2009 - ACM Distinguished Member
His primary areas of investigation include Data mining, Artificial intelligence, Data science, Machine learning and Social media. His Data mining study combines topics in areas such as Context, Algorithm design and Cluster analysis. Naren Ramakrishnan interconnects Event, Storytelling, Knowledge extraction and Medical record in the investigation of issues within Data science.
His studies in Event integrate themes in fields like Epidemic model, Graph theoretic, Information cascade, Content modeling and Popularity. His study in the field of Feature learning is also linked to topics like Coupling. His research integrates issues of Event forecasting, Vocabulary, Information discovery and Political economy in his study of Social media.
His main research concerns Data mining, Artificial intelligence, Data science, Machine learning and Social media. In most of his Data mining studies, his work intersects topics such as Cluster analysis. He has researched Artificial intelligence in several fields, including Pattern recognition and Natural language processing.
His Data science research is multidisciplinary, incorporating elements of Topic model, Event and Intelligence analysis. His study of Feature learning is a part of Machine learning. His work on Social media is being expanded to include thematically relevant topics such as Vocabulary.
Naren Ramakrishnan mostly deals with Artificial intelligence, Machine learning, Social media, Data mining and Artificial neural network. Much of his study explores Artificial intelligence relationship to Task. His Machine learning research integrates issues from Domain, Probabilistic logic, Computational epidemiology and Metric.
His work deals with themes such as Leverage, Computer security, Task analysis, Disease and Event, which intersect with Social media. The concepts of his Event study are interwoven with issues in Key, Narrative and Data science. His Data mining study focuses on Visual analytics in particular.
Artificial intelligence, Machine learning, Node, Data mining and Artificial neural network are his primary areas of study. His Artificial intelligence research incorporates themes from Schema, Computational epidemiology and Personalization. His work on Feature learning and Deep learning as part of general Machine learning research is frequently linked to Multiple methods and Future trend, thereby connecting diverse disciplines of science.
The various areas that he examines in his Node study include Theoretical computer science, Social network, Construct, Simple and Task analysis. His Theoretical computer science research includes themes of Embedding and Representation. Naren Ramakrishnan works on Data mining which deals in particular with Intrusion detection system.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Epidemiological modeling of news and rumors on Twitter
Fang Jin;Edward Dougherty;Parang Saraf;Yang Cao.
social network mining and analysis (2013)
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
Naren Ramakrishnan;Patrick Butler;Sathappan Muthiah;Nathan Self.
knowledge discovery and data mining (2014)
Privacy risks in recommender systems
N. Ramakrishnan;B.J. Keller;B.J. Mirza;A.Y. Grama.
IEEE Internet Computing (2001)
A systematic review of studies on forecasting the dynamics of influenza outbreaks.
Elaine O. Nsoesie;Elaine O. Nsoesie;Elaine O. Nsoesie;John S. Brownstein;John S. Brownstein;John S. Brownstein;Naren Ramakrishnan;Madhav V. Marathe;Madhav V. Marathe.
Influenza and Other Respiratory Viruses (2014)
Photosynthetic Acclimation Is Reflected in Specific Patterns of Gene Expression in Drought-Stressed Loblolly Pine
Jonathan I. Watkinson;Allan A. Sioson;Cecilia Vasquez-Robinet;Maulik Shukla.
Plant Physiology (2003)
The human is the loop: new directions for visual analytics
Alex Endert;M. Shahriar Hossain;Naren Ramakrishnan;Chris North.
intelligent information systems (2014)
Studying Recommendation Algorithms by Graph Analysis
Batul J. Mirza;Benjamin J. Keller;Naren Ramakrishnan.
intelligent information systems (2003)
Misinformation Propagation in the Age of Twitter
Fang Jin;Wei Wang;Liang Zhao;Edward Dougherty.
IEEE Computer (2014)
Data mining: from serendipity to science
N. Ramakrishnan;A.Y. Grama.
IEEE Computer (1999)
Multi-Task Learning for Spatio-Temporal Event Forecasting
Liang Zhao;Qian Sun;Jieping Ye;Feng Chen.
knowledge discovery and data mining (2015)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: