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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Thousands of Small, Constant Rallies: A Large-Scale Analysis of Partisan WhatsApp Groups
Published in Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2020
Acceptance rate of 15%.
Recommended citation: 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 https://dl.acm.org/doi/abs/10.1145/3341161.3342905
How Brazilian Congressmen Connect: Homophily and Cohesion in Voting and Donation Networks
Published in Journal of Complex Networks (Oxford University Press), 2020
Recommended citation: 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 https://academic.oup.com/comnet/article-abstract/8/1/cnaa006/5770925
Developing a Conversational Recommendation System for Navigating Limited Options
Published in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 2021
Acceptance rate of 39%. Presentation publicly available at: https://www.youtube.com/watch?v=97NOZQPrQr4
Recommended citation: 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 https://dl.acm.org/doi/abs/10.1145/3411763.3451596
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems.
Published in Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2021
Acceptance rate of 17.9%. Paper also invited for oral presentation.
Recommended citation: 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. https://aclanthology.org/2021.emnlp-main.145/
Gaudí: Conversational Interactions with Deep Representations to Generate Image Collections.
Published in NeurIPS Workshop on Machine Learning for Creativity and Design, 2021
A more comprehensive version of the methods in this paper has been submitted as a patent application.
Recommended citation: 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. https://neuripscreativityworkshop.github.io/2021/accepted/ncw_104.pdf
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
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., 2019
Patent applied while at Dell EMC’s Brazil R&D Center.
Recommended citation: 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. https://patents.google.com/patent/US10448120B1/en
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., 2021
Patent applied while at Dell EMC’s Brazil R&D Center.
Recommended citation: 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. https://patents.google.com/patent/US10901973B1/en
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., 2021
Patent applied while at Dell EMC’s Brazil R&D Center.
Recommended citation: 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. https://patents.google.com/patent/US10885464B1/en
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., 2021
Patent applied while at Dell EMC’s Brazil R&D Center.
Recommended citation: 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. https://patents.google.com/patent/US11030667B1/en