ABOUT ME

I am Senior Research Scientist at the ISI Foundation of Turin, Italy and Assistant Professor at the Copenhagen Center for Social Data Science (SODAS) of the University of Copenhagen. Here, I previously worked as a postdoctoral researcher, working in collaboration with the department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU).

My research interests stay at the intersection of computational social science and data science, with focus on studying human behaviors through digital traces. My current research aims at investigating the use of digital technologies and smartphones to understand their effects on human behaviors.

I hold a PhD in Applied Mathematics from the Polytechnic University of Turin, where I worked in collaboration with the Data Science group at the ISI Foundation of Turin. Here, I focused on the development of tensor factorization techniques and their applications to high-dimensional data of human proximity.

Professional Experience

  • Postdoc Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, DK. 1/4/2020 – 31/3/2023
  • Postdoc Dpt. of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, DK. 1/3/2019 – 29/2/2020
  • Postdoc Machine Intelligence and Data Science (MINDS), University of Southern California – Information Sciences Institute, Marina del Rey, CA, USA. 1/4/2017 – 15/2/2019
  • Research Assistant Machine Intelligence and Data Science, University of Southern California – Information Sciences Institute, Marina del Rey, CA, USA. 1/1/2017 – 31/3/2017
  • Intern System Biology Group, IRCC Candiolo Cancer Institute, Candiolo (TO), ITA.1/10/2012 – 31/3/2013

Education

  • PhD, Applied Mathematics Polytechnic University of Turin, thesis and research funded by the ISI Foundation, Turin. 1/1/2014 – 30/3/2017
  • M.Sc., Mathematical Modeling and engineering. Polytechnic University of Turin, thesis in collaboration with the Molecular Biotechnology – Center of Turin. 1/9/2011 – 10/10/2013
  • B.Sc., Mathematical Modeling and engineering. Polytechnic University of Turin, thesis in collaboration with the Mauriziano Umberto I , Hospital of Turin. 1/10/2008 – 13/12/2011

Project details and efforts

During my Postdoc I put my effort into the following projects and research topics at USC/ISI

DARPA YFA MITIGATE (PI Emilio Ferrara): I collected data and applied Non-negative tensor factorization techniques and other machine learning methods to investigate several aspects of individual performance and behaviour in Multiplayer Online Battle Arena (MOBA) games. I also developed a recommending system for teammates in such games, based on deep neural network techniques, with the aim of studying users’ collaborative behaviors.

IARPA CAUSE (PI Kristina Lerman): I analyzed the time series related to reported cyber-attacks to study possible correlations between different events and their power in predicting new threats. I also contributed in the development and test of the DISCOVER algorithm for predicting new cyber threats form social media and online discussions.

DARPA COSINE (PI Emilio Ferrara): I worked in the data collection and analysis of GitHub, to extract meaningful characteristics and statistics about GitHub users and repositories. I also worked on the embedding through Graph Factorization of the system bipartite network of users and repos to inform DASH, an agent- based simulator used to predict new events in the platform.

GEO-REFERENCED POSTS: I worked on geo-referenced posts shared by Twitter users in relation to the US census tracts information of the posts’ locations with the aim of studying the mobility-induced segregation in the LA metropolitan area (work under the supervision of Kristina Lerman).

European Research Council DISTRACT (PI Morten Axel Pedersen): The project brings together social science and data science methods to explore the political economy of distraction in the post-digital age. As part of the project I collected large-scale GitHub data and used network analysis, including the extraction of the main structures and components of the network, to study the GitHub dependency ecosystem.

Understanding Large-Scale Smartphone Usage: The project aims at understanding engagement in smartphone use and its effects on online and offline users’ behavior.

Here, I studied high-dimensional longitudinal smartphone data, including mobile app information via causal and deep learning methods to model multi-dimensional time-series of mobile app use.

Publications

Sapienza, A., & Lehmann, S. A view from data science. Big Data & Society, 8(2), 20539517211040198 (2021) –
Scopus: 87th percentile “Computer Science Applications”

Ozer, M., Sapienza, A., Abeliuk, A., Muric, G., Ferrara, E. Discovering patterns of online popularity from time-series. Expert Systems with Applications, 113337 (2020) – Scopus: 95th percentile “Computer Science Applications”

Matsui, A., Sapienza, A., & Ferrara, E. Does streaming esports affect players’ behavior and performance? Games and Culture, 15(1), 9-31 (2020) – Scopus: 58th percentile “Computer Science Human-Computer Interaction”

Sapienza, A., Goyal, P., Ferrara, E. Deep neural networks for optimal team composition. Frontiers in Big Data, 2.14 (2019) – Scopus: 58th percentile “Computer Science”

Zeng, Y., Sapienza, A., Ferrara, E. The Influence of Social Ties on Performance in Team-based Online Games. IEEE Transactions on Games (2019) – Scopus: 68th percentile “Computer Science: Artificial Intelligence”

Ozella, L., Gauvin, L., Carenzo, L., & …, D., Sapienza, A., Kalimeri K., Della Corte, F., Cattuto,C. Wearable proximity sensors for monitoring a mass casualty incident exercise: feasibility study. Journal of medical internet research 21.4: e12251 (2019) – Scopus: 88th percentile “Health Informatics”

Sapienza, A., Barrat, A., Cattuto, C. & Gauvin, L. Estimating the outcome of spreading processes on networks with incomplete information : a dimensionality reduction approach. Physical Review E 98.1 : 01237 (2018)
– Scopus: 88th percentile “Statistics and Probability”

Kobayashi, T., Sapienza, A. & Ferrara, E. Extracting the multi-timescale activity patterns of online financial markets. Scientific Reports (2018) – Scopus: 91th percentile “Multidisciplinary”

Sapienza, A., Zeng, Y., Bessi, A., Lerman, K. & Ferrara, E. Individual performance in team-based online games Royal Society Open Science, 5.6 : 180329. (2018) – Scopus: 89th percentile “Multidisciplinary”

Sapienza, A., Bessi, A. & Ferrara, E. Nonnegative tensor factorization for human behavioral pattern mining in online games. Information 9.3 : 66 (2018) – Scimago: 62th percentile “Computer Science: Information Systems”

Campa, C. C., Germena, G., Ciraolo, E., Copperi, F., Sapienza, A., Franco, I., … & Perino, A. Rac signal adaptation controls neutrophil mobilization from the bone marrow. Sci. Signal., 9(459), ra124-ra124 (2016) – Scopus: 93th percentile “Biochemistry, Genetics and Molecular Biology”

Conference Proceedings (peer-reviewed)

Wang, Z., Sapienza, A., Culotta, A., Ferrara, E. Personality and behavior in role-based online games. Proceedings of the IEEE Conference on Games (2019) – CORE Rating: C

Blythe, J., Bollenbacher, J., Huang, D., Hui, P. M., Krohn, R., Pacheco, D., Goran, M., Sapienza, A., … & Flammini, A. Massive Multi-agent Data-Driven Simulations of the GitHub Ecosystem. International Conference on Practical Applications of Agents and Multi-Agent Systems pp. 3-15, Springer, Cham (2019) – CORE rating: B

Blythe, J., Ferrara, E., Huang, D., Lerman, K., Muric, G., Sapienza, A., … & Hui, P. M. The DARPA SocialSim Challenge: Massive Multi-Agent Simulations of the Github Ecosystem. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (2019)  – GGS Rating: A

Sapienza, A., Goyal, P. & Ferrara, E. Recommending teammates with deep neural networks. Proceedings of the 29th ACM conference on Hypertext and Social Media. pp. 57-61 (2018) – GGS rating: B

Sapienza, A., Ernala, S. K., Bessi, A., Lerman, K. & Ferrara, E. DISCOVER : mining online chatter for emerging cyber threats. Companion of The Web Conference 2018 pp. 983-990 (2018) – GGS rating: A++

Sapienza, A., Bessi, A. & Ferrara, E. Early warnings of cyber threats in online discussions. Proceedings of ICDMW: Workshop of IEEE International Conference on Data Mining. pp. 667-674 (2017) – GGS rating: A++

Sapienza, A., Peng, H. & Ferrara, E. Performance dynamics and success in online games. Proceedings of ICDMW: Workshop of IEEE International Conference on Data Mining. pp. 902-909 (2017) – GGS rating: A++

Sapienza, A., Panisson, A., Wu, J., Gauvin, L., & Cattuto, C. Detecting anomalies in time-varying networks using tensor decomposition. Proceedings of IEEE ICDMW: International workshop on data mining in networks (2015) – GGS rating: A++

Sapienza, A., Panisson, A., Wu, J. T. K., Gauvin, L., & Cattuto, C. Anomaly detection in temporal graph data: An iterative tensor decomposition and masking approach. In International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD (2015).

Talks and posters

Apr 27, 2018: Talk at TheWebConf18 – DISCOVER : mining online chatter for emerging cyber threats.

Nov 18-21, 2017: Talk at “ICDMW: Workshop of the IEEE International Conference on Data Mining”. Title:
“Performance Dynamics and Success in Online Games”.

Nov 18-21, 2017: Talk at “ICDMW: Workshop of the IEEE International Conference on Data Mining”. Title: “Early
Warnings of Cyber Threats in Online Discussions”.

Sep 18-22, 2017: Talk at “CCS’17: Conference on Complex Systems”, Cancun, Mexico. Title: “A tensor decomposition-based method to estimate the spreading process outcomes for temporal networks with incomplete information”.

Sep 18-22, 2017: Talk at “CCS’17: Conference on Complex Systems”, Cancun, Mexico. Title: “Human behavioural patterns in online games”.

Jun 19-23, 2017: Poster at “NetSci’17: International School and Conference on Network Science”, Indianapolis,
Indiana. Title: Tensor decomposition method for spreading processes on temporal networks with partial information.

Sep 20-22, 2016: Talk at “CCS’16: Conference on Complex Systems”, Amsterdam, The Netherlands.
Title: Generative model of temporal networks for studying dynamical processes.

