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Chronological list of journal publications

  1. Y. Chen, A. Hashemi and H. Vikalo, "Accelerated distributed stochastic non-convex optimization over time-varying directed networks,” IEEE Transaction on Automatic Control, April 2025 (to appear).

  2. A. G. Aydin and H. Vikalo, "Viewport prediction via adaptive edge offloading," IEEE Networking Letters, 2024 (to appear).

  3. M. Ribero, H. Vikalo and G. de Veciana, "Federated learning at scale: Addressing client intermittency and resource constraints,” IEEE Journal of Selected Topics in Signal Processing, July 2024, pp: 1-14.

  4. M. Ribero and H. Vikalo, "Reducing communication in federated learning via efficient client sampling,” Pattern Recognition, vol. 148, 2024, pp. 110122.

  5. S. Consul, Z. Ke and H. Vikalo, "XHap: Haplotype assembly using long-distance read correlations learned by transformers," Bioinformatics Advances, vol. 3, no. 1, 2023, pp. 1-11.

  6. M. Sakthi, M. Arvinte and H. Vikalo, "Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information," IEEE Open Journal of Intelligent Transportation Systems, vol. 4, pp. 858-869, 2023, doi: 10.1109/OJITS.2023.3332043.

  7. Z. Ke and H. Vikalo, "Graph-based reconstruction and analysis of disease transmission networks using viral genomic data, Journal of Computational Biology, 30(7):796-813, Jun 2023.

  8. M. Ribeiro, H. Vikalo and G. de Veciana, "Federated learning under intermittent client availability and time-varying communication constraints," IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 1, January 2023, pp. 98-111.

  9. Y. Chen, A. Hashemi and H. Vikalo, "Communication-efficient variance-reduced decentralized stochastic optimization over time-varying directed graphs," IEEE Transactions on Automatic Control, vol. 67, no. 12, December 2022, pp. 6583-6594.

  10. A. Hashemi, A. Acharya, R. Das, H. Vikalo, S. Sanghavi, and I. Dhillon, "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, vol. 33, no. 11, November 2022, pp. 2727-2739.

  11. M. Ribero, J. Henderson, S. Williamson, and H. Vikalo, "Federated recommendations using differentially private prototypes," Pattern Recognition, volume 129, September 2022, pp. 108746.

  12. A. Hashemi, H. Vikalo, and G. de Veciana, "On the benefits of progressively increasing sampling sizes in stochastic greedy weak submodular maximization," IEEE Transactions on Signal Processing, vol. 70, no. 6, July 2022, pp. 3978-3992.

  13. A. Hashemi, R. Shafipour, H. Vikalo, and G. Mateos, "Towards accelerated greedy sampling and reconstruction of bandlimited graph signals," Signal Processing, vol. 195, June 2022.

  14. Z. Ke and H. Vikalo, "Real-time radio technology and modulation classification via an LSTM auto-encoder," IEEE Transactions on Wireless Communications, vol. 21, no. 1, January 2022, pp. 370-382.

  15. N. M. Arzeno and H. Vikalo, "Evolutionary clustering via message passing," IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 6, June 2021, pp. 2452-2466.

  16. A. Hashemi, M. Ghasemi, H. Vikalo and U. Topcu, "Randomized greedy sensor selection: Leveraging weak submodularity," IEEE Transactions on Automatic Control, vol. 66, no. 1, January 2021, pp. 199-212.

  17. A. Sankararaman, H. Vikalo, and F. Baccelli, "ComHapDet: A spatial community detection algorithm for haplotype assembly," BMC Genomics, vol. 21 (Suppl 9), September 2020, pp. 586:1-14.

  18. S. Barik and H. Vikalo, "Matrix completion and performance guarantees for single individual haplotyping," IEEE Transactions on Signal Processing, vol. 67, no. 18, September 2019, pp: 4782-4794.

  19. A. Hashemi and H. Vikalo, "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, December 2018, pp. 1534-1546.

  20. S. Barik, S. Das, and H. Vikalo, "Viral quasispecies reconstruction via correlation clustering," Genomics, vol. 110, no. 6, November 2018, pp. 375-381.

