| Quantum optimization with a novel Gibbs objective function and ansatz architecture search L Li, M Fan, M Coram, P Riley, S Leichenauer Physical Review Research 2 (2), 023074, 2020 | 94 | 2020 |
| Learning causal networks using inducible transcription factors and transcriptome‐wide time series SR Hackett, EA Baltz, M Coram, BJ Wranik, G Kim, A Baker, M Fan, ... Molecular systems biology 16 (3), e9174, 2020 | 46 | 2020 |
| Variants of seeded region growing M Fan, TCM Lee IET image processing 9 (6), 478-485, 2015 | 43 | 2015 |
| A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens GW Ashdown, M Dimon, M Fan, F Sánchez-Román Terán, K Witmer, ... Science advances 6 (39), eaba9338, 2020 | 39 | 2020 |
| Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts L Schiff, B Migliori, Y Chen, D Carter, C Bonilla, J Hall, M Fan, E Tam, ... Nature communications 13 (1), 1590, 2022 | 35 | 2022 |
| Modeling tangential vector fields on a sphere M Fan, D Paul, TCM Lee, T Matsuo Journal of the American Statistical Association 113 (524), 1625-1636, 2018 | 28 | 2018 |
| Portfolio management with robustness in both prediction and decision: A mixture model based learning approach S Zhu, M Fan, D Li Journal of Economic Dynamics and Control 48, 1-25, 2014 | 28 | 2014 |
| Applying deep neural network analysis to high-content image-based assays SJ Yang, SL Lipnick, NR Makhortova, S Venugopalan, M Fan, ... SLAS DISCOVERY: Advancing Life Sciences R&D 24 (8), 829-841, 2019 | 22 | 2019 |
| Neural-guided symbolic regression with asymptotic constraints L Li, M Fan, R Singh, P Riley arXiv preprint arXiv:1901.07714, 2019 | 21 | 2019 |
| Neural-guided symbolic regression with semantic prior L Li, M Fan, R Singh, P Riley arXiv preprint arXiv:1901.07714 2, 2019 | 15 | 2019 |
| Case‐control study on a decade of ground‐based magnetometers in California reveals modest signal 24‐72h prior to earthquakes WD Heavlin, K Kappler, L Yang, M Fan, J Hickey, J Lemon, L MacLean, ... Journal of Geophysical Research: Solid Earth, e2022JB024109, 2022 | 10 | 2022 |
| Correcting nuisance variation using Wasserstein distance G Tabak, M Fan, S Yang, S Hoyer, G Davis PeerJ 8, e8594, 2020 | 8 | 2020 |
| A multi-resolution model for non-Gaussian random fields on a sphere with application to ionospheric electrostatic potentials M Fan, D Paul, TCM Lee, T Matsuo The annals of applied statistics 12 (1), 459, 2018 | 8 | 2018 |
| A note on spherical needlets M Fan arXiv preprint arXiv:1508.05406, 2015 | 6 | 2015 |
| NYSCF Global Stem Cell Array® Team L Schiff, B Migliori, Y Chen, D Carter, C Bonilla, J Hall, M Fan, E Tam, ... Nelson PC, Frumkin M, Solomon SL, Bauer L, Aiyar RS, Schwarzbach E, Noggle …, 2022 | 5 | 2022 |
| Deep learning and automated Cell Painting reveal Parkinson’s disease-specific signatures in primary patient fibroblasts L Schiff, B Migliori, Y Chen, D Carter, C Bonilla, J Hall, M Fan, E Tam, ... bioRxiv, 2020, 2020 | 5 | 2020 |
| Multiresolution Modeling of High‐latitude Ionospheric Electric Field Variability and Impact on Joule Heating Using SuperDARN Data T Matsuo, M Fan, X Shi, C Miller, JM Ruohoniemi, D Paul, TCM Lee Journal of Geophysical Research: Space Physics, e2021JA029196, 2021 | 3 | 2021 |
| Time-resolved genome-scale profiling reveals a causal expression network SR Hackett, EA Baltz, M Coram, BJ Wranik, G Kim, A Baker, M Fan, ... BioRxiv, 619577, 2019 | 3 | 2019 |
| Correcting nuisance variation using wasserstein distance G Tabak, M Fan, SJ Yang, S Hoyer, G Davis arXiv preprint arXiv:1711.00882, 2017 | 3 | 2017 |
| Portfolio management with dual robustness in prediction and optimization: A mixture model based learning approach S Zhu, M Fan, D Li Available at SSRN 2320292, 2013 | 2 | 2013 |
Duan LiCity University of Hong Kong + The Chinese University of Hong Kong + University of Virginia確認したメール アドレス: cityu.edu.hk
