Research

My current research interests roughly lie on the intersection of machine learning, algorithmic design, and AI applications in healthcare. More specifically, I am interested in (1) the design of learning algorithms that are able to construct explanations for their predictions in terms of high-level concepts and (2) the broad applications that said algorithms may have in scenarios where transparency is not an option (such as in healthcare). For some more details on the general direction of my research, please refer to my Gates Cambridge scholar profile.

Below you can find a list of some of my publications, including their respective venues, papers, code, and presentations (when applicable). For a possibly more up-to-date list, however, please take refer to my Google Scholar profile.


Conference Publications (Refereed and Archived)

Spotlight paper at the conference on Neural Information Processing Systems (NeurIPS), 2023

Mateo Espinosa Zarlenga, Katherine M. Collins, Krishnamurthy (DJ) Dvijotham, Adrian Weller, Zohreh Shams, Mateja Jamnik

IntCEM architecture
DCR visual abstract

International Conference On Machine Learning (ICML), 2023. Also appeared at ICML's Differentiable Almost Everything Workshop, 2023.

Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frederic Precioso, Mateja Jamnik, Giuseppe Marra

Metrics visual abstract

Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI) as an oral presentation, 2023

Mateo Espinosa Zarlenga*, Pietro Barbiero*, Zohreh Shams*, Dmitry Kazhdan, Umang Bhatt, Adrian Weller, Mateja Jamnik

Microservices

The 24th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019

Yu Gan, Yanqi Zhang, Dailun Cheng, Ankitha Shetty, Priyal Rathi, Nayan Katarki, Ariana Bruno, Justin Hu, Brian Ritchken, Brendon Jackson, Kelvin Hu, Meghna Pancholi, Yuan He, Brett Clancy, Chris Colen, Fukang Wen, Catherine Leung, Siyuan Wang, Leon Zaruvinsky, Mateo Espinosa Zarlenga, Rick Lin, Zhongling Liu, Jake Padilla, Christina Delimitrou.


Journal Publications (Refereed and Archived)

Transactions of Machine Learning Research (TMLR), 2023. Also appeared at ICML's Workshop on Interpretable Machine Learning in Healthcare, 2023.

Mateo Espinosa Zarlenga, Zohreh Shams, Michael Edward Nelson, Been Kim*, Mateja Jamnik*

TabCBM visual abstract
Microservices

IEEE Micro, 2020

Yu Gan, Yanqi Zhang, Dailun Cheng, Ankitha Shetty, Priyal Rathi, Nayan Katarki, Ariana Bruno, Justin Hu, Brian Ritchken, Brendon Jackson, Kelvin Hu, Meghna Pancholi, Yuan He, Brett Clancy, Chris Colen, Fukang Wen, Catherine Leung, Siyuan Wang, Leon Zaruvinsky, Mateo Espinosa Zarlenga, Rick Lin, Zhongling Liu, Jake Padilla, Christina Delimitrou.


Workshop Publications (Refereed)

ECLAIRE

1st NeurIPS Workshop on eXplainable AI approaches for debugging and diagnosis (XAI4Debugging@NeurIPS) as a spotlight presentation, 2021

Mateo Espinosa Zarlenga, Zohreh Shams, Mateja Jamnik.