  21. A. Hashemi and H. Vikalo, "Accelerated orthogonal least-squares for large-scale sparse reconstruction,” Digital Signal Processing, vol. 82, no. 11, November 2018, pp. 91-105.

  22. S. Ahn, Z. Ke and H. Vikalo, "Viral quasispecies reconstruction via tensor factorization with successive read removal," Bioinformatics, vol. 34, no. 13, July 2018, pp. i23–i31.

  23. A. Hassibi, A. Manickam, R. Singh, S. Bolouki, R. Sinha, K. Jirage, M. McDermott, B. Hassibi, H. Vikalo, G. Mazarei, L. Pei, L. Bousse, M. Miller, M. Heshami, M. Savage, M. Taylor, N. Gamini, N. Wood, P. Mantina, P. Grogan, P. Kuimelis, P. Savalia, S. Conradson, Y. Li, R. Meyer, E. Ku, J. Ebert, B. Pinsky, G. Dolganov, T. Van, K. Johnson, P. Naraghi-Arani, R. Kuimelis, G. Schoolnik, "Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip," Nature Biotechnology, 36, July 2018, pp. 738–745.

  24. S. Ahn and H. Vikalo, "aBayesQR: A Bayesian method for reconstruction of viral populations characterized by low diversity," Journal of Computational Biology, vol. 25, no. 7, July 2018, pp: 637-648.

  25. H. Yang, J. Chun, and H. Vikalo, "Cyclic block coordinate minimization algorithms for DOA estimation in co-prime arrays," Signal Processing, vol. 145, no. 4, April 2018, pp. 272-284.

  26. A. Hashemi, B. Zhu and H. Vikalo, "Sparse tensor decomposition for haplotype assembly of diploids and polyploids," BMC Genomics, 19(Suppl 4):191, March 2018.

  27. H. Si, H. Vikalo, and S. Vishwanath, "Information-theoretic analysis of haplotype assembly," IEEE Transactions on Information Theory, vol. 63, no. 7, July 2017, pp. 3468-3479.

  28. S. Das and H. Vikalo, "Optimal haplotype assembly via a branch-and-bound algorithm," IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 3, no. 1, March 2017, pp. 1-12.

  29. E. O'Reilly, F. Baccelli, G. de Veciana, and H. Vikalo, "End-to-end optimization of high-throughput DNA sequencing" Journal of Computational Biology, 23(10): 789-800, October 2016.

  30. C. Cai, S. Sanghavi, and H. Vikalo, "Structured low-rank matrix factorization for haplotype assembly," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Structured Matrices in Signal and Data Processing, vol. 10, no. 4, August 2016, pp. 647-657.

  31. Z. Puljiz and H. Vikalo, "Decoding genetic variations: Communications-inspired haplotype assembly," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 3, June 2016, pp. 518-530.

  32. N. M. Arzeno, K. A. Lawson, S. V. Duzinski, and H. Vikalo, "Designing optimal mortality risk prediction scores that preserve clinical knowledge," Journal of Biomedical Informatics, vol. 56, August 2015, pp. 145-156.

  33. S. Ahn and H. Vikalo, "Joint haplotype assembly and genotype calling via sequential Monte Carlo algorithm," BMC Bioinformatics, 16:223, July 2015, doi:10.1186/s12859-015-0651-8.

  34. N. M. Arzeno and H. Vikalo, "Semi-supervised affinity propagation with soft instance-level constraints," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, May 2015, pp. 1041-1052.

  35. S. Das and H. Vikalo, "SDhaP: haplotype assembly for diploids and polyploids via semi-definite programming," BMC Genomics, 16:260, April 2015, doi:10.1186/s12864-015-1408-5.

  36. X. Shen, M. Shamaiah, and H. Vikalo, "Iterative learning of a DNA consensus sequence from high-throughput short reads: Algorithms and limits of performance," IEEE Transactions on Signal Processing, vol. 62, no. 17, September 2014, pp. 4425-4435.

