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Pages
About Me
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Posts
Augmented Probability Simulation for solving Sequential games.
Published:
I’ll talk at ADA 2022 in D.C. about augmented probability simulations in game theory. Slides available here.
Machine Learning for molecular design. A case study in dispersants desing.
Published:
Will talk at SEIO about optimal design of dispersants. Slides available here.
Adversarial Machine Learning. Bayesian Perspectives
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Will talk at Texas State University about some AML and Bayes. Slides available here.
Some ML applications in Online Marketing and Molecule Design
Published:
Will talk at Texas State University about some ML applications in online maerketing and molecule design. Slides available here.
Machine Learning for de novo molecular design
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Will talk about molecules at Matemáticas de la materia viva workshop. Slides available here. See you there!
AI in the discovery of intelligent materials
Published:
Tomorrow will be talking about Applications of Artificial Intelligence in the discovery of intelligent materials and in the improvement of processes in the chemical industry. In particular, I will focus on a recent project in which we leverage Bayesian techniques to search for molecules with pontentially high dispersancy efficacy.
You can register https://www.suschem-es.org/registro/IArtificial.asp.
Welcome!
Published:
Welcome to my personal site! I’m about to finish my PhD in the Institute of Mathematical Sciences. Contact me if you have any interesting (stats related) problem you want to solve.
portfolio
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publications
Large‐scale automated forecasting for network safety and security monitoring
Published in Applied Stochastic Models in Business and Industry, 2019
Download here
Adversarial Classification: An Adversarial Risk Analysis Approach
Published in International Journal of Approximate Reasoning, 2019
Download here
Reinforcement Learning under Threats
Published in AAAI 2019. Student Track, 2019
Download here
Gradient Methods for Solving Stackelberg Games
Published in Lecture Notes on Computer Science, 2019
Download here
Adversarial Risk Analysis: An overview
Published in WIREs Computational Statistics, 2020
Download here
Perspectives on Adversarial Classification
Published in Mathematics, 2020
Download here
talks
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teaching
Machine Learning with R
Course, National Statistics Institute, 2019
In 2019, the National Statistics Institute hired some colleagues and myself to teach a course in Practical Machine Learning with R. All the material in spanish (slides and practical excercises) can be found here!
Applications of Stochastic Processes
Undergraduate course, Complutense University of Madrid, Statistics and Operations Research Department, 2020
I taught Applications of Stochastic Processes to undergraduates on maths and economics during the spring semesters of years 2019 and 2020. All the material (in spanish) is available here!
A First Course on Probability
Undergraduate course, Complutense University of Madrid, Statistics and Operations Research Department, 2020
I taught a first coure on probability to undergraduates on maths and physics during 2020 winter semester.
Bayesian methods in Artificial Intelligence
Course, Institute of Mathematical Sciences (ICMAT-CSIC), 2021
Slides and labs of our recent course on Bayesian methods in Artificial Intelligence for the JAE school at ICMAT can be found here! Video recordings (in Spanish) are available at ICMAT YouTube Channel.
Machine Learning: Foundations and applications
Course, Institute of Mathematical Sciences (ICMAT-CSIC), 2021
Slides and labs of our recent course on Machine Learning for the JAE school at ICMAT can be found here! Video recordings (in Spanish) are available at ICMAT YouTube Channel.
Data Analysis
Undergraduate course, CUNEF University, 2023
I taught an introductory course on Data Science to undergraduates on economics and computer science during the fall semesters of years 2022 and 2023. All the material (in spanish) is available here!
Statistics
Undergraduate course, CUNEF University, 2023
I taught a Statistics course to undergraduates on mathematical engineering during the fall semester of years 2022.
Machine Learning for Molecular Design
Course, Repsol Technology Lab, 2023
Check here for an (almost) self-contained crash course on Machine Learning for Molecular Design.