基本信息
views: 0

Bio
My research interests focus on the following key areas:
Representations for counterfactual reasoning and planning: I aim to develop representations that provide reliable guarantees for counterfactual reasoning, particularly in offline settings. This research intersects with causal inference, offline reinforcement learning, and world models.
Representations in structured graph-like domains: My work involves learning representations in structured domains such as graph-based and temporal data. In addition to graph neural networks, I have adopted the topological deep learning paradigm, which allows for capturing higher-order interactions in the data.
Self-supervised learning: I am developing learning methods from unlabeled or partially labeled data across multiple modalities and tasks. My goal is to create foundation models that can be easily fine-tuned for counterfactual inference and sequential decision-making tasks, are robust to distributional shifts, and are adaptable to new features or variables. I am particularly focused on working with tabular and graph-structured data for which the current image-based self-supervised learning methods are not directly applicable.
Common-sense and external knowledge: I have been fascinated by AI systems that can leverage external common-sense reasoning to overcome tabula rasa learning. This is now possible more than ever with the advent of LLMs and foundation models. I seek to combine LLMs and other foundation models with learning embeddings of features to improve the generalization of models to new tasks and domains.
I am recognized as a principal investigator in federal grants by the National Science Foundation and the National Institutes of Health due to its implications on public health and climate change.
Representations for counterfactual reasoning and planning: I aim to develop representations that provide reliable guarantees for counterfactual reasoning, particularly in offline settings. This research intersects with causal inference, offline reinforcement learning, and world models.
Representations in structured graph-like domains: My work involves learning representations in structured domains such as graph-based and temporal data. In addition to graph neural networks, I have adopted the topological deep learning paradigm, which allows for capturing higher-order interactions in the data.
Self-supervised learning: I am developing learning methods from unlabeled or partially labeled data across multiple modalities and tasks. My goal is to create foundation models that can be easily fine-tuned for counterfactual inference and sequential decision-making tasks, are robust to distributional shifts, and are adaptable to new features or variables. I am particularly focused on working with tabular and graph-structured data for which the current image-based self-supervised learning methods are not directly applicable.
Common-sense and external knowledge: I have been fascinated by AI systems that can leverage external common-sense reasoning to overcome tabula rasa learning. This is now possible more than ever with the advent of LLMs and foundation models. I seek to combine LLMs and other foundation models with learning embeddings of features to improve the generalization of models to new tasks and domains.
I am recognized as a principal investigator in federal grants by the National Science Foundation and the National Institutes of Health due to its implications on public health and climate change.
Research Interests
Papers共 35 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Journal of the American Statistical Associationpp.1-2, (2024)
Christopher Tosh,Mauricio Tec, Jessica White, Jeffrey F. Quinn, Glorymar Ibanez Sanchez, Paul Calder, Andrew L. Kung, Filemon S. Dela Cruz, Wesley Tansey
Cancer Researchno. 6_Supplement (2024): 901-901
arxiv(2024)
Cited0Views0Bibtex
0
0
KDD 2024pp.2876-2887, (2024)
Claudio Battiloro, Ege Karaismailoğlu,Mauricio Tec,George Dasoulas,Michelle Audirac,Francesca Dominici
CoRR (2024)
Cited0Views0EIBibtex
0
0
Mauricio Tec,Ana Trisovic,Michelle Audirac, Sophie Mirabai Woodward, Jie Kate Hu,Naeem Khoshnevis,Francesca Dominici
Guillermo Bernárdez,Lev Telyatnikov, Marco Montagna, Federica Baccini,Mathilde Papillon,Miquel Ferriol-Galmés,Mustafa Hajij,Theodore Papamarkou,Maria Sofia Bucarelli,Olga Zaghen,Johan Mathe,Audun Myers,Scott Mahan, Hansen Lillemark,Sharvaree Vadgama,Erik Bekkers,Tim Doster,Tegan Emerson,Henry Kvinge, Katrina Agate,Nesreen K Ahmed, Pengfei Bai,Michael Banf,Claudio Battiloro,Maxim Beketov,Paul Bogdan, Martin Carrasco, Andrea Cavallo,Yun Young Choi,George Dasoulas, Matouš Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia,Hongwei Jin, Graham Johnson,Nikos Kanakaris,Boshko Koloski, Veljko Kovač, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long,German Magai, Alvaro Martinez,Marissa Masden,Sebastian Mežnar, Bertran Miquel-Oliver,Alexis Molina,Alexander Nikitin,Marco Nurisso,Matt Piekenbrock,Yu Qin,Patryk Rygiel,Alessandro Salatiello, Max Schattauer, Pavel Snopov,Julian Suk, Valentina Sánchez,Mauricio Tec,Francesco Vaccarino, Jonas Verhellen, Frederic Wantiez, Alexander Weers, Patrik Zajec,Blaž Škrlj,Nina Miolane
CoRR (2024)
Cited0Views0EIBibtex
0
0
Cancer Researchno. 6_Supplement (2024): 901-901
CoRR (2023)
Load More
Author Statistics
#Papers: 33
#Citation: 679
H-Index: 9
G-Index: 14
Sociability: 6
Diversity: 1
Activity: 5
Co-Author
Co-Institution
D-Core
- 合作者
- 学生
- 导师
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn