| A probabilistic passenger-to-train assignment model based on automated data Y Zhu, HN Koutsopoulos, NHM Wilson Transportation Research Part B: Methodological 104, 522-542, 2017 | 123 | 2017 |
| Machine learning at Microsoft with ML. NET Z Ahmed, S Amizadeh, M Bilenko, R Carr, WS Chin, Y Dekel, X Dupre, ... Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 85 | 2019 |
| Data science through the looking glass: Analysis of millions of github notebooks and ml. net pipelines F Psallidas, Y Zhu, B Karlas, J Henkel, M Interlandi, S Krishnan, B Kroth, ... ACM SIGMOD Record 51 (2), 30-37, 2022 | 65 | 2022 |
| Vamsa: Automated provenance tracking in data science scripts MH Namaki, A Floratou, F Psallidas, S Krishnan, A Agrawal, Y Wu, Y Zhu, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 62 | 2020 |
| Transit data analytics for planning, monitoring, control, and information HN Koutsopoulos, Z Ma, P Noursalehi, Y Zhu Mobility patterns, big data and transport analytics, 229-261, 2019 | 55 | 2019 |
| Inferring left behind passengers in congested metro systems from automated data Y Zhu, HN Koutsopoulos, NHM Wilson Transportation research procedia 23, 362-379, 2017 | 53 | 2017 |
| Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML A Agrawal, R Chatterjee, C Curino, A Floratou, N Gowdal, M Interlandi, ... arXiv preprint arXiv:1909.00084, 2019 | 39 | 2019 |
| Passenger itinerary inference model for congested urban rail networks Y Zhu, HN Koutsopoulos, NHM Wilson Transportation Research Part C: Emerging Technologies 123, 102896, 2021 | 32 | 2021 |
| Inferring left behind passengers in congested metro systems from automated data Y Zhu, HN Koutsopoulos, NHM Wilson Transportation Research Part C: Emerging Technologies 94, 323-337, 2018 | 28 | 2018 |
| Automated data in transit: Recent developments and applications HN Koutsopoulos, P Noursalehi, Y Zhu, NHM Wilson 2017 5th IEEE international conference on models and technologies for …, 2017 | 27 | 2017 |
| Passenger-to-Train Assignment Model Based on Automated Data Y Zhu Massachusetts Institute of Technology, 2014 | 24 | 2014 |
| Kea: Tuning an exabyte-scale data infrastructure Y Zhu, S Krishnan, K Karanasos, I Tarte, C Power, A Modi, M Kumar, ... Proceedings of the 2021 International Conference on Management of Data, 2667 …, 2021 | 21 | 2021 |
| Seagull: An infrastructure for load prediction and optimized resource allocation O Poppe, T Amuneke, D Banda, A De, A Green, M Knoertzer, ... arXiv preprint arXiv:2009.12922, 2020 | 16 | 2020 |
| MLOS: An infrastructure for automated software performance engineering C Curino, N Godwal, B Kroth, S Kuryata, G Lapinski, S Liu, S Oks, ... Proceedings of the Fourth International Workshop on Data Management for End …, 2020 | 14 | 2020 |
| Griffon: Reasoning about job anomalies with unlabeled data in cloud-based platforms L Shao, Y Zhu, S Liu, A Eswaran, K Lieber, J Mahajan, M Thigpen, ... Proceedings of the ACM Symposium on Cloud Computing, 441-452, 2019 | 13 | 2019 |
| Doppler: automated SKU recommendation in migrating SQL workloads to the cloud J Cahoon, W Wang, Y Zhu, K Lin, S Liu, R Truong, N Singh, C Wan, ... arXiv preprint arXiv:2208.04978, 2022 | 9 | 2022 |
| Phoebe: A Learning-based Checkpoint Optimizer Y Zhu, M Interlandi, A Roy, K Das, H Patel, M Bag, H Sharma, A Jindal Proceedings of the VLDB Endowment 14 (11), 2505-2518, 2021 | 8 | 2021 |
| Data Science through the looking glass and what we found there. CoRR abs/1912.09536 (2019) F Psallidas, Y Zhu, B Karlas, M Interlandi, A Floratou, K Karanasos, W Wu, ... arXiv preprint arXiv:1912.09536, 2019 | 8 | 2019 |
| Passenger-to-itinerary assignment model based on automated data Y Zhu Northeastern University, 2017 | 7 | 2017 |
| Towards Building Autonomous Data Services on Azure Y Zhu, Y Tian, J Cahoon, S Krishnan, A Agarwal, R Alotaibi, ... Companion of the 2023 International Conference on Management of Data, 217-224, 2023 | 6 | 2023 |
Subru KrishnanMicrosoftVerifierad e-postadress på microsoft.com