Matteo Interlandi


I am a Senior Research Scientist in the AI Frameworks group 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

  • 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

  • 11/09/2017 - Our paper Towards High-Performance Prediction Serving Systems is accepted @ NIPS ML Systems Workshop

  • 07/18/2017 - Our paper Automated Debugging in Data-Intensive Scalable Computing is accepted @ SoCC 2017

  • 07/07/2017 - Our paper Fixpoint Semantics and Optimization of Recursive Datalog Programs with Aggregates is accepted @ ICLP 2017

  • 05/15/2017 - This June I will be joining the CISL group at Microsoft to work on new super exciting machine learning problems.

  • 04/11/2017 - Our paper Adding Data Provenance Support to Apache Sparks with Ari Ekmekji, Kshitij Shah, Muhammad Ali Gulzar, Sai Deep Tetali, Miryung Kim, Todd Millstein and Tyson Condie, is accepted for publication in The VLDB Journal Special Issue on (the Best Papers of) VLDB 2016

  • 03/20/2017 - This June I will present two of our projects at the Spark Summit 2017

  • 02/26/2017 - Our demo Debugging Big Data Analytics in Spark with BigDebug with Muhammad Ali Gulzar, Tyson Condie, and Miryung Kim is accepted @ SIGMOD 2017

  • 01/26/2017 - I have contributed to the paper Apache REEF: Retainable Evaluator Execution Framework which will be published on TOCS

Previous News

Professional activities

Program Committee: HotCloud 2016, SIGMOD 2017 (Demo Track), TAPP 2018, SoCC 2018, MLOSS 2018, DASFAA 2019.

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


Papers

  • 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 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

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

  • 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)

  • Supporting Data Provenance in Data-Intensive Scalable Computing Systems
    Matteo Interlandi and Tyson Condie
    IEEE Data Engineering Bulleting

  • Automated Debugging in Data-Intensive Scalable Computing
    Muhammad Ali Gulzar, Matteo Interlandi, Tyson Condie and Miryung Kim
    Symposium on Cloud Computing SoCC 2017

  • Fixpoint Semantics and Optimization of Recursive Datalog Programs with Aggregates
    Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky and Tyson Condie
    The 33rd International Conference on Logic Programming ICLP

  • Adding Data Provenance Support to Apache Sparks
    Matteo Interlandi, Ari Ekmekji, Kshitij Shah, Muhammad Ali Gulzar, Sai Deep Tetali, Miryung Kim, Todd Millstein and Tyson Condie
    The VLDB Journal VLDBJ (Special Issue on Best Papers of VLDB 2016)

  • Apache REEF: Retainable Evaluator Execution Framework
    Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Matteo Interlandi, 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, Seysim Sezgin, Taegeon Um, Julia Wang, Markus Weimer and Youngseok Yang
    The ACM Transactions on Computer Systems TOCS

  • Debugging Big Data Analytics in Spark with BigDebug
    Muhammad Ali Gulzar, Matteo Interlandi, Tyson Condie and Miryung Kim
    The 2017 International Conference on Management of Data SIGMOD 2017 (demo)

Full papers list

Recent talks

  • UCLA Big Data and Machine Learning Seminar, Los Angeles, CA, USA October 2018
    Machine Learning is / as a System Problem

  • SysML 2018, Stanford, CA, USA February 2018
    Towards High-Performance Prediction Serving Systems

  • Spark Summit 2017, San Francisco, CA, USA June 2017
    Debugging Big Data Analytics In Apache Spark With BigDebug

  • Spark Summit 2017, San Francisco, CA, USA June 2017
    Lazy Join Optimization Without Upfront Statistics

  • SoCC 2016, Santa Clara, CA, USA October 2016
    Optimizing Interactive Development of Data-Intensive Applications

  • Platofora, San Mateo, CA, USA, October 2016
    Interactive Debugging of Big Data Programs

  • UCLA DB Seminar, Los Angeles, CA, USA October 2016
    Optimizing Interactive Development of Data-Intensive Applications

  • VLDB 2016, New Delhi, India, September 2016
    Titian: Data Provenance support in Spark

  • 2016 Big Data Day LA, Los Angeles, CA, USA, July 2016
    Data Provenance Support in Spark

  • Teradata, Los Angeles, CA, USA, June 2016
    Provenance Debugging for Big Data Applications

  • ScAI Big Data Symposium, Los Angeles, CA, USA, August 2015
    On the CALM principle for Bulk Synchronous Parallel (BSP) computation: towards coordination-free data-parallel systems

  • Politecnico di Milano, Milan, Italy, February 2014
    On Declarative Data-Parallel Computation: Models, Languages and Semantics


Contact information