Shubhranshu Shekhar

Assistant Professor of Data Science at School of Business and Economics.
Email: sshekhar [at] brandeis [dot] edu

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About

I am an Assistant Professor in the School of Business and Economics at the Brandeis University.

I work, broadly, in Machine Learning. My research interests are in unsupervised and explainable methods for data-driven decision support in high-stakes domains like healthcare and finance. Nowadays, my focus is on empowering human decision-making by building intelligent systems that are unsupervised, explainable, and equitable.

Prior to Brandeis: I obatined my PhD in Machine Learning and Public Policy, and MS in Machine Learning Research from Carnegie Mellon University, where I worked with Prof. Leman Akoglu and Prof. Christos Faloutsos.

My CV is available here.

Research

    1. ArXiv
      Macroeconomic forecasting with large language models
      Andrea Carriero, Davide Pettenuzzo, and Shubhranshu Shekhar
      In arXiv preprint arXiv:2407.00890 2024
      PDF
    2. NBER
      Can Machine Learning Target Health Care Fraud? Evidence from Medicare Hospitalizations
      Shubhranshu Shekhar, Jetson Leder-Luis, and Leman Akoglu
      NBER Working Paper #30946 Feb 2024

      Accepted at Journal of Policy Analysis and Management (JPAM) 2025

      PDF
    3. PAKDD
      NETEFFECT: Discovery and Exploitation of Generalized Network Effects
      Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, and Christos Faloutsos
      In PAKDD Feb 2024
      PDF Code
    4. PAKDD
      DIFFFIND: Discovering Differential Equations from Time Series
      Lalithsai Posam, Shubhranshu Shekhar, Meng-Chieh Lee, and Christos Faloutsos
      In PAKDD Feb 2024
      PDF Code
    1. KDD
      Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
      Jaemin Yoo*, Meng-Chieh Lee*, Shubhranshu Shekhar, and Christos Faloutsos
      In ACM SIGKDD Conference on Knowledge Discovery and Data Mining Feb 2023
      PDF Code
    2. JBI
      Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
      :trophy: George Duncan Award for PhD 2nd Paper at Heinz College
      Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi, Jonathan Elmer, Christos Faloutsos, and Leman Akoglu
      Journal of Biomedical Informatics Feb 2023
      PDF Code
      1. IEEE Big Data
        GEN2OUT: Detecting and Ranking Generalized Anomalies
        Meng-Chieh Lee*, Shubhranshu Shekhar*, Christos Faloutsos, T Noah Hutson, and Leon Iasemidis
        In IEEE International Conference on Big Data (Big Data) Feb 2021
        PDF Code
      2. AAAI/ACM AIES
        FAIROD: Fairness-aware outlier detection
        Shubhranshu Shekhar, Neil Shah, and Leman Akoglu
        In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Feb 2021
        PDF Code
      1. TheWebConf
        Entity resolution in dynamic heterogeneous networks
        Shubhranshu Shekhar, Deepak Pai, and Sriram Ravindran
        In Workshop on Deep Learning for Graphs at The Web Conference (WWW) Feb 2020
        PDF
        1. ECML-PKDD
          Incorporating privileged information to unsupervised anomaly detection
          :trophy: Best Student Machine Learning Paper Runner-up Award
          Shubhranshu Shekhar, and Leman Akoglu
          In Joint European Conference on Machine Learning and Knowledge Discovery in Databases Feb 2018
          PDF Code
        1. MIKE
          Spreading Activation Way of Knowledge Integration
          Shubhranshu Shekhar, Sutanu Chakraborti, and Deepak Khemani
          In International Conference on Mining Intelligence and Knowledge Exploration Feb 2015
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        1. ICCBR
          Linking cases up: An extension to the case retrieval network
          Shubhranshu Shekhar, Sutanu Chakraborti, and Deepak Khemani
          In International Conference on Case-Based Reasoning Feb 2014
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        2. RecSys
          How popular are your tweets?
          Avijit Saha, Janarthanan Rajendran, Shubhranshu Shekhar, and Balaraman Ravindran
          In Proceedings of the 2014 Recommender Systems Challenge Feb 2014
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        3. I-CARE
          Business Networking in Social Networks
          Dhanvin Mehta, Shubhranshu Shekhar, and Balaraman Ravindran
          In 6th IBM Collaborative Academia Research Exchange (I-CARE), non-archival Feb 2014

        Patents

        1. Patent
          Utilizing a time-dependent graph convolutional neural network for fraudulent transaction identification
          Shubhranshu Shekhar, Deepak Pai, and Sriram Ravindran
          US Patent 11,403,643 2022
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        2. Patent
          Machine learning based on post-transaction data
          Moein Saleh, Xing Ji, and Shubhranshu Shekhar
          US Patent 11,321,632 2022
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