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Computer Science

D-Index
38
Citations
5145
World Ranking
10364
National Ranking
4337

Overview

B. Aditya Prakash is affiliated with the Georgia Institute of Technology in the United States. Their research primarily spans the field of Computer Science, with significant focus on Artificial Intelligence, Epidemiology, Management Science and Operations Research, Signal Processing, and Modeling and Simulation.

The scientist's work includes contributions to a variety of topics, notably:

  • COVID-19 epidemiological studies
  • Anomaly Detection Techniques and Applications
  • Time Series Analysis and Forecasting
  • Data-Driven Disease Surveillance
  • Forecasting Techniques and Applications
  • Influenza Virus Research Studies
  • Stock Market Forecasting Methods

Recent publications by B. Aditya Prakash cover a range of subjects linked to infectious disease modeling and forecasting. Notable papers include:

  • "Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S." (2020) published in bioRxiv (Cold Spring Harbor Laboratory)
  • "DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting" (2020) in bioRxiv (Cold Spring Harbor Laboratory)
  • "DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting" (2021) in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations" (2024) in Nature Communications
  • "Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19" (2021) in Proceedings of the AAAI Conference on Artificial Intelligence

B. Aditya Prakash has published extensively in venues including:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Zenodo (CERN European Organization for Nuclear Research)

The scientist frequently collaborates with other researchers, with coauthors such as:

  • Alexander Rodríguez
  • Harshavardhan Kamarthi
  • Jiaming Cui
  • Anil Vullikanti
  • Bijaya Adhikari

Best Publications

  • Rise and fall patterns of information diffusion: model and implications

    Yasuko Matsubara;Yasushi Sakurai;B. Aditya Prakash;Lei Li

  • Gelling, and melting, large graphs by edge manipulation

    Hanghang Tong;B. Aditya Prakash;Tina Eliassi-Rad;Michalis Faloutsos

  • Spotting Culprits in Epidemics: How Many and Which Ones?

    B. Aditya Prakash;Jilles Vreeken;Christos Faloutsos

  • Threshold conditions for arbitrary cascade models on arbitrary networks

    B. Aditya Prakash;Deepayan Chakrabarti;Nicholas C. Valler;Michalis Faloutsos

  • Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

    Evan L Ray;Nutcha Wattanachit;Jarad Niemi;Abdul Hannan Kanji

  • Virus propagation on time-varying networks: theory and immunization algorithms

    B. Aditya Prakash;Hanghang Tong;Nicholas Valler;Michalis Faloutsos

  • EigenSpokes: surprising patterns and scalable community chipping in large graphs

    B. Aditya Prakash;Ashwin Sridharan;Mukund Seshadri;Sridhar Machiraju

  • Winner takes all: competing viruses or ideas on fair-play networks

    B. Aditya Prakash;Alex Beutel;Roni Rosenfeld;Christos Faloutsos

  • On the Vulnerability of Large Graphs

    Hanghang Tong;B. Aditya Prakash;Charalampos Tsourakakis;Tina Eliassi-Rad

  • Sub2Vec : Feature Learning for Subgraphs

    Bijaya Adhikari;Yao Zhang;Naren Ramakrishnan;B. Aditya Prakash

  • Interacting viruses in networks: can both survive?

    Alex Beutel;B. Aditya Prakash;Roni Rosenfeld;Christos Faloutsos

  • Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks

    B. Aditya Prakash;Deepayan Chakrabarti;Michalis Faloutsos;Nicholas Valler

  • Node Immunization on Large Graphs: Theory and Algorithms

    Chen Chen;Hanghang Tong;B. Aditya Prakash;Charalampos E. Tsourakakis

  • Efficiently spotting the starting points of an epidemic in a large graph

    B. Aditya Prakash;Jilles Vreeken;Christos Faloutsos

  • Epidemic spread in mobile Ad Hoc networks: determining the tipping point

    Nicholas C. Valler;B. Aditya Prakash;Hanghang Tong;Michalis Faloutsos

  • Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread.

    Sudip Saha;Abhijin Adiga;B. Aditya Prakash;Anil Kumar S. Vullikanti

  • DeepCOVID: An Operational Deep Learning -driven Framework for Explainable Real-time COVID-19 Forecasting

    Alexander Rodríguez;Anika Tabassum;Jiaming Cui;Jiajia Xie

  • DeepDiffuse: Predicting the 'Who' and 'When' in Cascades

    Mohammad Raihanul Islam;Sathappan Muthiah;Bijaya Adhikari;B. Aditya Prakash

  • Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models

    Liangzhe Chen;K. S. Tozammel Hossain;Patrick Butler;Naren Ramakrishnan

  • Metric forensics: a multi-level approach for mining volatile graphs

    Keith Henderson;Tina Eliassi-Rad;Christos Faloutsos;Leman Akoglu

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

    Estee Y Cramer;Evan L Ray;Velma K Lopez;Johannes Bracher

Frequent Co-Authors

Christos Faloutsos
Christos Faloutsos Carnegie Mellon University
Naren Ramakrishnan
Naren Ramakrishnan Virginia Tech
Michalis Faloutsos
Michalis Faloutsos University of California, Riverside
V. S. Subrahmanian
V. S. Subrahmanian Dartmouth College
Hanghang Tong
Hanghang Tong University of Illinois at Urbana-Champaign
Tina Eliassi-Rad
Tina Eliassi-Rad Northeastern University
Jilles Vreeken
Jilles Vreeken Max Planck Society
Duen Horng Chau
Duen Horng Chau Georgia Institute of Technology
Deepayan Chakrabarti
Deepayan Chakrabarti The University of Texas at Austin
Pinar Keskinocak
Pinar Keskinocak Georgia Institute of Technology

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