Scholar
Paul Suganthan
Google Scholar ID: jHIT6M0AAAAJ
Software Engineer at Google
Citations & Impact
Citations
6,735
H-index
12
i10-index
12
Publications
20
Co-authors
5
list available
Publications
Resume (English only)
Academic Achievements
- TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines, E. Caveness, Paul Suganthan G. C., Z. Peng, N. Polyzotis, S. Roy, M. Zinkevich. SIGMOD-20 Demo Track (To Appear)
- Entity Matching Meets Data Science: A Progress Report from the Magellan Project, Y. Govind, P. Konda, Paul Suganthan G. C., P. Martinkus, P. Nagarajan, A. Soundararajan, H. Li, S. Mudgal, J. Ballard, H. Zhang, A. Ardalan, S. Das, D. Paulsen, A. Singh Saini, E. Paulson, Y. Park, M. Carter, M. Sun, G. Fung, A. Doan. SIGMOD-19 Industrial Track
- Smurf: Self-Service String Matching Using Random Forests, Paul Suganthan G.C., Adel Ardalan, AnHai Doan, Aditya Akella. VLDB-19 (To Appear)
- CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching, Y. Govind, E. Paulson, P. Nagarajan, P. Suganthan G.C., A. Doan, Y. Park, G. Fung, D. Conathan, M. Carter, M. Sun. VLDB-18 demo
- Magellan: Toward Building Entity Matching Management Systems, P. Konda, S. Das, P. Suganthan G.C., P. Martinkus, A. Doan, A. Ardalan, J. R. Ballard, Y. Govind, H. Li, F. Panahi, H. Zhang, J. Naughton, S. Prasad, G. Krishnan, R. Deep, V. Raghavendra. SIGMOD Research Highlights 2017
Research Experience
- Prior to joining Google, he received his Ph.D. from the University of Wisconsin-Madison. Currently, he works as a software engineer at Google Research, focusing on the intersection of data management and machine learning as part of the Google Brain team.
Education
- Ph.D. in Computer Sciences from University of Wisconsin-Madison; Bachelors degree in Computer Science from College of Engineering Guindy, Anna University, India.
Background
- Software engineer at Google Research since March 2018, working as part of the Google Brain team to solve problems at the intersection of data management and machine learning (ML). Specifically, he is one of the core contributors of TensorFlow Data Validation, an open-source library that helps developers understand, validate, and monitor their ML data at scale.
