Preprints

  1. Castillo, A., Kaya, E., Ye, L., Hashemi, A., “Submodular Maximization Approaches for Equitable Client Selection in Federated Learning.” (Link)

  2. Kim, J., Chandra, A., Hashemi, A., Brinton, C. “A Fast Single-Loop Primal-Dual Algorithm for Non-convex Functional Constrained Optimization.” (Link)

  3. Aketi, A., Hashemi, A., Roy, K, “AdaGossip: Adaptive Consensus Step-size for Decentralized Deep Learning with Communication Compression.” (Link)

  4. Kaya, E., Hashemi, A., “Localized Distributional Robustness in Submodular Multi-Task Subset Selection.” (Link)

  5. Chellapandi, V., Upadhyay, A., Hashemi, A., Zak, S., “Decentralized Federated Learning: Model Update Tracking Under Imperfect Information Sharing.” (Link)

  6. Lan, G., Han, D., Hashemi, A., Aggarwal, V., Brinton, G., “Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis.” (Link)

  7. Memarian, F., Hashemi, A., Niekum, S., Topcu, U., “On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning.” (Link)

Journals

  1. Kaya, E., Hibbard, M., Tanaka, T., Topcu, U., Hashemi, A., “Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations,” Automatica, 2024. (Link)

  2. Chen, Y., Hashemi, A., Vikalo, H., “Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs,” IEEE Transactions on Automatic Control, 2022. (Link)

  3. Lauffer, N., Ghasemi, M., Hashemi, A., Savas, Y., Topcu, U., “No-Regret Learning in Dynamic Stackelberg Games,” IEEE Transactions on Automatic Control, 2023. (Link)

  4. Chellapandi, V., Upadhyay, A., Hashemi, A., Zak, S., “On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing,” IEEE Control Systems Letters, 2023. (Link)

  5. Upadhyay, A., Hashemi, A., “Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise,” IEEE ACCESS, 2023. (Link)

  6. Kaya, E., Sahin, M., Hashemi, A., “Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications,” IEEE ACCESS, 2023. (Link)

  7. Hashemi, A., Schaeffer, H., Shi, B., Tran, G., Ward, R., “Generalization Bounds for Sparse Random Feature Expansions,” Applied and Computational Harmonic Analysis, 2023. (Link)

  8. Hashemi, A., Vikalo, H., de Veciana, G., “On the Benefits of Progressively Increasing Sampling Sizes in Stochastic Greedy Weak Submodular Maximization,” IEEE Transactions on Signal Processing, 2022. (Link)

  9. Hashemi, A., Shafipour, R., Vikalo, H., Mateos, G., “Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals,” Elsevier Signal Processing, 2022. (Link)

  10. Hashemi, A., Acharya, A., Das, R., Vikalo, H., Sanghavi, S., Dhillon, I., “On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning,” IEEE Transactions on Parallel and Distributed Systems, Special Section on Parallel and Distributed Computing Techniques for AI, ML, and DL, 2022. (Link)

  11. Chen, Y., Hashemi, A., Vikalo, H., “Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs,” IEEE Transactions on Automatic Control, 2022. (Link)

  12. Hashemi, A., Ghasemi, M., Vikalo, H., Topcu, U., “Randomized greedy sensor selection: Leveraging weak submodularity,” IEEE Transactions on Automatic Control, Jan. 2021. (Link)

  13. Hashemi, A. and Vikalo, H., “Evolutionary Self-Expressive Models for Subspace Clustering,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Data Science: Robust Subspace Learning and Tracking, vol. 12, no. 6, pp. 1534–1546, Dec. 2018. (Link)

  14. Hashemi, A., Zhu, B., Vikalo, H., “Sparse Tensor Decomposition for Haplotype Assembly of Diploids and Polyploids,” BMC Genomics, vol. 19, no. 4, pp. 1–15, Mar. 2018. (Link)

  15. Hashemi, A. and Vikalo, H., “Accelerated Orthogonal Least-Squares for Large-Scale Sparse Reconstruction,” Digital Signal Processing, vol. 82, pp. 91–105, Nov. 2018. (Link)

Conference Papers

  1. Castillo, A., Kaya, E., Ye, L., Hashemi, A., “Equitable Client Selection in Federated Learning via Truncated Submodular Maximization,” IEEE Conference on Decision and Control (CDC), 2024. (Link)

  2. Luo, Z., Hashemi, A., “Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression,” International Conference on Machine Learning (ICML), 2024. (Link)

  3. Ravikumar, D., Soufleri, E., Hashemi, A., Roy, K. “Unveiling Privacy, Memorization, and Input Curvature Links,” International Conference on Machine Learning (ICML), 2024. (Link)

  4. Moon, S., Hashemi, A., “Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes,” Conference on Uncertainty in Artificial Intelligence (UAI), 2024. (Link)

  5. Sahin, M., Yalcinkaya, D., Hashemi, A., Dharmakumar, R., Sharif, B., “Retrospective k-Space Synthesis for Cardiac MRI Deep-learning Applications from Magnitude-only Images Using Score based Diffusion Models,” 32nd Annual Meeting of ISMRM, 2024. (Link)

  6. Sahin, M., Yalcinkaya, D., Hashemi, A., Dharmakumar, R., Sharif, B., “Retrospective Phase-map Synthesis for CMR datasets from Magnitude-only DICOM Images Enabled by AI Generative Models to Create Large Training Datasets for Deep Learning-based Image Reconstruction,” 27th Annual Scientific Sessions of SCMR, 2024. (Link)

  7. Aketi, A., Hashemi, A., Roy, K, “Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data,” Conference on Neural Information Processing Systems (NeurIPS), 2023. (Link)

  8. Kaya, E., Hashemi, A., “Relative Entropy Regularization for Robust Submodular Multi-Task Subset Selection,” Allerton Conference on Communication, Control, and Computing, 2023. (Link)

  9. Ramishetty, S., Hashemi, A., “High Probability Guarantees for Federated Learning,” Allerton Conference on Communication, Control, and Computing, 2023. (Link)

  10. Castillo, A., Kaya, E., Hashemi, A., “High Probability Guarantees for Submodular Maximization via Boosted Stochastic Greedy,” Asilomar Conference on Signals, Systems, and Computers, 2023. (Link)

  11. Upadhyay, A., Hashemi, A., “Noisy Communication of Information in Federated Learning: An Improved Convergence Analysis,” Asilomar Conference on Signals, Systems, and Computers, 2023. (Link)

  12. Hashemi, A. and Upadhyay, A.,“Predictive Estimation for Reinforcement Learning with Time-Varying Reward Functions,” Asilomar Conference on Signals, Systems, and Computers, 2023. (Link)

  13. Kaya, E., Sahin, M., Hashemi, A., “Communication-Constrained Exchange of Zeroth-Order Information with Application to Collaborative Target Tracking,” International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. (Link)

  14. Chen, Y., Hashemi, A., Vikalo, H., “Accelerated Decentralized Stochastic Non-Convex Optimization over Directed Networks,” International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. (Link)

  15. Yalcinkaya, D., Benan Unal, H., Raman, S., Hashemi, A., Dharmakumar, R., Sharif, B., “Data-adapted Neural Network Denoisers as a Regularization Engine for Low-latency Image Reconstruction in Accelerated Cardiac Perfusion MRI,” 31st Annual Meeting of ISMRM, 2023. (Link)

  16. Hibbard, M., Hashemi, A., Tanaka, T., Topcu, U., “Randomized Greedy Algorithms for Sensor Selection in Large-Scale Satellite Constellations,” American Control Conference, 2023. (Link)

  17. Das, R., Hashemi, A., Sanghavi, S., Dhillon, I., “DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning,” 14th International Workshop on Optimization for Machine Learning at NeurIPS, 2022. (Link)

  18. Das, R., Acharya, A., Hashemi, A., Sanghavi, S., Dhillon, I., Topcu, U., “Faster Non-Convex Federated Learning via Global and Local Momentum,” Conference on Uncertainty in Artificial Intelligence (UAI), 2022. (Link)

  19. Acharya, A., Hashemi, A., Jain, P., Sanghavi, S., Dhillon, I., Topcu, U., “Robust Training in High Dimensions via Block Coordinate Geometric Median Descent,” The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. (Link) (Slides) (Poster)

  20. Ghasemi, M., Hashemi, A., Vikalo, H., Topcu, U., “No-Regret Learning with High-Probability in Adversarial Markov Decision Processes,” Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (Link) (Slides) (Poster)

  21. Ghasemi, M., Hashemi, A., Topcu, U., Vikalo, H., “Online Learning with Implicit Exploration in Episodic Markov Decision Processes,” American Control Conference (ACC), 2021. (Link) (Slides)

  22. Savas, Y., Hashemi, A., Vinod, AP., Sadler, BM., Topcu, U., “Physical-Layer Security via Distributed Beam-forming in the Presence of Adversaries with Unknown Locations,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021. (Link) (arXiv) (Slides) (Poster)

  23. Chen, Y., Hashemi, A., Vikalo, H., “Decentralized Optimization on Time-Varying Directed Graphs under Communication Constraints,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021. (Link) (Slides) (Poster)

  24. Hashemi, A., Vikalo, H., de Veciana, G., “On the Performance-Complexity Tradeoff in Stochastic Greedy Weak Submodular Optimization,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021. (Link) (Slides) (Poster)

  25. Ghasemi, M., Hashemi, A., Vikalo, H., Topcu, U., “Identifying Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach,” American Control Conference (ACC), Denver, CO, July 2020. (Link) (Slides)

  26. Hashemi, A., Ghasemi, M., Vikalo, H., Topcu, U., “Submodular Observation Selection and Information Gathering for Quadratic Models,” International Conference on Machine Learning (ICML), Long Beach, CA, June 2019. (Link)

  27. Ghasemi, M., Hashemi, A., Vikalo, H., Topcu, U., “On Submodularity of Quadratic Observation Selection in Constrained Networked Sensing Systems,” American Control Conference (ACC), Philadelphia, PA, July 2019. (Link) (Slides)

  28. Shafipour, R., Hashemi, A., Mateos, G., Vikalo, H., “Online topology inference from streaming stationary graph signals,” IEEE Data Science Workshop (DSW), Minneapolis, MN, June 2019. (Link) (Slides)

  29. Hashemi, A. and Vikalo, H., “Evolutionary Subspace Clustering: Discovering Structure In Self-expressive Time-series Data,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019. (Link)

  30. Consul, S., Hashemi, A., Vikalo, H., “A MAP Framework for Support Recovery of Sparse Signals Using Orthogonal Least Squares,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019. (Link) (Poster)

  31. Hashemi, A., Kilic, O.F., Vikalo, H., “Near-Optimal Distributed Estimation for a Network of Sensing Units Operating Under Communication Constraints,” Conference on Decision and Control (CDC), Miami, FL, Dec. 2018. (Link)

  32. Hashemi, A., Shafipour, R., Vikalo, H., Mateos, G., “A Novel Scheme for Support Identification and Iterative Sampling of Bandlimited Graph Signals,” Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, Nov. 2018. (Link) (Poster)

  33. Hashemi, A., Ghasemi, M., Vikalo, H., Topcu, U., “A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks,” American Control Conference (ACC), Milwaukee, WI, Jun. 2018. (Link)

  34. Hashemi, A., Shafipour, R., Vikalo, H., Mateos, G., “Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), Calgary, Alberta, Canada, Apr. 2018. (Link) (Poster)

  35. Hashemi, A. and Vikalo, H., “Sparse Recovery via Branch and Bound Least-Squares,” International Conference on Acoustic, Speech and Signal Processing (ICASSP), New Orleans, LA, Mar. 2017. (Link) (Poster)

  36. Hashemi, A., Zhu, B., Vikalo, H., “A Tensor Factorization Framework for Haplotype Assembly of Diploids and Polyploids,” RECOMB Satellite Workshop on Massively Parallel Sequencing, Hong Kong, May 2017.

  37. Hashemi, A. and Vikalo, H., “Sparse Linear Regression via Generalized Orthogonal Least-Squares,” Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, Dec. 2016. (Link) (Slides)

Technical Reports

  1. Chen, Y., Hashemi, A., Vikalo, H., “Communication-Efficient Algorithms for Distributed Optimization Over Directed Graphs,” Technical Report, 2020. (Link)

  2. Hashemi, A. and Vikalo, H., “Sparse Recovery via Orthogonal Least-Squares under Presence of Noise,” Technical Report, 2016. (Link)

Theses

  1. Hashemi, A., “Efficient Algorithms for Structured Inference and Collaborative Learning,” Dissertation, University of Texas at Austin, Aug. 2020. (Link)

  2. Hashemi, A., “Vision-Based Gait Analysis via Exploiting Human Body-Parts Proportion,” Bachelor Thesis, Sharif University of Technology, June 2014.