Over the years I realised my main two passions being distributed systems and artificial intelligence, and I am lucky to have experienced that in both research with multiagent systems and industry throughout the Big Data hype. I consider myself as the bridge between those data engineers looking at infrastructural aspects, and the pure data scientists who are focused on the mathematical aspects. My goal is to deliver customers with production-ready use cases that add value to their data or infrastructure, while considering performance and system-oriented aspects. Somebody calls this position Machine Learning Engineer. If you are still in doubt, I invite you to read this nice post about the gap between Data Scientists and Data Engineers.

** Since July 2018 I am on a sabbatical leave, traveling and doing independent research **

From July 1st 2016 to end of June 2018 I was with Data Reply GmbH (Reply AG) in München (Germany) as a Data Science & Engineering consultant, and I mainly worked with the Confluent (Kafka, Kafka connect, Kafka Streams) and Hadoop (Hadoop, Hive, Impala, Hue, Spark, HBase, Ooozie, Sqoop) ecosystems, both on on-premise (i.e., cloudera, hortonworks) and on-cloud (mainly AWS and Azure) infrastructures. The company has a focus on Big Data Science and Engineering. Beyond the hype, what I did was to design software artefacts for large-scale computing clusters, which can run AI algorithms on massive datasets in order to deliver business insights and services.


Among the successfully completed projects:
  • Asset Replacement Optimization: priorization of medium voltage cables and substations to prevent outages (Azure cloud, PySpark, Scikit-learn, Gitlab CI, Docker/Kubernetes). This includes both explorative data analysis of SAP, GIS and outage databases, as well as the industrialization of existing pyspark/scikit learn code extracting the models.
  • Fraud Detection: unsupervised identification of likely fraudolent behavior from Conviva set-top-box data (Spark, Kafka-Connect). This included mainly the industrialization of Java Spark code, as well as the setup of two new Kafka connectors to ingest Conviva data.
  • Customer Segmentation: scaleout of pyspark code doing k-means-based basket analysis (PySpark). This included test automatization of existing PySpark code and the finetuning of Spark runtime parameters.
  • Anomalous Energy Demand Alerting: ARIMA-based early detection of demand peaks for large energy consumers (Spark, AWS)
  • CP 360: processing of usage logs and catalogue information to build a customer 360 querable view (Java Spark Streaming, HBase, Hive, Oozie, Ansible)
  • Automated Business reporting for Financial Department (Oozie, Hive, Kafka, Kafka-connect, Ansible)
  • Setup of Ingestion connectors for a Data Lake, mostly JDBC, HDFS, Couchbase, HTTP, Logs, FTP (Sqoop, Kafka, Kafka-connect, Ansible)

For the period February-June 2016 I have been IT Consultant for Smart Energy solutions at Power Reply GmbH (Reply AG). There I worked on mobile energy analytics projects with German energy utilities, focusing mostly on Android development and REST/JSON web services running on the Spring framework. I focused on demand-related analytics, specifically empowered by load disaggregation and mostly targeted to residential customers. This included:
  • Load Disaggregation App for residential customers (Android, Spring, Spring boot), where I worked on event detection from outlet data, initially implemented in Python/Scikit-learn and then converted to a batch Spring task writing the results to PostgreSQL; The events are used as ground truth in a pilot study, and a set of metrics are periodically calculated and shown on a jquery dashboard to assess the third-party load disaggregation component used to identify the device events out of the aggregated power signal.
  • Mobile App for energy related value-added services, mainly to have all billing and historical consumption and production information displayed, as well as location-based services, such as coupons. The use of Crashlytics and Google analytics allows for the tracking of the application usage and a quick bug fixing, as well as user modeling. (Android, REST backend, Crashlytics, Google Analytics)

Until January 2016, I have been doing research on power trading and brokerage for Microgrids, supported by a research scholarship funded by the Alpen-Adria-Universität Klagenfurt. I eventually defended my PhD dissertation in September 2016.
In the period running from October 2012 to August 2015 I have been with the Lakeside Labs, a research cluster focusing on self-organising systems, namely working for the MONERGY Interreg IV project and the Smart Grid Lab. There I worked on intelligent energy applications in Smart homes, namely on solutions to improve energy awareness and facilitate the integration of renewable energy generation.
In June 2012, I received a Master in Computer Science from Reykjavik University (Iceland) and University of Camerino (Italy). During my first year in Camerino, I focused on distributed and multi agent systems. I spent my second year at Reykjavik University in Iceland. I am a former student of the Center for Analysis and Design of Intelligent Agents (CADIA), where I wrote my thesis and I attended courses on Artificial Intelligence. My project dealt with the implementation of an Early Warning System for Ambient Assisted Living, basically a context-aware agent that, given a description of the environment as input, is able to autonomously evaluate the danger level of states and prevent users from getting too close to such dangers. Together with other 7 recipients, I was awarded for this project the best MS thesis award by the Marche regional government.
In 2010, I graduated as a B.S. in Applied Computer Science with focus on Embedded Systems at "Carlo Bo" University of Urbino. I have attended courses for designing embedded systems (e.g. digital electronics, electronic design automation, architectures and communication), as well as multimedia systems (e.g. multimedia processing, languages and applications, Information Systems). My final project concerned the use of Contiki OS for measuring environmental parameters in internet-of-things applications.
In 2009, I spent 3 months as intern software engineer for Townet, a leading company in wireless broadband networks in the ISM band. In this period, I had the chance to participate to the complete hardware and software development process and get an overall understanding of startup companies. Previously, I have worked as intern for companies involved in electronics and automation (ATE Elettronica), as well as web development (Lyn-X).

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