Contact

  • Academic
    degleris@stanford.edu
    University - Student Department: Electrical Engineering Position: Graduate

Additional Info

  • Mail Code: 2085

All Publications

  • Dynamic Locational Marginal Emissions via Implicit Differentiation IEEE TRANSACTIONS ON POWER SYSTEMS Valenzuela, L., Degleris, A., El Gamal, A., Pavone, M., Rajagopal, R. 2024; 39 (1): 1138-1147

    View details for DOI 10.1109/TPWRS.2023.3247345

    View details for Web of Science ID 001136086900088

  • Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Wang, Y., Degleris, A., Williams, A., Linderman, S. W. 2023

    View details for DOI 10.1080/01621459.2023.2257896

    View details for Web of Science ID 001123516000001

  • Point process models for sequence detection in high-dimensional neural spike trains. Advances in neural information processing systems Williams, A. H., Degleris, A., Wang, Y., Linderman, S. W. 2020; 33: 14350-14361

    Abstract

    Sparse sequences of neural spikes are posited to underlie aspects of working memory [1], motor production [2], and learning [3, 4]. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience [5-7]. Promising recent work [4, 8] utilized a convolutive nonnegative matrix factorization model [9] to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model predictions or estimated parameters. We address each of these shortcomings by developing a point process model that characterizes fine-scale sequences at the level of individual spikes and represents sequence occurrences as a small number of marked events in continuous time. This ultra-sparse representation of sequence events opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, which have been experimentally observed in neural circuits [10]. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.

    View details for PubMedID 35002191

    View details for PubMedCentralID PMC8734964

  • A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization IEEE TRANSACTIONS ON SIGNAL PROCESSING Degleris, A., Gillis, N. 2020; 68: 2499–2512

    View details for DOI 10.1109/TSP.2020.2984163

    View details for Web of Science ID 000538021500001