| Current progress and open challenges for applying deep learning across the biosciences N Sapoval*, A Aghazadeh*, MG Nute, DA Antunes, A Balaji, R Baraniuk, ... Nature Communications 13 (1), 1728, 2022 | 170 | 2022 |
| Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells RT Leenay*, A Aghazadeh*, J Hiatt*, D Tse, TL Roth, R Apathy, E Shifrut, ... Nature Biotechnology 37 (9), 1034-1037, 2019 | 116 | 2019 |
| Data-driven semi-supervised and supervised learning algorithms for health monitoring D Sen, A Aghazadeh, A Mousavi, S Nagarajaiah, R Baraniuk, A Dabak Mechanical Systems and Signal Processing 131, 524-537, 2019 | 77 | 2019 |
| Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis V Jamali, C Hargus, A Ben-Moshe, A Aghazadeh, HD Ha, KK Mandadapu, ... Proceedings of the National Academy of Sciences 118 (10), e2017616118, 2021 | 43 | 2021 |
| MISSION: Ultra Large-scale Feature Selection using Count-Sketches A Aghazadeh, R Spring, D LeJeune, G Dasarathy, A Shrivastava, ... International Conference on Machine Learning Research 80, 80-88, 2018 | 42 | 2018 |
| On the sparsity of fitness functions and implications for learning DH Brookes, A Aghazadeh, J Listgarten Proceedings of the National Academy of Sciences 119 (1), e2109649118, 2022 | 38 | 2022 |
| Sparsity-based approaches for damage detection in plates D Sen, A Aghazadeh, A Mousavi, S Nagarajaiah, R Baraniuk Mechanical Systems and Signal Processing 117, 333-346, 2019 | 37 | 2019 |
| Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions A Aghazadeh, H Nisonoff, O Ocal, DH Brookes, Y Huang, OO Koyluoglu, ... Nature communications 12 (1), 5225, 2021 | 34* | 2021 |
| Universal microbial diagnostics using random DNA probes A Aghazadeh, AY Lin, MA Sheikh, AL Chen, LM Atkins, CL Johnson, ... Science advances 2 (9), e1600025, 2016 | 20 | 2016 |
| CRISPRL and: Interpretable large-scale inference of DNA repair landscape based on a spectral approach A Aghazadeh, O Ocal, K Ramchandran Bioinformatics 36 (Supplement_1), i560-i568, 2020 | 9 | 2020 |
| Insense: Incoherent sensor selection for sparse signals A Aghazadeh, M Golbabaee, A Lan, R Baraniuk Signal Processing 150, 57-65, 2018 | 9 | 2018 |
| RHash: Robust hashing via l∞-norm distortion A Aghazadeh, A Lan, A Shrivastava, R Baraniuk Proceedings of the 26th International Joint Conference on Artificial …, 2017 | 5 | 2017 |
| Efficiently Computing Sparse Fourier Transforms of -ary Functions YE Erginbas, JS Kang, A Aghazadeh, K Ramchandran IEEE International Symposium on Information Theory (ISIT), 2023 | 4 | 2023 |
| Universal microbial diagnostics using random DNA probes RA Drezek, RG Baraniuk, A Aghazadeh, M Sheikh, P BUGGA US Patent 11,104,964, 2021 | 3 | 2021 |
| BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory A Amirali, V Gupta, A DeWeese, OO Koyluoglu, K Ramchandran Proceedings of Machine Learning Research vol 107: 1–18, 2021 2nd Annual …, 2021 | 2* | 2021 |
| ProtiGeno: a prokaryotic short gene finder using protein language models T Tu, G Krishna, A Aghazadeh International Conference on Machine Learning Research (ICML) Workshop on …, 2023 | 1 | 2023 |
| Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces A Aghazadeh, N Rajaraman, T Tu, K Ramchandran Transactions on Machine Learning Research (TMLR), 2022 | 1 | 2022 |
| On Recovering Higher-order Interactions from Protein Language Models D Tsui, A Aghazadeh ICLR 2024 Workshop on Generative and Experimental Perspectives for …, 2024 | | 2024 |
| A Fourier Tour of Protein Function Prediction A Aghazadeh Bulletin of the American Physical Society, 2024 | | 2024 |
| Group-Structured Adversarial Training F Farnia, A Aghazadeh, J Zou, D Tse arXiv preprint arXiv:2106.10324, 2021 | | 2021 |
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice University在 rice.edu 的电子邮件经过验证
