Carlo Lipizzi

Teaching Associate Professor & Program Lead

School: School of Systems and Enterprises

Building: Babbio Center

Room: 504

Phone: (201) 216-3303

Fax: (201) 216-5541

Email: clipizzi@stevens.edu

Education
  • PhD (2015) Stevens Institute of Technology (Systems Engineering)
  • Other (1996) IMD (Executive MBA)
  • MS (1981) Universita' degli studi La Sapienza Roma - Italy (Mathematics)
General Information

Data Science professional teaching, researching and consulting on Machine Learning and Natural Language Processing.

Associate Professor at the Stevens Institute of Technology, teaching and researching on Natural Language Processing, Machine Learning, and Data Science.

Managing as Principal Investigator Government funded research projects using Data Analytics, Natural Language Processing and Machine Learning.

Developing an Engineering practice on decision taking using Data Science, Natural Language Processing and Machine Learning.

Experience

Worked for 25+ years in Industry as executive, consultant and entrepreneur.

Current focus on Machine Learning, Natural Language Processing, C4ISR, Predictive Analytics, Decision Support Systems, Business Intelligence.

Institutional Service
  • Intelliboard/Canvas metrics Member
  • Program Director Chair
  • Program Director Chair
  • WorkDay development group Member
Consulting Service

Worked in major consulting firms and my own for most of my career.

My consulting is now focused on Data Science, Machine Learning and Natural Language Processing.

Innovation and Entrepreneurship

As entrepreneur I launched 4 startups, primarily in consulting for product innovation.

Within a European IT conglomerate, I opened and closed companies and subsidiaries in EU, Brazil and US.

Professional Societies
  • IEEE Member
Grants, Contracts, and Funds

PI for WRT-1010: ~$4 million over 2 years (2018-2020) for DoD/Picatinny Arsenal.
The project leveraged on Natural Language Processing to develop 2 systems: a Risk evaluation interactive panel and a Technology monitoring system, both based on metrics extracted from vectorized text. The team was composed by about 25 great people.

PI for WRT-1023: ~$500k in 2020 for the Defense Acquisition University. The project - based on Natural Language Processing - was focused on creating a prototype system to classify purchase requests by contract type, using a computational version of the knowledge base of a contracting officer we created.

Co-PI for WRT-1018: ~$750k in 2020-'21 for the Defense Acquisition University. The project was focused on defining educational macro credentials in key areas. My responsibility is on Data Analytics and included 3 seminars for DAU audience.

