Dutta A., Li X., Ntoulas A., Kalnis P.
2021
Advances in Neural Information Processing Systems (NeurIPS)
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Dutta A., Bergou E.H, Abdelmoniem A.M., Ho C.Y., Sahu A.N., Canini M., Kalnis P.
2020
American Association for Artificial Intelligence Conference (AAAI)
Matrix Algebra Framework for Portable, Scalable and Efficient Query Engines for RDF Graphs
Jamour F., Abdelaziz I., Chen Y., Kalnis P.
2019
EuroSys
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph
Elseidy M., Abdelhamid E., Skiadopoulos S., Kalnis P.
2014
Proceedings of the VLDB Endowment (PVLDB)
Private Queries in Location Based Services: Anonymizers are not Necessary
Ghinita G., Kalnis P., Khoshgozaran A., Shahabi C., Tan K.L.
2008
ACM SIGMOD