Matteo Interlandi

Principal Scientist

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出版物

表示方法:

- Terabyte-Scale Analytics in the Blink of an Eye
  
  Bowen Wu, Wei Cui, Carlo Curino, Matteo Interlandi, Rathijit Sen
  
  VLDB 2026 | January 2026
  
  DOI Publication Publication
- Data Debugging with Shapley Importance over Machine Learning Pipelines
  
  Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang
  
  International Conference on Learning Representations (ICLR 2024) | January 2024
  
  Publication
- Optimizing Data Pipelines for Machine Learning in Feature Stores
  
  Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
  
  Proc. VLDB Endow. | August 2023, 巻16: pp. 4230-4239
  
  DOI PDF
- A Deep Dive into Common Open Formats for Analytical DBMSs
  
  Chunwei Liu, Anna Pavlenko, Matteo Interlandi, Brandon Haynes
  
  Proc. VLDB Endow. | June 2023, 巻16: pp. 3044-3056
  
  Best Paper Runner-Up
  
  DOI
- Query Processing on Gaming Consoles
  
  Wei Cui, Qianxi Zhang, Jesús Camacho-Rodríguez, Spyros Blanas, Brandon Haynes, Yinan Li, Peng Cheng, Ravishankar Ramamurthy, Rathijit Sen, Matteo Interlandi
  
  June 2023
  
  DOI Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas C. Müller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- The Tensor Data Platform: Towards an AI-centric Database System
  
  Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Lihao Zhang, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi
  
  CIDR | January 2023
  
  Publication
- Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
  
  Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
  
  VLDB | September 2022, 巻15(12): pp. 3598-3601
  
  Best demo award
  
  Publication
- Query Processing on Tensor Computation Runtimes
  
  Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
  
  VLDB 2022 | September 2022
  
  Publication
- Deploying a Steered Query Optimizer in Production at Microsoft
  
  Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
  
  2022 International Conference on Management of Data | July 2022
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- End-to-end Optimization of Machine Learning Prediction Queries
  
  Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos
  
  SIGMOD | June 2022
  
  Publication
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model
  
  Gyeong-in Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
  
  VLDB 2022 | September 2021
  
  Publication
- Phoebe: A Learning-based Checkpoint Optimizer
  
  Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal
  
  VLDB 2021 | August 2021
  
  Publication
- Steering Query Optimizers: A Practical Take on Big Data Workloads
  
  Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal
  
  2021 International Conference on Management of Data | June 2021
  
  Honorable mention for the Industry Track
  
  DOI PDF Publication Publication プロジェクト
- Tensors: An abstraction for general data processing
  
  Dimitrios Koutsoukos, Supun Nakandala, Konstantinos Karanasos, Karla Saur, Gustavo Alonso, Matteo Interlandi
  
  VLDB 2021 | December 2020
  
  PDF Publication
- A Tensor Compiler for Unified Machine Learning Prediction Serving
  
  Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
  
  Symposium on Operating Systems Design and Implementation (OSDI) | November 2020
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Coded Elastic Computing
  
  Yaoqing Yang, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Matteo Interlandi, Markus Weimer
  
  International Symposium on Information Theory | May 2019
  
  DOI PDF Publication Publication
- From the Edge to the Cloud: Model Serving in ML.NET.
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Matteo Interlandi, Markus Weimer
  
  IEEE Data(base) Engineering Bulletin | December 2018, 巻41: pp. 46-53
  
  PDF PDF Publication
- PRETZEL: opening the black box of machine learning prediction serving systems
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Matteo Interlandi, Markus Weimer
  
  Operating Systems Design and Implementation | October 2018
  
  PDF Publication Publication
- Towards High-Performance Prediction Serving Systems
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Matteo Interlandi, Markus Weimer
  
  31st Conference on Neural Information Processing Systems | December 2017
  
  PDF
- Apache REEF: Retainable Evaluator Execution Framework
  
  Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Youngseok Yang, Raghu Ramakrishnan, Carlo Curino, Matteo Interlandi
  
  ACM Transactions on Computer Systems | October 2017, 巻35(2): pp. 5
  
  DOI PDF Publication

- Terabyte-Scale Analytics in the Blink of an Eye
  
  Bowen Wu, Wei Cui, Carlo Curino, Matteo Interlandi, Rathijit Sen
  
  VLDB 2026 | January 2026
  
  DOI Publication Publication
- Coded Elastic Computing
  
  Yaoqing Yang, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Matteo Interlandi, Markus Weimer
  
  International Symposium on Information Theory | May 2019
  
  DOI PDF Publication Publication
- PRETZEL: opening the black box of machine learning prediction serving systems
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Matteo Interlandi, Markus Weimer
  
  Operating Systems Design and Implementation | October 2018
  
  PDF Publication Publication
- Towards High-Performance Prediction Serving Systems
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Matteo Interlandi, Markus Weimer
  
  31st Conference on Neural Information Processing Systems | December 2017
  
  PDF
- Apache REEF: Retainable Evaluator Execution Framework
  
  Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Youngseok Yang, Raghu Ramakrishnan, Carlo Curino, Matteo Interlandi
  
  ACM Transactions on Computer Systems | October 2017, 巻35(2): pp. 5
  
  DOI PDF Publication
- Data Debugging with Shapley Importance over Machine Learning Pipelines
  
  Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang
  
  International Conference on Learning Representations (ICLR 2024) | January 2024
  
  Publication
- Optimizing Data Pipelines for Machine Learning in Feature Stores
  
  Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
  
  Proc. VLDB Endow. | August 2023, 巻16: pp. 4230-4239
  
  DOI PDF
- A Deep Dive into Common Open Formats for Analytical DBMSs
  
  Chunwei Liu, Anna Pavlenko, Matteo Interlandi, Brandon Haynes
  
  Proc. VLDB Endow. | June 2023, 巻16: pp. 3044-3056
  
  Best Paper Runner-Up
  
  DOI
- Query Processing on Gaming Consoles
  
  Wei Cui, Qianxi Zhang, Jesús Camacho-Rodríguez, Spyros Blanas, Brandon Haynes, Yinan Li, Peng Cheng, Ravishankar Ramamurthy, Rathijit Sen, Matteo Interlandi
  
  June 2023
  
  DOI Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas C. Müller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- The Tensor Data Platform: Towards an AI-centric Database System
  
  Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Lihao Zhang, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi
  
  CIDR | January 2023
  
  Publication
- Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
  
  Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
  
  VLDB | September 2022, 巻15(12): pp. 3598-3601
  
  Best demo award
  
  Publication
- Query Processing on Tensor Computation Runtimes
  
  Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
  
  VLDB 2022 | September 2022
  
  Publication
- Deploying a Steered Query Optimizer in Production at Microsoft
  
  Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
  
  2022 International Conference on Management of Data | July 2022
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- End-to-end Optimization of Machine Learning Prediction Queries
  
  Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos
  
  SIGMOD | June 2022
  
  Publication
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model
  
  Gyeong-in Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
  
  VLDB 2022 | September 2021
  
  Publication
- Phoebe: A Learning-based Checkpoint Optimizer
  
  Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal
  
  VLDB 2021 | August 2021
  
  Publication
- Steering Query Optimizers: A Practical Take on Big Data Workloads
  
  Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal
  
  2021 International Conference on Management of Data | June 2021
  
  Honorable mention for the Industry Track
  
  DOI PDF Publication Publication プロジェクト
- Tensors: An abstraction for general data processing
  
  Dimitrios Koutsoukos, Supun Nakandala, Konstantinos Karanasos, Karla Saur, Gustavo Alonso, Matteo Interlandi
  
  VLDB 2021 | December 2020
  
  PDF Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Data Debugging with Shapley Importance over Machine Learning Pipelines
  
  Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang
  
  International Conference on Learning Representations (ICLR 2024) | January 2024
  
  Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas C. Müller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- The Tensor Data Platform: Towards an AI-centric Database System
  
  Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Lihao Zhang, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi
  
  CIDR | January 2023
  
  Publication
- Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
  
  Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
  
  VLDB | September 2022, 巻15(12): pp. 3598-3601
  
  Best demo award
  
  Publication
- Query Processing on Tensor Computation Runtimes
  
  Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
  
  VLDB 2022 | September 2022
  
  Publication
- Deploying a Steered Query Optimizer in Production at Microsoft
  
  Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
  
  2022 International Conference on Management of Data | July 2022
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- End-to-end Optimization of Machine Learning Prediction Queries
  
  Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos
  
  SIGMOD | June 2022
  
  Publication
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- A Tensor Compiler for Unified Machine Learning Prediction Serving
  
  Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
  
  Symposium on Operating Systems Design and Implementation (OSDI) | November 2020
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- Tensors: An abstraction for general data processing
  
  Dimitrios Koutsoukos, Supun Nakandala, Konstantinos Karanasos, Karla Saur, Gustavo Alonso, Matteo Interlandi
  
