Scholar
Ke Zhang
Google Scholar ID: SkwHDa4AAAAJ
Dataminr
Citations & Impact
Citations
749
H-index
12
i10-index
17
Publications
20
Co-authors
14
list available
Contact
No contact links provided.
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Led research and development on multiple projects and initiatives such as Natural Language Generation with Deep Learning for event summarization from online social media posts, Text Classification with Deep Learning for real-time event detection from online social media data, Spatio-temporal data mining and machine learning/deep learning for real-time event and anomaly detection from sensor data.
Research Experience
- AI Platform, Staff Research Scientist at Dataminr, inc., NYC, May 2025 - Present; Senior Research Scientist, AI & Data Science at Dataminr, inc., NYC, Jan 2022 - April 2025; Research Scientist II, AI & Data Science at Dataminr, inc., NYC, June 2019 - Dec 2021; Senior Member of Technical Staff at AT&T Labs Research, Bedminster, NJ, Nov 2016 - June 2019; Software Engineering Intern at TuSimple LLC, San Diego, CA, May 2016 - Aug 2016; Research Intern at NEC Labs America, Princeton, NJ, Jun 2015 - Nov 2015.
Education
- PhD in Information Sciences from the University of Pittsburgh, Jan 2012 - Oct 2016, Advisor: Prof. Konstantinos Pelechrinis; M.S. in Communication and Information System from Huazhong University of Science and Technology, Sep 2009 - Dec 2011; B.S. in Telecommunication Engineering from Huazhong University of Science and Technology, Sep 2005 - Jun 2009.
Background
- Currently working as an AI Research Scientist and Tech Lead at Dataminr, focusing on R&D in data mining, machine learning, and natural language processing for real-time event discovery and summarization. Previously, worked as a Senior Member of Technical Staff at AT&T Labs Research, with R&D focusing on spatial-temporal data mining, applied machine learning, and deep learning on large-scale spatially distributed networking data, to automate dynamic resource management and heterogeneous 4G/5G network planning. With 1-year R&D internship experiences at NEC Labs America, TuSimple, and MSKCC in predictive modeling, text mining, and anomaly detection, received a PhD from the School of Computing and Information at the University of Pittsburgh, with a background in data mining, machine learning, network science, and statistics. The PhD thesis focuses on large-scale geo-social network data mining to model spatial, social, and temporal aspects of user mobility behaviors in the physical world and its applications to local economy.
Miscellany
- Passionate about learning insights from various structured/unstructured datasets, communicating findings to scientists, engineers, and non-experts via publications/presentations/demos, as well as delivering data-driven software/products to drive intelligent and automated businesses/services.
