Siddharth Krishnan Assistant Professor
Computer Science Department
University of North Carolina - Charlotte

About Me

I am an Assistant Professor in the Department of Computer Science at the University of North Carolina at Charlotte. Go 49ers!!

At UNC-Charlotte, I direct the Network Analytics and Social Computing Lab (NASCL) [webpage under construction]. I am also an affiliate faculty member of the Complex Systems Instiute and the School of Data Science.

I received a Ph.D. from the Computer Science Department at Virginia Tech. Prior to joining Virginia Tech, I obtained an M.S. (Computer Science) from the Computer Science department at Florida State University. So I guess I am a Seminole first and then a Hokie!
I attended Sri Sathya Sai Univesity in India, where I obtained an integrated B.S./M.S. in Mathematics.

Research Interests

How do memes spread on Facebook? How and when does a hashtag become popular? Can we forecast/predict viral content? How can we harness information cascades to make 'real-world' predictions? I am broadly interested in web-mining, data analytics, computational social science, and applied machine learning with a primary emphasis on analyzing, characterizing, and forecasting information (news, rumors, memes, advertisements, etc.) dynamics on online social networks & social media. Furthermore, my research aims to leverage dynamical processes (like cascade propagation) to build explanatory & predictive models of actions of large groups of people and societies.

Publications: Refereed Journals and Conference Proceedings

(in reverse chronological order)

  1. Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency [PDF]
    L. Safarnejad, Q. Xu, Y. Ge, A. Bagavathi, S. Krishnan, and S. Chen
    in the American Journal of Public Health 110, S340-S347, 2020
  2. DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection [PDF]
    J. Melton, A. Bagavathi, and S. Krishnan
    in the IEEE International Conference on Machine Learning Applications (ICMLA), 2020
  3. Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models [PDF]
    M. Ridenhour, A. Bagavathi, E. Raisi, and S. Krishnan
    in the Springer International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction, and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2020
  4. Identifying Influential Factors on Discussion Dynamics of Emerging Health Issues on Social Media: A Computational Study [PDF]
    L. Safarnejad, Q. Xu, Y. Ge, A. Bagavathi, S. Krishnan, and S. Chen;
    in the JMIR (Journal of Medical Internet Research) Public Health and Surveillance, 2020
  5. ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert [PDF]
    Arunkumar Bagavathi, Siddharth Krishnan, Sanjay Subrahmanyan, and S.L. Narasimhan
    in the IEEE International Conference On Machine Learning Applications, Boca Raton, FL, 2019
  6. Extracting Cryptocurrency Price Movements From the Reddit Network Sentiment [PDF]
    with A. Bagavathi, S. Wooley, A. Edmonds, and S. Krishnan
    in the IEEE International Conference On Machine Learning Applications, Boca Raton, FL, 2019
  7. Examining Untempered Social Media: Analyzing Cascades of Polarized Conversation [PDF]
    with A. Bagavathi, P. Bashiri, S. Reid, M. Phillips, and S. Krishnan
    in the IEEE/ACM Advnaces in Social Network Analysis and Mining (ASONAM), Vancouver, Canada, 2019
  8. MultiNet: Scalable Multilayer Network Embeddings [PDF]
    A. Bagavathi and S. Krishnan
    in the Springer International Conference on Complex Networks and Applications, Cambridge, UK, 2018
  9. Dynamics of Health Agency Response and Public Engagement during Public Health Emergency: A Case Study of CDC Tweeting Pattern during 2016 Zika Epidemic in the U.S. [PDF]
    S. Chen, Q. Xu, J. Buchenberger, A. Bagavathi, G. Fair, S. Shaikh, and S. Krishnan
    in Journal of Medical Internet Research Public Health and Surveillance, Vol4 No 4, 2018
  10. Comparative Analysis of Real vs Fake Health Information Dissemination Dynamics on Social Media [LINK]
    S. Chen, S. Krishnan, S. Shaikh, and J. Buchenberger
    in American Public Health Association Annual Meeting and Expo, San Diego, CA, 2018
  11. Deep Learning Based Urban Analytics Platform: Applications to Traffic Flow Modeling and Prediction [PDF]
    with A. Parnami, P. Bavi, D. Papanikolaou, S. Akella. M. Lee, and S. Krishnan
    in ACM SIGKDD Workshop on Mining Urban Data (MUD3), London, UK, 2018
  12. Social Sensors: Early Detection of Contagious Outbreaks in Social Media [PDF]
    A. Bagavathi and S. Krishnan
    in Springer International Conference on Applied Human Factors and Ergonomics, Orlando, USA, 2018
  13. Seeing the Forest for the Trees: New approaches to forecasting cascades [PDF]
    Siddharth Krishnan, Patrick Butler, Ravi Tandon, Jure Leskovec, and Naren Ramakrishnan
    in ACM Web Science (WebSci'16), Hannover, Germany, 2016
    Received ACM SIGWeb Travel Award
  14. A Mechanism Design Approach to Influence Maximization in Social Networks [PDF]
    Michael Levet and Siddharth Krishnan
    in EAI Intl. Conf. on Game Theory for Networks, British Columbia, Canada, 2016
    Received VT@CS Travel Scholarship
  15. Inferring Multi-dimensional Ideal Points for US Supreme Court Justices [PDF]
    M. R. Islam, K.S.M.Tozammel Hossain, Siddharth Krishnan, and Naren Ramakrishnan
    in AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016
    Featured in ACM TechNews, The Conversation, and Daily Mirror
  16. Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media [PDF]
    Siddharth Krishnan, Brian Goode, Michael Roan, and Naren Ramakrishnan
    PLoS ONE, Vol. 10, No. 10, 2015
  17. The Dynamics of Competing Cascades in Social Media: Applications to Agenda Setting [PDF]
    Siddharth Krishnan, J. Cadena, and Naren Ramakrishnan
    in WSDM 2014 Workshop on Diffusion Networks and Cascade Analytics, New York, NY, 2014
    Press: [ article]
  18. Encouraging civic participation through local news aggregation. [PDF]
    Andrea Kavanaugh, Siddharth Krishnan, Manuel Perez-Quinones, et. al.
    Information Polity 19 (1-2): 35-56, 2014.
  19. Dynamic Load Balancing for Peta-scale Quantum Monte Carlo Applications [PDF]
    C.D. Sudheer, Siddharth Krishnan, Ashok Srinivasan, and Paul Kent
    Computer Physics Communications, 184, 284-292, 2013.


