Gabitto

Mariano Gabitto

Mariano Gabitto is an Assistant Investigator at the Allen Institute for Brain Science, where he studies how the cellular diversity observed in the brain is generated. He leads the analysis efforts for the Seattle Alzheimer's Disease Cell Brain Atlas. Previously, Mariano was a researcher at the Center for Computational Biology at the Flatiron Institute, part of the Simons Foundation, and at the Broad Institute of Harvard and MIT. He was also a visiting researcher in Mike Jordan's group at the University of California, Berkeley, where he developed nonparametric Bayesian methods for super-resolution imaging and deep generative models to represent single-cell information. Mariano earned his Ph.D. in Neuroscience at Columbia University in Charles Zuker's lab, collaborating with the groups of Liam Paninski and Larry Abbott.

Neder

Catherine O'Neil

Catherine O'Neil is a mathematician, data scientist, and author renowned for her work on algorithm ethics and the societal impact of data. She earned a Ph.D. in Mathematics from Harvard University and worked as a Mathematics professor at MIT and Barnard College. Later, she ventured into the financial industry, working in hedge funds, where she became interested in the impact of mathematical models on the economy. O'Neil is the author of the book "Weapons of Math Destruction", which explores how algorithms can perpetuate inequality and cause harm. She also founded ORCAA (O'Neil Risk Consulting and Algorithmic Auditing), a consulting firm dedicated to auditing algorithms and ensuring their ethical use. She has written extensively on topics related to fairness, transparency, and the regulation of automated systems.

Leila

Gavino Puggioni

Gavino Puggioni is an Associate Professor and Head of the Statistics Section in the Department of Computer Science and Statistics at the University of Rhode Island, where he also holds a joint appointment with the College of the Environment and Life Sciences. Born and raised in Italy, he completed his undergraduate and master’s studies in Economics at Bocconi University. He later earned a master’s degree and a Ph.D. in Statistics from Duke University in 2008. After completing his Ph.D., he pursued postdoctoral studies at the University of North Carolina at Chapel Hill and Emory University. His primary research areas include the development and application of Bayesian methods, with a particular focus on the analysis of dependent data such as time series, spatial, and spatio-temporal data, stochastic differential equations, model mixtures, and model averaging.

terHorst

Valérie Gauthier-Umaña

Valérie Gauthier-Umaña is an Assistant Professor in the Department of Systems and Computing Engineering at the Universidad de los Andes, Colombia. She earned her degree in Mathematics from the same university. She later completed a master's degree in Algebra, Geometry, and Number Theory at the University of Bordeaux I, France, and the Università degli Studi di Padova, Italy. She obtained her Ph.D. in Applied Mathematics, focusing on post-quantum cryptography, at the Technical University of Denmark (DTU). Her research areas include cryptography and coding theory. She has contributed to academic publications, including co-editing the proceedings of the 17th International Conference on Applied Cryptography and Network Security (ACNS 2019) in Bogotá, Colombia. Additionally, she has collaborated on research regarding attacks on cryptographic systems based on Reed-Solomon and McEliece codes.

Bekios

Jeremias Sulam

Jeremias Sulam is an Assistant Professor in the Department of Biomedical Engineering at Johns Hopkins University and a core member of the Mathematical Institute for Data Science (MINDS) and the Data Science and AI Institute at the same university. He earned his degree in Bioengineering from the National University of Entre Ríos, Argentina, in 2013, and completed his Ph.D. in Computer Science at the Technion - Israel Institute of Technology in 2018. Jeremias has received the Best Graduates Award from the National Academy of Engineering of Argentina and the Early CAREER Award from the National Science Foundation (NSF) in the United States. His research interests include signal and image processing, sparse representation modeling, inverse problems, and machine learning.

Alberto

Alberto Cabezas Gonzalez

Alberto Cabezas is a postdoctoral researcher at the University of Turin, where he models time sequences extracted from administrative data, such as employment biographies or healthcare system visits, using transformer models. He holds a Ph.D. in Statistics from Lancaster University, where he worked at the intersection of Monte Carlo methods and Machine Learning. Prior to that, he earned a master's degree in Economics and Social Sciences from Bocconi University, specializing in nonparametric Bayesian models. He has developed software for probabilistic artificial intelligence projects, collaborating with academic institutions and tech startups. He is the first author of papers published in Stat, NeurIPS, and AISTATS, exploring topics such as normalizing flows and Markov chain Monte Carlo.

Vitoria

Vitória Barin Pacela

Vitória Barin Pacela is a researcher in the field of machine learning and artificial intelligence. She grew up in Brazil and moved to Finland to pursue a bachelor's degree in Computer Science at the University of Helsinki, where she worked on machine learning for particle physics, spending several summers at CERN with the groups of Professor Maria Spiropulu and Dr. Maurizio Pierini. She later earned a master's degree in Data Science at the same university, supervised by Professor Aapo Hyvärinen and Dr. Antti Hyttinen, focusing on independent component analysis for binary data. Currently, Vitória is a Ph.D. student at the Mila lab at the University of Montreal, under the supervision of Professor Simon Lacoste-Julien. Her research areas include causality, deep learning, out-of-distribution generalization, probabilistic models, representation learning, and robustness.

Landfried

Gustavo Landfried

Gustavo Landfried is a professor at the School of Science and Technology at the National University of San Martín, where he teaches courses on Bayesian Causal Inference in the Data Science program. After earning a bachelor's degree in Social Anthropology, Gustavo completed a Ph.D. in Computer Science at the Faculty of Exact and Natural Sciences at the University of Buenos Aires, under the supervision of the directors of the Applied Artificial Intelligence Laboratory (Diego Slezak) and the Interdisciplinary High-Performance Computing Laboratory (Esteban Mocskos). Gustavo currently works in the industry as a Senior Data Scientist, developing causal inference projects for multinational companies. He is also the developer and maintainer of the state-of-the-art skill estimator in the video game industry (TrueSkill Through Time) in Python, Julia, and R. Additionally, he is a co-founder of Plurinational Bayes.

Matias

Matías Altamirano Moreno

Matías Altamirano is a Ph.D. student in Statistical Science at UCL and a member of the Fundamentals of Statistical Machine Learning research group, supervised by Jeremias Knoblauch and François-Xavier Briol. His research focuses on developing robust generalized Bayesian methods applied to time series models. Currently, his Ph.D. is supported by the Bloomberg Data Science PhD Fellowship. Before starting his doctoral studies, he worked as a research engineer at the Center for Mathematical Modeling at the University of Chile, where he applied data science and stochastic models to various interdisciplinary projects.

Gerardo

Gerardo Durán-Martin

Gerardo Durán-Martín is a Ph.D. candidate in Mathematical Sciences at Queen Mary University of London, under the supervision of Kevin Murphy and Alexander Shestopaloff, with a visiting student position at the Oxford-Man Institute of Quantitative Finance. His research focuses on developing learning algorithms for streaming data in dynamic environments, emphasizing filtering techniques as learning mechanisms in non-stationary and misspecified contexts. This includes applications such as online continuous learning and signal extraction in high-dimensional environments with low signal-to-noise ratios.