Sep 19, 2016: Poster and ignite at “ECML PKDD 2016, PhD Forum”, Riva del Garda, Italy.
Title: Tensor decomposition techniques for temporal graph mining.

Jul 11-13, 2016: Talk at “Complex Networks: from theory to interdisciplinary applications”, Marseille, France.
Title: Joint factorization for analysing multi-dimensional data.

Nov 14-17, 2015: Talk at “IEEE ICDM 2015: International workshop on data mining in networks”, Atlantic City, NJ, USA. Title: Detecting anomalies in time-varying networks using tensor decomposition.

Poster at “ECML PKDD 2015, workshop on advanced analytics and learning on temporal data”, Porto, Portugal. Title: anomaly detection in temporal graph data: an iterative tensor decomposition and masking approach.

Invited Talks

Understanding Large-scale Smartphone Use
University of Eastern Piedmont, DISIT 20/10/2022

Understanding Human Behavior and Engagement in Smartphone Usage
Polytechnic of Milan, DEIB 18/10/2022 

Escape from Covid Island
HOPE Seminar, Aarhus University, 24/11/2021.

Understanding Large-scale Smartphone Usage
CUDAN Open Lab seminars, 1/2/2021.

Understanding Large-scale Smartphone Usage
DISTRACT Seminar, University of Copenhagen, 11/12/2020

Escape from Covid Island
SODAS Data Discussion, University of Copenhagen, 19/6/2020

How Millions of Individuals Use the Apps on their Smartphones
Machine Anthropology Workshop, University of Copenhagen, 27-28/1/2020.

Understanding Human Behavior and Engagement in Mobile App Usage
IT University of Copenhagen, 28/11/2019.

Mining Human Behaviors and Performance in Online Platforms
ISI Foundation of Turin, 20/2/2019.

Urban-Rural divide in smartphone usage
CPH Tech Policy Committee, Copenhagen, Denmark, 08/12/2022

Understanding Large-scale Smartphone Use
Fondazione ISI di Torino, 10/11/2022

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Programming Languages

Python, Matlab & Simulink, C
Strong experience with SciPy, NumPy, Scikit-learn, Pandas, Scikit tensor, Ipython/Jupyter, PySpark

Certificates

Coursera: ‘Neural Networks and Deep Learning’, ‘Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization’, ‘Structuring Machine Learning Projects’.

Research interests

Tensor decomposition, computational social science, human behaviors, complex networks, time-varying networks,
data science, machine learning, data collection and analysis, social network dynamics, data for social good.

Organization of scientific events:

SODAS PhD Reading Club, University of Copenhagen, Spring 2022

ERC DISTRACT Seminars, University of Copenhagen, Spring 2022

SODAS Postdoc Salon, University of Copenhagen, 19/8/2021

SODAS Panel Discussion Series on Fair Science, University of Copenhagen, Spring 2021

Copenhagen Data Beer, 2nd edition 23/9/2019

Copenhagen Data Beer, 1st edition 23/5/2019

Summer School on Computational Social Science 30/7/2019 – 4/8/2019

ICDMW ACUMEN: Data science for human performance in social networks. 18/11/2017

Schools and lectures (Attended):

May 4 th , 2018: SDM’18 Tutorial: “The Canonical Polyadic Tensor Decomposition”. San Diego, CA, USA.

May 11-21, 2016: MLSS’16 “The machine learning summer school”, Càdiz, Spain.

Feb 2-4, 2015: Winter school: “Search for latent variables: ICA, tensors and NMF”, Villard de Lans, Grenoble, France.

Sep 19-21, 2014: Summer school on: “An interdisciplinary approach to tensor decomposition”, CIRM, Trento, Italy.

Apr 7-18, 2014: Les Houches Thematic school on: “Structure and Dynamics of Complex Networks”, École de physique des Houches, France.

Jan 2014 – Mar 2014: “Data Mining, Statistical Modelling and Machine Learning” course, University of Turin.
Theoretical part: machine learning algorithms, supervised/unsupervised methods, validation techniques. Practical part: Ipython notebook classes, Kaggle competition.

Other

Co-supervised the following students:

USC/ISI Summer interns: Mert Ozer (2018, PhD student at Arizona State University), Zhao Wang (2018, PhD student at Illinois Institute of Technology), Hao Peng (2017, PhD student at Indiana University), Sindhu K. Ernala (2017, PhD student at Georgia Tech University), Susan Fennell (2017, PhD at University College Cork).

USC Viterbi CS PhD students: Palash Goyal, Yilei Zeng, Akira Matsui, Di Huang.

Intervention for educational guidance at the scientific high school E. Majorana, Moncalieri, To, Italy, 2015.

CONTACT

Address:
Copenhagen Center for Social Data Science (SODAS)

Øster Farimagsgade 5A, Building 1, 2nd floor
1353 Copenhagen K

E-mail:
ansa@sodas.ku.dk

Address:

ISI Foundation,
Via Chisola 5 10126 Torino – Italy

E-mail:
anna.sapienza@isi.it