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. SSRN
      MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
      Andrea Carriero, Davide Pettenuzzo, and Shubhranshu Shekhar
      Working paper, Social Science Research Network Jun 2026
      PDF Code
    2. JPAM
      Can Machine Learning Target Health Care Fraud? Evidence from Medicare Hospitalizations
      Shubhranshu Shekhar, Jetson Leder-Luis, and Leman Akoglu
      Journal of Policy Analysis and Management Jan 2026

      NBER Working Paper #30946
      :chart_with_upwards_trend: Cited in Economic Report 2025

      PDF
      1. ArXiv
        Macroeconomic forecasting with large language models
        Andrea Carriero, Davide Pettenuzzo, and Shubhranshu Shekhar
        In arXiv preprint arXiv:2407.00890 2024
        PDF
      2. PAKDD
        NETEFFECT: Discovery and Exploitation of Generalized Network Effects
        Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, and Christos Faloutsos
        In PAKDD 2024
        PDF Code
      3. PAKDD
        DIFFFIND: Discovering Differential Equations from Time Series
        Lalithsai Posam, Shubhranshu Shekhar, Meng-Chieh Lee, and Christos Faloutsos
        In PAKDD 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 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

          :studio_microphone: Podcast episode

          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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