Bio


I am a Postdoctoral Research Fellow at the Department of Biomedical Data Science. My research focuses on developing machine learning and statistical models to solve problems that are inter-disciplinary in nature, including those from the biomedical, ecological, and socio-political sciences. I received my Ph.D. in Computer Science from University of Zurich, Switzerland in 2019, where I developed new algorithms to improve recommendation diversity and algorithmic fairness. I used graph theory, deep learning, and latent-factor models to build documents representations, explainable knowledge base embeddings, and personalization systems. At Stanford, I am building new machine learning models for personalized medicine by combining biological domain knowledge and large heterogeneous datasets. My research spans both ends of the biomedical data spectrum: from single-cell observations to population health data. I am particularly interested in examining the disparate health impacts of environmental factors on vulnerable and minority populations and in understanding how these findings can guide policy interventions.

Stanford Advisors


All Publications


  • Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning Zhang, W., Paudel, B., Wang, L., Chen, J., Zhu, H., Zhang, W., Bernstein, A., Chen, H., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 2366–77
  • Interaction Embeddings for Prediction and Explanation in Knowledge Graphs Zhang, W., Paudel, B., Zhang, W., Bernstein, A., Chen, H., ACM ASSOC COMPUTING MACHINERY. 2019: 96–104
  • Cross-Cutting Political Awareness through Diverse News Recommendations European Symposium Series on Societal Challenges in Computational Social Science Paudel, B., Bernstein, A. EuroCSS. 2019
  • Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality The First International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA), co-organized with the 12th ACM International Conference on Web Search and Data Mining Paudel, B., Luck, S., Bernstein, A. 2019
  • Bringing Diversity in News Recommender Algorithms ECREA 2018 - pre-conference workshop on Information, Diversity and Media Pluralism in the Age of Algorithms Paudel, B., Tolmeijer, S., Bernstein, A. 2018
  • Aligning Knowledge Base and Document Embedding Models Using Regularized Multi-Task Learning Baumgartner, M., Zhang, W., Paudel, B., Dell'Aglio, D., Chen, H., Bernstein, A., Vrandecic, D., Bontcheva, K., SuarezFigueroa, M. C., Presutti, Celino, Sabou, M., Kaffee, L. A., Simperl, E. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 21–37
  • Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS Paudel, B., Christoffel, F., Newell, C., Bernstein, A. 2017; 7 (1)

    View details for DOI 10.1145/2955101

    View details for Web of Science ID 000399087600001

  • Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering Paudel, B., Haas, T., Bernstein, A., ACM ASSOC COMPUTING MACHINERY. 2017: 215–23
  • Random Walk TripleRush: Asynchronous Graph Querying and Sampling Stutz, P., Paudel, B., Verman, M., Bernstein, A., ACM ASSOC COMPUTING MACHINERY. 2015: 1034–44
  • Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems Christoffel, F., Paudel, B., Newell, C., Bernstein, A. ASSOC COMPUTING MACHINERY.. 2015: 163–170

    View details for DOI 10.1145/2792838.2800180