D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 57 Citations 9,110 445 World Ranking 2594 National Ranking 1384

Research.com Recognitions

Awards & Achievements

2009 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Operating system

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 most cited work include:

  • Epidemiological modeling of news and rumors on Twitter (201 citations)
  • Privacy risks in recommender systems (191 citations)
  • 'Beating the news' with EMBERS: forecasting civil unrest using open source indicators (139 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Data mining (23.23%)
  • Artificial intelligence (23.87%)
  • Data science (16.34%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (23.87%)
  • Machine learning (16.56%)
  • Social media (10.54%)

In recent papers he was focusing on the following fields of study:

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.

Between 2016 and 2021, his most popular works were:

  • Results from the second year of a collaborative effort to forecast influenza seasons in the United States. (60 citations)
  • Deep Reinforcement Learning for Sequence-to-Sequence Models (51 citations)
  • Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media (44 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

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.

Best Publications

Epidemiological modeling of news and rumors on Twitter

Fang Jin;Edward Dougherty;Parang Saraf;Yang Cao.
social network mining and analysis (2013)

345 Citations

'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)

244 Citations

Privacy risks in recommender systems

N. Ramakrishnan;B.J. Keller;B.J. Mirza;A.Y. Grama.
IEEE Internet Computing (2001)

239 Citations

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)

191 Citations

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)

182 Citations

The human is the loop: new directions for visual analytics

Alex Endert;M. Shahriar Hossain;Naren Ramakrishnan;Chris North.
intelligent information systems (2014)

171 Citations

Studying Recommendation Algorithms by Graph Analysis

Batul J. Mirza;Benjamin J. Keller;Naren Ramakrishnan.
intelligent information systems (2003)

170 Citations

Misinformation Propagation in the Age of Twitter

Fang Jin;Wei Wang;Liang Zhao;Edward Dougherty.
IEEE Computer (2014)

168 Citations

Data mining: from serendipity to science

N. Ramakrishnan;A.Y. Grama.
IEEE Computer (1999)

151 Citations

Multi-Task Learning for Spatio-Temporal Event Forecasting

Liang Zhao;Qian Sun;Jieping Ye;Feng Chen.
knowledge discovery and data mining (2015)

148 Citations

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