  VLDB 2021 | December 2020
  
  PDF Publication
- A Tensor Compiler for Unified Machine Learning Prediction Serving
  
  Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
  
  Symposium on Operating Systems Design and Implementation (OSDI) | November 2020
  
  Publication
- From the Edge to the Cloud: Model Serving in ML.NET.
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Matteo Interlandi, Markus Weimer
  
  IEEE Data(base) Engineering Bulletin | December 2018, 巻41: pp. 46-53
  
  PDF PDF Publication
- A Tensor Compiler for Unified Machine Learning Prediction Serving
  
  Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
  
  Symposium on Operating Systems Design and Implementation (OSDI) | November 2020
  
  Publication

- Terabyte-Scale Analytics in the Blink of an Eye
  
  Bowen Wu, Wei Cui, Carlo Curino, Matteo Interlandi, Rathijit Sen
  
  VLDB 2026 | January 2026
  
  DOI Publication Publication
- Data Debugging with Shapley Importance over Machine Learning Pipelines
  
  Bojan Karlas, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu, Ce Zhang
  
  International Conference on Learning Representations (ICLR 2024) | January 2024
  
  Publication
- Query Processing on Gaming Consoles
  
  Wei Cui, Qianxi Zhang, Jesús Camacho-Rodríguez, Spyros Blanas, Brandon Haynes, Yinan Li, Peng Cheng, Ravishankar Ramamurthy, Rathijit Sen, Matteo Interlandi
  
  June 2023
  
  DOI Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas C. Müller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- The Tensor Data Platform: Towards an AI-centric Database System
  
  Apurva Gandhi, Yuki Asada, Victor Fu, Advitya Gemawat, Lihao Zhang, Rathijit Sen, Carlo Curino, Jesús Camacho-Rodríguez, Matteo Interlandi
  
  CIDR | January 2023
  
  Publication
- Query Processing on Tensor Computation Runtimes
  
  Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
  
  VLDB 2022 | September 2022
  
  Publication
- Deploying a Steered Query Optimizer in Production at Microsoft
  
  Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
  
  2022 International Conference on Management of Data | July 2022
  
  Publication
- End-to-end Optimization of Machine Learning Prediction Queries
  
  Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos
  
  SIGMOD | June 2022
  
  Publication
- WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model
  
  Gyeong-in Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi
  
  VLDB 2022 | September 2021
  
  Publication
- Phoebe: A Learning-based Checkpoint Optimizer
  
  Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal
  
  VLDB 2021 | August 2021
  
  Publication
- Steering Query Optimizers: A Practical Take on Big Data Workloads
  
  Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal
  
  2021 International Conference on Management of Data | June 2021
  
  Honorable mention for the Industry Track
  
  DOI PDF Publication Publication プロジェクト
- Tensors: An abstraction for general data processing
  
  Dimitrios Koutsoukos, Supun Nakandala, Konstantinos Karanasos, Karla Saur, Gustavo Alonso, Matteo Interlandi
  
  VLDB 2021 | December 2020
  
  PDF Publication
- A Tensor Compiler for Unified Machine Learning Prediction Serving
  
  Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
  
  Symposium on Operating Systems Design and Implementation (OSDI) | November 2020
  
  Publication
- Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  
  Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Extending Relational Query Processing with ML Inference
  
  Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  Publication
- Coded Elastic Computing
  
  Yaoqing Yang, Pulkit Grover, Soummya Kar, Saeed Amizadeh, Matteo Interlandi, Markus Weimer
  
  International Symposium on Information Theory | May 2019
  
  DOI PDF Publication Publication
- PRETZEL: opening the black box of machine learning prediction serving systems
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Matteo Interlandi, Markus Weimer
  
  Operating Systems Design and Implementation | October 2018
  
  PDF Publication Publication
- Towards High-Performance Prediction Serving Systems
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Matteo Interlandi, Markus Weimer
  
  31st Conference on Neural Information Processing Systems | December 2017
  
  PDF
- Optimizing Data Pipelines for Machine Learning in Feature Stores
  
  Rui Liu, Kwanghyun Park, Fotis Psallidas, Xiaoyong Zhu, Jinghui Mo, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos, Yuanyuan Tian, Jesús Camacho-Rodríguez
  
  Proc. VLDB Endow. | August 2023, 巻16: pp. 4230-4239
  
  DOI PDF
- A Deep Dive into Common Open Formats for Analytical DBMSs
  
  Chunwei Liu, Anna Pavlenko, Matteo Interlandi, Brandon Haynes
  
  Proc. VLDB Endow. | June 2023, 巻16: pp. 3044-3056
  
  Best Paper Runner-Up
  
  DOI
- Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
  
  Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
  
  VLDB | September 2022, 巻15(12): pp. 3598-3601
  
  Best demo award
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- From the Edge to the Cloud: Model Serving in ML.NET.
  
  Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Matteo Interlandi, Markus Weimer
  
  IEEE Data(base) Engineering Bulletin | December 2018, 巻41: pp. 46-53
  
  PDF PDF Publication
- Apache REEF: Retainable Evaluator Execution Framework
  
  Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Youngseok Yang, Raghu Ramakrishnan, Carlo Curino, Matteo Interlandi
  
  ACM Transactions on Computer Systems | October 2017, 巻35(2): pp. 5
  
  DOI PDF Publication

Matteo Interlandi

出版物

Terabyte-Scale Analytics in the Blink of an Eye

Data Debugging with Shapley Importance over Machine Learning Pipelines

Optimizing Data Pipelines for Machine Learning in Feature Stores

A Deep Dive into Common Open Formats for Analytical DBMSs

Query Processing on Gaming Consoles

Towards Building Autonomous Data Services on Azure

The Tensor Data Platform: Towards an AI-centric Database System

Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

Query Processing on Tensor Computation Runtimes

Deploying a Steered Query Optimizer in Production at Microsoft

Data Science Through the Looking Glass

End-to-end Optimization of Machine Learning Prediction Queries

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model

Phoebe: A Learning-based Checkpoint Optimizer

Steering Query Optimizers: A Practical Take on Big Data Workloads

Tensors: An abstraction for general data processing

A Tensor Compiler for Unified Machine Learning Prediction Serving

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Coded Elastic Computing

From the Edge to the Cloud: Model Serving in ML.NET.

PRETZEL: opening the black box of machine learning prediction serving systems

Towards High-Performance Prediction Serving Systems

Apache REEF: Retainable Evaluator Execution Framework

Terabyte-Scale Analytics in the Blink of an Eye

Coded Elastic Computing

PRETZEL: opening the black box of machine learning prediction serving systems

Towards High-Performance Prediction Serving Systems

Apache REEF: Retainable Evaluator Execution Framework

Data Debugging with Shapley Importance over Machine Learning Pipelines

Optimizing Data Pipelines for Machine Learning in Feature Stores

A Deep Dive into Common Open Formats for Analytical DBMSs

Query Processing on Gaming Consoles

Towards Building Autonomous Data Services on Azure

The Tensor Data Platform: Towards an AI-centric Database System

Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

Query Processing on Tensor Computation Runtimes

Deploying a Steered Query Optimizer in Production at Microsoft

Data Science Through the Looking Glass

End-to-end Optimization of Machine Learning Prediction Queries

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model

Phoebe: A Learning-based Checkpoint Optimizer

Steering Query Optimizers: A Practical Take on Big Data Workloads

Tensors: An abstraction for general data processing

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Data Debugging with Shapley Importance over Machine Learning Pipelines

Towards Building Autonomous Data Services on Azure

The Tensor Data Platform: Towards an AI-centric Database System

Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

Query Processing on Tensor Computation Runtimes

Deploying a Steered Query Optimizer in Production at Microsoft

Data Science Through the Looking Glass

End-to-end Optimization of Machine Learning Prediction Queries

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines

A Tensor Compiler for Unified Machine Learning Prediction Serving

Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

Extending Relational Query Processing with ML Inference

Data Science Through the Looking Glass

Tensors: An abstraction for general data processing

A Tensor Compiler for Unified Machine Learning Prediction Serving

From the Edge to the Cloud: Model Serving in ML.NET.

A Tensor Compiler for Unified Machine Learning Prediction Serving

Terabyte-Scale Analytics in the Blink of an Eye

Data Debugging with Shapley Importance over Machine Learning Pipelines

Query Processing on Gaming Consoles

Towards Building Autonomous Data Services on Azure

The Tensor Data Platform: Towards an AI-centric Database System

Query Processing on Tensor Computation Runtimes

Deploying a Steered Query Optimizer in Production at Microsoft

End-to-end Optimization of Machine Learning Prediction Queries

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model

Phoebe: A Learning-based Checkpoint Optimizer

Steering Query Optimizers: A Practical Take on Big Data Workloads

Tensors: An abstraction for general data processing

A Tensor Compiler for Unified Machine Learning Prediction Serving