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Principal Scientist Manager at Microsoft GSL
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I am a Scientist in Microsoft CISL. I love to build systems and tools making easier to design and implement efficient data-driven distributed applications.
Università degli Studi di Modena e Reggio Emilia
Doctor of Philosophy (Ph.D.), ICT
January 1, 2011 – January 1, 2013
Microsoft
Principal Scientist Manager
November 1, 2024 – Present
Microsoft
Principal Research Scientist
June 1, 2017 – June 1, 2025
UCLA
Postdoc
August 1, 2014 – June 1, 2017
Greater Los Angeles Area
Qatar Computing Research Institute
Research Associate
March 1, 2014 – July 1, 2014
Doha, Qatar
UCLA
Visiting PHD Student
June 1, 2013 – December 1, 2013
University of Padova
Research Assistant
January 1, 2011 – March 1, 2011
Padova Area, Italy
University of Modena and Reggio Emilia
Research Assistant
September 1, 2010 – December 1, 2010
Modena Area, Italy
Florida Institute for Human and Machine Cognition
Resarch Associate
November 1, 2008 – December 1, 2009
Pensacola, Florida Area
System Electronics S.p.A.
Intern
September 1, 2005 – December 1, 2005
Modena Area, Italy
Big Data Debugger
January 1, 2015 – Present
This project aims at the development of an interactive debugger for Big Data Applications. In this context I have (1) helped in designing the debugger features, (2) developed the data lineage framework (Titian). This work appeared in HPTS2016, ICSE2016, HotCloud2016, FSE2016.
Hyperdrive
January 1, 2015 – Present
Development of new scheduling techniques for increasing the performance of Spark for milliseconds-level tasks. Using Hyperdrive we were able to improve by 10X the performance of Titian.
Vega
January 1, 2015 – January 1, 2016
Incremental evaluation module on top of Spark. Using Vega data scientist can interactively refine programs while reusing previous outcomes. Thanks to language-level optimizations, Vega is able to evaluate edited programs orders of magnitude faster than Spark. This work will appear in SOCC2016.
Sherlock
January 1, 2014 – Present
Development of a new type of data cleaning rule able to detect and repair dirty datasets using (partially available) ground truth data. This work appeared in ICDE2015.
Titian
January 1, 2014 – January 1, 2015
Development of an extension of the Spark RDD model providing fine-grained data provenance capture and trace functionality. This work was published in VLDB2016.
QUEST
January 1, 2013 – Present
Development of a keyword-search engine over DBMS that mixes a Hidden Markov Model approach with a graph-based approach to return accurate SQL queries. Appeared in VLDB2013 and IS2016.
Declarative Parallel Programming
January 1, 2011 – Present
Development of Datalog-based language, model, and semantics for declarative programming of parallel (synchronous) systems. Published in Datalog2.0, AMW2013, and AMW2015. A system implementation on Spark appeared in SIGMOD2016.
eLog
January 1, 2011 – Present
Development of a semi-static OLAP framework. The framework allow users non proficient with OLAP tools to create business reports through a controlled interface compiling request in MDX queries. Published in ICDE2012.
DisService
January 1, 2008 – January 1, 2009
Design and implementation of a publish/subscribe service that uses an efficient combination of replication and forwarding policies to disseminate information in tactical wireless networks. Published in MILCOM2009 and MILCOM2010.
Microsoft Global Hackathon Executive Challenge 2022 Winner
Microsoft
June 24, 2026 – Present
Microsoft Global Hackathon 2022
Microsoft
June 24, 2026 – Present
Fall 2020 MLADS Conference Presenter
Microsoft
June 24, 2026 – Present
Web Intelligence and Big Data
Coursera
June 24, 2026 – Present
Introduction to Databases
Coursera
June 24, 2026 – Present
Introduction to Machine Learning
Coursera
June 24, 2026 – Present
Functional Programming Principles in Scala
Coursera
June 24, 2026 – Present
Introduction to Logic
Coursera
June 24, 2026 – Present
Model Thinking
Coursera
June 24, 2026 – Present
Algorithms: Design and Analysis, Part 1
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards academic research and large-scale data systems, with a strong emphasis on distributed computing and data management. While this demonstrates a high level of technical rigor and innovation, the target role is 'Computer Vision'. There is a significant gap in directly relevant experience or projects in computer vision. The projects listed are primarily in data analytics, databases, and distributed systems. This suggests a potential mismatch with the specific domain requirements of a Computer Vision role, although the underlying strong technical foundation could be transferable with significant upskilling.
Soft Skills & Operational Fit
The candidate's extensive publication record and roles at Microsoft and UCLA suggest strong analytical, problem-solving, and research skills. The descriptions of projects like 'Big Data Debugger' and 'Hyperdrive' indicate a collaborative approach to complex system development. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.