  1. The daily use of Gab is climbing. Which talker might become as violent as the Pittsburgh synagogue gunman? The Monkey Cage, The Washington Post [LINK]
    with Matt Phillips, Arunkumar Bagavathi, and others
  2. Technical Report: A inventory of open and dark web marketplace for identity misrepresentation [PDF]
    with Arunkumar Bagavathi, Bojan Cukic, and others
  3. Using Cascades as Sensors: A non-network approach to forecast information contagion (manuscript)[e-mail for copy]
    Siddharth Krishnan, Arunkumar Bagavathi, and Lenwood Heath
    (under review)
  4. Analyzing and forecasting the growth of concurrent cascades: A forest of trees approach (manuscript) [e-mail for copy]
    Siddharth Krishnan and Jose Cadena
    (under preparation)


  1. Seeing the Forest for the Trees: New Approaches to Characterizing and Forecasting Cascades
    Siddharth Krishnan
    Ph.D. Thesis, Virginia Tech, 2017
  2. Dynamic Load Balancing for Peta-scale Quantum Monte Carlo Applications
    Siddharth Krishnan
    M.S. Thesis, Florida State University, 2011


At UNC Charlotte

  1. ITIS 6520: Network Science [Fall 2018]
  2. DSBA 6156: Applied Machine Learning [Fall 2017, Spring 2018]

At Virginia Tech

  1. CS4984: Capstone in Social Network Analytics [Spring 2017] (Instructor)
  2. CS5644: Machine Learning with Big Data [Fall 2016] (Teaching Assistant)
  3. CS5984: Introduction to Urban Computing [Fall 2015, Fall 2016] (Teaching Assistant)



Woodward Hall | 403B
Mailing Address:
UNC Charlotte | College of Computing and Informatics
9201 University City Blvd.
Charlotte, NC 28223

Phone & Email

invert( @ skrishnan)
Primary laout designed by Polo Chau