Matteo Interlandi


I am a Senior Scientist in GSL at Microsoft, working on scalable Machine Learning systems. Before Microsoft, I was a Postdoctoral Scholar in the CS Department at the University of California, Los Angeles under the supervision of Professor Tyson Condie and working on Big Data systems.

Prior to joining UCLA, I was Research Associate at the Qatar Computing Research Institute and at the Institute for Human and Machine Cognition. I obtained my PhD in Computer Science at the University of Modena and Reggio Emilia.


News

    08/18/2020 - Our paper Steering Query Optimizers: A Practical Take on Big DataWorkloads is accepted @ SIGMOD 2021!

  • 08/18/2020 - Our paper on Hummingbird ("A Tensor-based Approach for One-size-fits-all ML Prediction Serving") is accepted @ OSDI 2020! The technical report can be found here.

  • 05/27/2020 - We recently open sourced Hummingbird.

  • 05/15/2020 - Our paper on Building Continuous Integration Services for Machine Learning is accepted @ KDD Applied Data Science Track as oral presentation (only 44\756 got oral this year)!

  • 05/02/2020 - Our paper on Explaining data with descriptions is accepted @ Information Systems.

  • 11/23/2019 - Apparently I am in the SoCC's top 10 most proficient authors.

  • 11/20/2019 - I will be presenting Humminbgbird @ the TVM conference.

  • 10/14/2019 - Two of our papers got accepted @ CIDR.

  • 10/12/2019 - Our paper Compiling Classical ML Pipelines into Tensor Computations for One-size-fits-all Prediction Serving got accepted @ Systems for ML workshop at NeurIPS 2019.

  • 09/02/2019 - Our paper Acorn: Aggressive Result Caching in Distributed Data Processing Frameworks is accepted @ SoCC 2019.

  • 08/10/2019 - Our demo Understanding Data in the Blink of an Eye is accepted @ CIKM 2019.

  • 04/29/2019 - Our full paper on ML.NET is accepted as poster presentation @ KDD.

  • 03/31/2019 - Our coded elastic computing paper is accepted @ ISIT.

  • 03/15/2019 - I will present a demo on ML.NET at SysML this spring.

  • 12/28/2018 - Our paper on model serving with ML.NET is in the December issue of IEEE Data Engineering Bulletin

  • 11/14/2018 - Four of our papers got accepted at NIPS 2018 workshops

  • 06/08/2018 - Our paper RIOS: Runtime Integrated Optimizer for Spark is accepted @ SoCC 2018

  • 01/08/2018 - Our paper PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems is accepted @ OSDI 2018

  • 01/08/2018 - Two of our articles will appear in TPLP Special issue on Past and Present (and Future) of Parallel and Distributed Computation in LP

  • 02/02/2018 - Our paper Supporting Data Provenance in Data-Intensive Scalable Computing Systems will appear in the March issue of IEEE Data Engineering Bulletin

Previous News

Professional activities

Program Committee: VLDB 2021, EuroSys 2020, TaPP 2020, DASFAA 2020, DASFAA 2019, MLOSS 2018, TaPP 2018, SoCC 2018, SIGMOD 2017 (Demo Track), HotCloud 2016.

External Reviewer: CIKM 2018, SIGMOD 2017, VLDB 2016, SIGMOD 2016, JDIQ 2016, TODS 2015, VLDB 2015, ICDE 2015, VLDB 2014, VLDB 2013, ICDE 2013.


Open Source


Papers

  • A Tensor Compiler for Unified Machine Learning Prediction Serving
    Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
    OSDI 2020

  • Building Continuous Integration Services for Machine Learning
    Bojan KarlaĆĄ, Matteo Interlandi, Cedric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
    KDD 2020

  • Extending Relational Query Processing with ML Inference
    Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino
    CIDR 2020

  • Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
    The GSL Team
    CIDR 2020

  • Compiling Classical ML Pipelines into Tensor Computations for One-size-fits-all Prediction Serving
    Supun Nakandala, Gyeong-In Yu, Markus Weimer, Matteo Interlandi
    Systems for ML Workshop at NeurIPS 2019

  • Acorn: Aggressive Result Caching in Distributed Data Processing Frameworks
    Lana Ramjit, Matteo Interlandi, Eugene Wu, Ravi Netravali
    SoCC 2019

  • Understanding Data in the Blink of an Eye
    Matteo Paganelli, Paolo Sottovia, Antonio Maccioni, Matteo Interlandi, Francesco Guerra
    CIKM 2019 (demo)

  • Machine Learning at Microsoft with ML.NET
    ML.NET Team
    KDD 2019 (Applied Data Science Track)

  • Coded Elastic Computing
    Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh and Markus Weimer
    ISIT 2019

  • ML.NET: Machine Learning Toolkit for Software Developers
    Matteo Interlandi, Sergiy Matusevych and Markus Weimer
    SysML (demo)

  • From the Edge to the Cloud: Model Serving in ML.NET
    Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer and Matteo Interlandi
    IEEE Data Engineering Bulleting

  • Machine Learning at Microsoft with ML.NET
    Matteo Interlandi, Sergiy Matusevych, Saeed Amizadeh, Shauheen Zahirazami and Markus Weimer
    Systems for ML Workshop at NeurIPS 2018 and Machine Learning Open Source Software Workshop

  • Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
    Gyeong-In Yu, Saeed Amizadeh, Byung-Gon Chun, Markus Weimer and Matteo Interlandi
    Systems for ML Workshop at NeurIPS 2018

  • Coded Elastic Computing
    Yaoqing Yang, Matteo Interlandi, Pulkit Grover, Soummya Kar, Saeed Amizadeh and Markus Weimer
    Systems for ML Workshop at NeurIPS 2018

  • Pretzel: Opening the Black Box of Machine Learning Prediction Serving Systems
    Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer and Matteo Interlandi
    Operating System Design and Implementation OSDI 2018

  • RIOS: Runtime Integrated Optimizer for Spark
    Youfu Li, Mingda Li, Ling Ding and Matteo Interlandi
    Symposium on Cloud Computing SoCC 2018

  • Scaling-up reasoning and advanced analytics on BigData
    Tyson Condie, Ariyam Das, Matteo Interlandi, Alexander Shkapsky, Mohan Yang and Carlo Zaniolo
    Theory and Practice of Logical Programming TPLP (Special issue on Past and Present (and Future) of Parallel and Distributed Computation in LP)

  • A Datalog-based Computational Model for Coordination-free, Data-Parallel Systems
    Matteo Interlandi and Letizia Tanca
    Theory and Practice of Logical Programming TPLP (Special issue on Past and Present (and Future) of Parallel and Distributed Computation in LP)

Full papers list


Contact information