Selected Publications
Conference Proceeding
  1. Borrelli, D.; Saremi, R.; Vallabhaneni, S.; Pugliese, A.; Shankar, R.; Martinez-Mejorado, D.; Iandoli, L.; Ramirez-Marquez, J.; Lipizzi, C. (2020). WINS: Web Interface for Network Science via Natural Language Distributed Representations. Communications in Computer and Information Science (vol. 1224 CCIS, pp. 614-621).
    https://api.elsevier.com/content/abstract/scopus_id/85088741668.
  2. Borrelli, D.; Saremi, R.; Vallabhaneni, S.; Pugliese, A.; Shankar, R.; Martinez-Mejorado, D.; Iandoli, L.; Ramirez-Marquez, J.; Lipizzi, C. (2020). WINS: Web Interface for Network Science via Natural Language Distributed Representations. Communications in Computer and Information Science (vol. 1224 CCIS, pp. 614-621).
    https://api.elsevier.com/content/abstract/scopus_id/85088741668.
  3. Babvey, P.; Lipizzi, C.; Ramirez-Marquez, J. (2019). Dissecting twitter discussion threads with topic-aware network visualization. Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019 (pp. 1359-1364).
    https://api.elsevier.com/content/abstract/scopus_id/85084741207.
  4. Babvey, P.; Lipizzi, C.; Ramirez-Marquez, J. (2019). Dissecting twitter discussion threads with topic-aware network visualization. Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019 (pp. 1359-1364).
    https://api.elsevier.com/content/abstract/scopus_id/85084741207.
  5. Desai, P.; Saremi, R.; Hoffenson, S.; Lipizzi, C. (2019). Agile and Affordable: A Survey of Supply Chain Management Methods in Long Lifecycle Products. Proceedings of the IEEE International Systems Conference.
  6. Primario, S.; Borrelli, D.; Zollo, G.; Iandoli, L.; Lipizzi, C. (2017). Measuring polarization in Twitter enabled in online political conversation: The case of 2016 US Presidential election. Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017 (vol. 2017-January, pp. 607-613).
    https://api.elsevier.com/content/abstract/scopus_id/85044191677.
  7. Lipizzi, C.; Dessavre, D. G.; Iandoli, L.; Marquez, J. E. (2016). Social media conversation monitoring: Visualize information contents of twitter messages using conversational metrics. Procedia Computer Science (vol. 80, pp. 2216-2220).
    https://api.elsevier.com/content/abstract/scopus_id/84978521835.
Erratum, Journal
  1. Borrelli, D.; Svartzman, G. G.; Lipizzi, C. (2021). Erratum: Unsupervised acquisition of idiomatic units of symbolic natural language: An n-gram frequency-based approach for the chunking of news articles and tweets (PLoS ONE (2020) 15: 6 (e0234214) DOI: 10.1371/journal.pone.0234214). PLoS ONE (1 January ed., vol. 16).
    https://api.elsevier.com/content/abstract/scopus_id/85099481777.
Journal Article
  1. Borrelli, D.; Svartzman, G. G.; Lipizzi, C. (2020). Unsupervised acquisition of idiomatic units of symbolic natural language: An n-gram frequency-based approach for the chunking of news articles and tweets. PLoS ONE (6 ed., vol. 15).
    https://api.elsevier.com/content/abstract/scopus_id/85086262125.
  2. Babvey, P.; Borrelli, D.; Lipizzi, C.; Ramirez-Marquez, J. (2020). Content-Aware Galaxies: Digital Fingerprints of Discussions on Social Media. IEEE Transactions on Computational Social Systems.
    https://api.elsevier.com/content/abstract/scopus_id/85092014412.
  3. Babvey, P.; Capela, F.; Cappa, C.; Lipizzi, C.; Petrowski, N.; Ramirez-Marquez, J. (2020). Using social media data for assessing children's exposure to violence during the COVID-19 pandemic. Child Abuse and Neglect.
    https://api.elsevier.com/content/abstract/scopus_id/85098158653.
  4. Borrelli, D.; Gongora, G.; Lipizzi, C. (2020). Unsupervised acquisition of idiomatic units of symbolic natural language: An n-gram frequency-based approach for the chunking of news articles and tweets. Dario Borrelli. San Francisco, CA: PLOS ONE.
    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234214.
  5. Garcia-Mancilla, J.; Ramirez-Marquez, J.; Lipizzi, C.; Vesonder, G.; Gonzalez, V. M. (2019). Characterizing negative sentiments in at-risk populations via crowd computing: a computational social science approach. International Journal of Data Science and Analytics (3 ed., vol. 7, pp. 165-177).
    https://api.elsevier.com/content/abstract/scopus_id/85088165791.
  6. Lipizzi, C.; Dessavre, D. G.; Iandoli, L.; Ramirez Marquez, J. E. (2016). Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations. Computers in Human Behavior (vol. 64, pp. 782-792).
    https://api.elsevier.com/content/abstract/scopus_id/84982811832.
  7. Lipizzi, C.; Iandoli, L.; Marquez, J. E. (2016). Combining structure, content and meaning in online social networks: The analysis of public's early reaction in social media to newly launched movies. Technological Forecasting and Social Change (vol. 109, pp. 35-49).
    https://api.elsevier.com/content/abstract/scopus_id/84970046151.
  8. Lipizzi, C.; Iandoli, L.; Ramirez Marquez, J. E. (2015). Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers' reactions to the launch of new products using Twitter streams. International Journal of Information Management (4 ed., vol. 35, pp. 490-503).
    https://api.elsevier.com/content/abstract/scopus_id/84929145299.
Courses

I'm teaching Data Science Courses at the School of Systems and Enterprises.

In particular, I created and taught EM 623 - Data Science; EM 624 - Data Exploration; EM 626 - AI and Machine Learning for Systems; ISE 225 - Data Engineering