Victor S. Bursztyn and Larry Birnbaum. 2019. Thousands of small, constant rallies: a large-scale analysis of partisan WhatsApp groups. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '19). Association for Computing Machinery, New York, NY, USA, 484–488. DOI:https://doi.org/10.1145/3341161.3342905
Victor S. Bursztyn, Marcelo G. Nunes, and Daniel R. Figueiredo. 2020. How Brazilian Congressmen Connect: Homophily and Cohesion in Voting and Donation Networks, Journal of Complex Networks, Volume 8, Issue 1, February 2020, cnaa006, https://doi.org/10.1093/comnet/cnaa006
Victor S. Bursztyn, Jennifer Healey, Eunyee Koh, Nedim Lipka, and Larry Birnbaum. 2021. Developing a Conversational Recommendation System for Navigating Limited Options. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Article 309, 1–6. DOI:https://doi.org/10.1145/3411763.3451596
Victor S. Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, and Larry Birnbaum. 2021. “It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1913–1918, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Victor S. Bursztyn, Jennifer Healey, and Vishwa Vinay. 2021. Gaudí: Conversational Interactions with Deep Representations to Generate Image Collections. In the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design.
Talk at UC San Francisco, Department of Testing, San Francisco, California
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk at London School of Testing, London, UK
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA
Victor S. Bursztyn, Jonas Furtado Dias, André de Almeida Maximo, Adriana Bechara Prado, and Rodrigo Dias Arruda Senra. 2019. Recommending Features for Content Planning Based on Advertiser Polling and Historical Audience Measurements. U.S. Patent No. 10,448,120. Washington, DC: U.S. Patent and Trademark Office.
Rodrigo Dias Arruda Senra, Karin Breitman, Adriana Bechara Prado, and Victor S. Bursztyn. 2021. Methods and Apparatus for a Semantic Multi-Database Data Lake. U.S. Patent No. 10,901,973. Washington, DC: U.S. Patent and Trademark Office.
Diego Salomone Bruno, Victor S. Bursztyn, Percy Enrique Rivera Salas, and Tiago Salviano Calmon. 2021. Relevance Decay for Time-based Evaluation of Machine Learning Applications. U.S. Patent No. 10,885,464. Washington, DC: U.S. Patent and Trademark Office.
Adriana Bechara Prado, Victor S. Bursztyn, Jonas Furtado Dias, André de Almeida Maximo, and Angelo Ernani Maia Ciarlini. 2021. Method, Medium, and System for Recommending Compositions of Product Features Using Regression Trees. U.S. Patent No. 11,030,667. Washington, DC: U.S. Patent and Trademark Office.