SZ

Preprints under review

  1. Marshall Mueller, Murphy James M, and Abiy Tasissa. “Locality Regularized Reconstruction: Structured Sparsity and Delaunay Triangulations”. In: Sampling Theory, Signal Processing, and Data Analysis (2024).

Journal articles

  1. Samuel Lichtenberg and Abiy Tasissa. “Localization from structured distance matrices via low-rank matrix recovery ”. In: IEEE Transactions of Information Theory (2024).
  2. Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams, and Demba Ba. “Discriminative reconstruction via simultaneous dense and sparse coding”. In: Transactions on Machine Learning Research (2024).
  3. Samuel Lichtenberg and Abiy Tasissa. “A dual basis approach to multidimensional scaling”. In: Linear Algebra and its Applications 682 (2024), pp. 86–95.
  4. Abiy Tasissa, Pranay Tankala, James M Murphy, and Demba Ba. “K-Deep Simplex: Manifold Learning via Local Dictionaries”. In: IEEE Transactions on Signal Processing (2023).
  5. Demba Ba, Akshunna S Dogra, Rikab Gambhir, Abiy Tasissa, and Jesse Thaler. “SHAPER: Can You Hear the Shape of a Jet?” In: Journal of High Energy Physics (2023) [PDF]
  6. Ahmed Ali Abbasi, Abiy Tasissa, and Shuchin Aeron. “R-local unlabeled sensing: A novel graph matching approach for multiview unlabeled sensing under local permutations”. In: IEEE Open Journal of Signal Processing 2 (2021), pp. 309–317 [PDF]
  7. Abiy Tasissa and Rongjie Lai. “Low-rank matrix completion in a general non-orthogonal basis”. In: Linear Algebra and its Applications 625 (2021), pp. 81–112 [PDF]
  8. Abiy Tasissa and Rongjie Lai. "Exact reconstruction of euclidean distance geometry problem using low-rank matrix completion”. In: IEEE Transactions on Information Theory 65.5 (2018), pp. 3124–3144 [PDF]
  9. Abiy F Tasissa, Martin Hautefeuille, John H Fitek, and Ra´ul A Radovitzky. “On the formation of Friedlander waves in a compressed-gas-driven shock tube”. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472.2186 (2016), p. 20150611 [PDF]

Peer-reviewed conference proceedings

  1. Scott Fullenbaum, Marshall Mueller, Abiy Tasissa, and James M Murphy. “Nonlinear unmixing of hyperspectral images via regularized Wasserstein dictionary learning.” In: 2024 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE. 2024. To appear.
  2. Scott Fullenbaum, Marshall Mueller, Abiy Tasissa, and James M Murphy. “Hyperspectral image clustering via learned representation in Wasserstein space”. In: 2024 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE. 2024. To appear.
  3. Marshall Mueller, Shuchin Aeron, James M Murphy, and Abiy Tasissa. “Geometrically Regularized Wasserstein Dictionary Learning”. In: Topological, Algebraic and Geometric Learning Workshops 2023. PMLR. 2023, pp. 384–403
  4. Mattthew E Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, and James M Murphy. “Measure Estimation in the Barycentric Coding Model”. In: International Conference on Machine Learning. PMLR. 2022, pp. 23781–23803 [PDF]
  5. Ahmed Ali Abbasi, Abiy Tasissa, and Shuchin Aeron. “r-Local Unlabeled Sensing: Improved Algorithm and Applications”. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2022, pp. 5593–5597 [PDF]
  6. Abiy Tasissa, Pranay Tankala, and Demba Ba. “Weighed l1 on the Simplex: Compressive Sensing Meets Locality”. In: 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE. 2021, pp. 476–480 [PDF]
  7. Abiy Tasissa, Duc Nguyen, and James M Murphy. “Deep diffusion processes for active learning of hyperspectral images”. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE. 2021, pp. 3665–3668 [PDF]

Peer-Reviewed workshop papers

  1. Chandler Mack Smith, Samuel P Lichtenberg, HanQin Cai, and Abiy Tasissa. “Riemannian Optimization for Euclidean Distance Geometry”. In: OPT 2023: Optimization for Machine Learning. 2023
  2. Jonathan Huml, Abiy Tasissa, and Demba Ba. “Sparse, Geometric Autoencoder Models of V1”. In: NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations. 2022 [PDF]