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Matteo Brucato

Researcher at Microsoft Research

mbrucato@microsoft.com
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DOWNLOAD CV   My Research Statement

Favourite Quotes

“The process of preparing programs for a digital computer is especially attractive, not only because it can be economically and scientifically rewarding, but also because it can be an aesthetic experience much like composing poetry or music”
“If you can't explain it simply, you don't understand it well enough”
— Albert Einstein

Research Interests

I am a researcher at Microsoft Research. My research focuses on augmenting data management systems to support business analytics, with emphasis on data-driven predictive and prescriptive analytics.

The ultimate goal of my research is to democratize decision making in a variety of fundamental applications and disciplines, such as financial planning, meal planning, robotics, fair machine learning, text summarization, information retrieval, and systems.

Education

In my Ph.D. dissertation, I built a complete and efficient data management system (PackageBuilder) able to support a broad class of decision-making problems that can be expressed as integer linear programs (ILP), on both certain and uncertain data. My work has been recognized by several ACM awards.

I obtained my Ph.D. in Computer Science from the University of Massachusetts Amherst, where I collaborated with members of the DREAM Lab, and the Human Data (HuDa) Interaction Lab from NYU Abu Dhabi. I received my Bachelor's and Master's degrees in Computer Science from the Computer Science and Engineering Department at the University of Bologna, Italy.

During summer 2017, I interned at Microsoft Research, in the Data Systems Group (formerly DMX). During summer 2016, I interned at IBM Watson. During spring 2016, I visited NYU Abu Dhabi, UAE. During summer 2015, I visited the AMPLab at the University of California, Berkeley. During my Master's studies, I visited the Data-Intensive Systems group at Aarhus University, Denmark. During my Bachelor's studies, I interned in the Database Lab at the University of California, Riverside.

Videos

  • VLDB 2020 Best Demonstration Award:
  • SIGMOD 2020 Technical Paper Presentation:

Projects

  •  
    Package Builder — a system that extends query engines to support package generation.
  •  
    Scalable Markov Decision Processes
  •  
    Scalable Fair Machine Learning — collaboration with Microsoft for improving their Fairlearn tool (code).
  •  
    SuDocu — Example-Driven Personalized Document Summarization
  •  
    Temporal Information Retrieval

Publications

  1. Wred: Workload Reduction for Scalable Index Tuning
    Matteo Brucato, Tarique Siddiqui, Wentao Wu, Vivek Narasayya, Surajit Chaudhuri
    SIGMOD 2024
    Paper
  2. Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization
    Anh L. Mai, Pengyu Wang, Azza Abouzied, Matteo Brucato, Peter J. Haas, Alexandra Meliou
    VLDB 2024
    Paper Paper (Extended)
  3. Ranking Models for the Temporal Dimension of Text
    Stefano Giovanni Rizzo, Matteo Brucato, Danilo Montesi.
    ACM Transactions on Information Systems (TOIS) 2022
    Paper
  4. SubSumE: A Dataset for Subjective Summary Extraction from Wikipedia Documents
    Nishant Yadav, Matteo Brucato, Anna Fariha, Oscar Yongquist, Julian Killingback, Alexandra Meliou, Peter J. Haas.
    NewSum at EMNLP 2021 (workshop)
    Paper
  5. Solving Markov Decision Processes with Partial State Abstractions
    Samer B. Nashed, Justin Svegliato, Matteo Brucato, Connor Basich, Rod Grupen, Shlomo Zilberstein.
    ICRA 2021
    Paper
  6. sPaQLTooLs: A Stochastic Package Query Interface for Scalable Constrained Optimization
    Matteo Brucato, Miro Mannino, Azza Abouzied, Peter J. Haas, Alexandra Meliou.
    VLDB 2020 (demo)
    Paper Talk Best Demonstration Award
  7. SuDocu: Summarizing Documents by Example
    Anna Fariha, Matteo Brucato, Peter J. Haas, Alexandra Meliou.
    VLDB 2020 (demo)
    Paper Talk Best Demonstration Runner-up Award
  8. Stochastic Package Queries in Probabilistic Databases
    Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou.
    SIGMOD 2020
    Paper Paper (Extended) Talk
  9. Scalable Computation of High-Order Optimization Queries
    Matteo Brucato, Azza Abouzied, Alexandra Meliou.
    CACM 2019
    Paper CACM Research Highlight Technical Perspective by Surajit Chaudhuri
  10. Package queries: efficient and scalable computation of high-order constraints
    Matteo Brucato, Azza Abouzied, Alexandra Meliou.
    VLDBJ 2018 (Special Issue on Best Papers of VDLB 2016)
    Pre-print Final publication
  11. A Scalable Execution Engine for Package Queries
    Matteo Brucato, Azza Abouzied, Alexandra Meliou.
    SIGMOD Record 2017
    Paper ACM SIGMOD Research Highlight Award
  12. Redistributing Funds across Charitable Crowdfunding Campaigns
    Matteo Brucato, Azza Abouzied, Chris Blauvelt.
    arXiv 2017
    Paper
  13. Scalable Package Queries in Relational Database Systems
    Matteo Brucato, Juan Felipe Beltran, Azza Abouzied, Alexandra Meliou.
    VLDB 2016
    Paper Paper (Extended) Slides Poster Best Papers of VDLB 2016
  14. Improving Package Recommendations through Query Relaxation
    Matteo Brucato, Azza Abouzied, Alexandra Meliou.
    Data4U at VLDB 2014 (workshop)
    Paper Slides
  15. PackageBuilder: From Tuples to Packages
    Matteo Brucato, Rahul Ramakrishna, Azza Abouzied, Alexandra Meliou.
    VLDB 2014 (demo)
    Paper Poster Challenge Poster
  16. PackageBuilder: Querying for packages of Tuples
    Kevin Fernandes, Matteo Brucato, Rahul Ramakrishna, Azza Abouzied, Alexandra Meliou.
    SIGMOD 2014 (poster)
    Paper
  17. Metric Spaces for Temporal Information Retrieval
    Matteo Brucato, Danilo Montesi.
    ECIR 2014
    Paper Slides
  18. Recognising and Interpreting Named Temporal Expressions
    Matteo Brucato, Leon Derczynski, Hector Llorens, Kalina Bontcheva, Christian S. Jensen.
    RANLP 2013
    Paper Best Young Researcher Award Nominee

Service

Talks

Matteo Brucato
Researcher
Microsoft Research, Redmond
mbrucato@microsoft.com