  37. S. Barik and H. Vikalo, "Sparsity-aware sphere decoding: Algorithms and complexity analysis," IEEE Transactions on Signal Processing, vol. 62, no. 9, May 2014, pp. 2212-2225.

  38. M. Park, M. Nassar, and H. Vikalo, "Bayesian active learning for drug combinations," IEEE Transactions on Biomedical Engineering, 60(11):3248-55, November 2013 (doi: 10.1109/TBME.2013.2272322).

  39. S. Das and H. Vikalo, "Base calling for high-throughput short-read sequencing: Dynamic programming solutions," BMC Bioinformatics, 14:129, April 2013 (doi:10.1186/1471-2105-14-129).

  40. S.-H. Lee, M. Shamaiah, H. Vikalo, and S. Vishwanath, "Message-passing algorithms for coordinated spectrum sensing in cognitive radio networks," IEEE Communication Letters, vol. 17, no. 4, April 2013, pp. 812-815.

  41. M. Shamaiah, S.-H. Lee, S. Vishwanath, and H. Vikalo, "Distributed algorithms for spectrum access in cognitive radio relay networks," IEEE Journal on Sel. Areas in Communications - Cognitive Radio Series, vol. 30, no. 10, November 2012, pp. 1947-1957.

  42. T. Wu and H. Vikalo, "Joint parameter estimation and base-calling for pyrosequencing systems," IEEE Transactions on Signal Processing, vol. 60, no. 8, August 2012, pp. 4376-4386.

  43. M. Shamaiah, S. Banerjee, and H. Vikalo, "Greedy sensor selection under channel uncertainty," IEEE Wireless Communications Letters, vol. 1, no. 4, August 2012, pp. 376-379.

  44. S. Das and H. Vikalo, "OnlineCall: Fast online parameter estimation and base calling for Illumina's next generation sequencing," Bioinformatics, vol. 28, no. 13, July 2012, pp. 1677-1683.

  45. X. Shen and H. Vikalo, "ParticleCall: A particle filter for base calling in next-generation sequencing systems," BMC Bioinformatics, vol. 13, no. 160, July 2012.

  46. M. Shamaiah, X. Shen, and H. Vikalo, "Estimating parameters of sampled diffusion processes in affinity biosensors," IEEE Transactions on Signal Processing, vol. 60, no. 6, June 2012, pp. 3228-3239.

  47. M. Shamaiah, S.-H. Lee, and H. Vikalo, "Graphical models and inference on graphs in genomics," IEEE Signal Processing Magazine, Special Issue on Genomic and Proteomic Signal Processing in Biomolecular Pathways, vol. 29, no. 1, January 2012, pp. 51-65.

  48. M. Shamaiah and H. Vikalo, "Estimating time-varying sparse signals under communication constraints," IEEE Transactions on Signal Processing, vol. 59, no. 6, June 2011, pp. 2961 - 2964.

  49. X. Shen and H. Vikalo, "Inferring parameters of gene regulatory networks via particle filtering," EURASIP Journal on Advances in Signal Processing, Special Issue on Genomic Signal Processing, 2010, doi:10.1155/2010/204612.

  50. H. Vikalo and M. Gokdemir, "An MCMC algorithm for estimation in real-time biosensor arrays," EURASIP Journal on Advances in Signal Processing, Special Issue on Genomic Signal Processing, 2010, doi:10.1155/2010/736301.

  51. H. Vikalo, B. Hassibi, and A. Hassibi, "Limits of performance of quantitative polymerase chain reaction systems," IEEE Transactions on Information Theory, Special Issue on Molecular Biology and Neuroscience, vol. 56, no. 2, February 2010, pp. 1-8.

  52. A. Hassibi, H. Vikalo, J. L. Riechmann, and B. Hassibi, "Real-time DNA microarray analysis," Nucleic Acids Research, vol. 37, no. 20, 2009, e132:1-12.

  53. M. El-Khamy, H. Vikalo, B. Hassibi, and R. J. McEliece, "Performance of sphere decoding of block codes," IEEE Transactions on Communications, vol. 57, no. 10, October 2009, pp. 2940-2950.

  54. S. Das, H. Vikalo, and A. Hassibi, "On scaling laws of biosensors: a stochastic approach," Journal of Applied Physics, vol. 105, no. 10, May 2009, pp. 102021-102021-7.

  55. H. Vikalo, B. Hassibi, and A. Hassibi, "Modeling and estimation for real-time microarrays," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Genomic and Proteomic Signal Processing, vol. 2, no. 3, June 2008, pp. 286-296.

  56. F. Parvaresh, H. Vikalo, S. Misra, and B. Hassibi, "Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Genomic and Proteomic Signal Processing, vol. 2, no. 3, June 2008, pp. 275-285.

  57. M. Stojnic, H. Vikalo, and B. Hassibi, "Speeding up the sphere decoder with H-infinity and SDP inspired lower bounds," IEEE Transactions on Signal Processing, vol. 56, no. 2, February 2008, pp. 712-726.

  58. A. Hassibi, H. Vikalo, and A. Hajimiri, "On noise processes and limits of performance in biosensors,"Journal of Applied Physics, vol. 102, no. 1, July 2007, pp. 014909-014909-12.

  59. H. Vikalo, B. Hassibi, and A. Hassibi, "A statistical model for microarrays, optimal estimation algorithms, and limits of performance," IEEE Transactions on Signal Processing, Special Issue on Genomics Signal Processing, vol. 54, no. 6, June 2006, pp. 2444-2455.

  60. H. Vikalo and B. Hassibi, "On joint detection and decoding of linear block codes on Gaussian vector channels," IEEE Transactions on Signal Processing, vol. 54, no. 9, September 2006, pp. 3330-3342.

  61. M. Stojnic, H. Vikalo, and B. Hassibi, "Maximizing the sum-rate of multi-antenna broadcast channels using linear preprocessing," IEEE Transactions on Wireless Communications, vol. 5, no. 9, September 2006, pp. 2338-2342.

  62. H. Vikalo, B. Hassibi, and P. Stoica, "Joint ML channel estimation and signal detection," IEEE Transactions on Wireless Communications, vol. 5, no. 7, July 2006, pp. 1838-1845.

  63. H. Vikalo, B. Hassibi, and U. Mitra, "Sphere-constrained ML detection for frequency-selective channels," IEEE Transactions on Communications, vol. 54, no. 7, July 2006, pp. 1179 - 1183.

  64. H. Vikalo and B. Hassibi, "On sphere decoding algorithm. II. Generalizations, second-order statistics, and applications to communications," IEEE Transactions on Signal Processing, vol. 53, no. 8, August 2005, pp. 2819 - 2834.

  65. B. Hassibi and H. Vikalo, "On sphere decoding algorithm. I. Expected complexity," IEEE Transactions on Signal Processing, vol. 53, no. 8, August 2005, pp. 2806 - 2818.

  66. H. Vikalo, B. Hassibi, A. Erdogan, and T. Kailath, "On H-infinity design techniques for robust signal reconstruction in noisy filter banks," EURASIP Signal Processing, vol. 85, no. 1, January 2005, pp. 1-14.

  67. H. Vikalo, B. Hassibi, and T. Kailath, "Iterative decoding for MIMO channels via modified sphere decoder," IEEE Transactions on Wireless Communications, vol.3, no. 6, November 2004, pp. 2299-2311.

  68. H. Vikalo, B. Hassibi, B. Hochwald, and T. Kailath, "On the capacity of frequency-selective channels in training-based transmission schemes," IEEE Transactions on Signal Processing, vol. 52, no. 9, September 2004, pp. 2572-2583.

  69. H. Vikalo and B. Hassibi, "On maximum-likelihood sequence detection for multiple antenna systems over dispersive channels," EURASIP Journal on Applied Signal Processing, Special Issue on Space-Time Coding, May 2002, pp. 525-531.