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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
Solving Complex Problems with Mathematical Engineering
Solving complex problems with data-driven approaches
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Posts
Deep Learning for Network Analysis
Published:
Deep learning has transformed many fields by enabling machines to automatically learn data representations. In network analysis, deep learning plays a pivotal role in representation learning, facilitating the extraction of meaningful features from graphs for tasks such as classification, clustering, and link prediction.
ODE Applications: Deep Learning
Published:
Ordinary Differential Equations (ODEs) play a critical role in deep learning, providing a mathematical framework for modeling continuous transformations in neural networks. Below, we explore seven key examples where ODEs intersect with deep learning, accompanied by real-world applications and references.
Introduction to Social Networks Analysis
Published:
A network is a collection of objects, known as nodes (or vertices), connected by relationships called edges (or links). The study of networks, also referred to as graph theory, has applications in multiple disciplines, including sociology, biology, computer science, and economics.
P&S: Experiments
Published:
In this post, we explore the a practical example of simulating a coin toss experiment with a Linear Congruential Generator (LCG), a well-known random number generator (RNG). We’ll illustrate how the probabilities of heads and tails converge to 0.5 as the number of tosses increases, validating the frequency approach to probability.
portfolio
DeepCausal
DeepCausal is a framework that applies deep learning techniques to uncover causal relationships in large, complex datasets. It combines the rigor of causal inference with the flexibility of neural networks, aiming for more accurate and interpretable results in causal discovery.
EMPHASIS
EMPHASIS is a comprehensive framework designed to model and simulate evolutionary processes. It offers robust statistical inference capabilities for phylogenetic studies, helping researchers understand species diversification.
EPIMOS
EPIMOS (Environmental Prediction and Imputation Modular Statistical System) is a comprehensive framework designed to predict and impute missing environmental variables using a modular statistical approach.
publications
Fieldwork to Sample Microsnails for Diet and Microbiome Studies along the Kinabatangan River, Sabah, Malaysian Borneo
Published in Malacologist, 2019
This study documents fieldwork to sample microsnails for diet and microbiome analysis in the Kinabatangan River region of Malaysian Borneo.
Recommended citation: Hendriks, K.P., Bisschop, K., Kavanagh, J.C., Kortenbosch, H.H., Larue, A.E.A., ... (2019). "Fieldwork to Sample Microsnails for Diet and Microbiome Studies along the Kinabatangan River, Sabah, Malaysian Borneo." Malacologist, 72, 33-38. https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.3237
Introducing a General Class of Species Diversification Models for Phylogenetic Trees
Published in Statistica Neerlandica, 2020
This paper introduces a general class of species diversification models for phylogenetic trees and provides an EM framework for maximum likelihood estimation.
Recommended citation: Richter, F., Haegeman, B., Etienne, R.S., & Wit, E.C. (2020). "Introducing a General Class of Species Diversification Models for Phylogenetic Trees." Statistica Neerlandica. 74(3), 261-274. https://onlinelibrary.wiley.com/doi/pdf/10.1111/stan.12205
Microbiome and Environment Explain the Absence of Correlations Between Consumers and Their Diet in Bornean Microsnails
Published in Ecology, 2021
This paper explores the role of microbiome and environment in the absence of correlations between land snails and their diet in Bornean microsnail communities.
Recommended citation: Hendriks, K.P., Bisschop, K., Kortenbosch, H.H., Kavanagh, J.C., Larue, A.E., Chee-Chean, P., & Etienne, R.S. (2021). "Microbiome and Environment Explain the Absence of Correlations Between Consumers and Their Diet in Bornean Microsnails." Ecology, 102(2), e03237. https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.3237
Plant Diets of Land Snail Community Members are Similar in Composition but Differ in Richness
Published in Journal of Molluscan Studies, 2021
This study investigates the plant diets of land snail communities, showing similar compositions but differences in species richness.
Recommended citation: Hendriks, K.P., Bisschop, K., Kavanagh, J.C., Kortenbosch, H.H., Larue, A.E.A., ... (2021). "Plant Diets of Land Snail Community Members are Similar in Composition but Differ in Richness." Journal of Molluscan Studies, 87(4), eyab041. https://academic.oup.com/mollus/article/87/4/eyab041/6484603?login=false
Detecting Phylodiversity-Dependent Diversification with a General Phylogenetic Inference Framework
Published in bioRxiv, 2021
This paper introduces a novel method for detecting phylodiversity-dependent diversification using a Monte Carlo Expectation-Maximization framework applied to phylogenetic trees.
Recommended citation: Richter, F., Janzen, T., Hildenbrandt, H., Wit, E.C., & Etienne, R.S. (2021). "Detecting Phylodiversity-Dependent Diversification with a General Phylogenetic Inference Framework." bioRxiv. https://www.biorxiv.org/content/biorxiv/early/2021/07/04/2021.07.01.450729.full.pdf
Random Graphical Model of Microbiome Interactions in Related Environments
Published in Journal of Agricultural, Biological, and Environmental Statistics, 2024
This paper develops a random graphical model for inferring microbiome interactions across different body sites and introduces a Bayesian approach for joint inference of microbiota systems.
Recommended citation: Vinciotti, V., Wit, E.C., & Richter, F. (2024). "Random Graphical Model of Microbiome Interactions in Related Environments." Journal of Agricultural, Biological, and Environmental Statistics. https://link.springer.com/article/10.1007/s13253-024-00638-6
Stereo 3D Cloud Motion from Tandem Satellites
Published in Mathematics in Industry Reports, 2024
This paper introduces a method for estimating cloud motion using stereo images from tandem satellites, providing insights into atmospheric dynamics.
Recommended citation: Zhou, Y., Abrahams, S., Mendoza, F., Donà, M., & Verbiest, R. (2024). "Stereo 3D Cloud Motion from Tandem Satellites." Mathematics in Industry Reports. https://www.cambridge.org/engage/miir/article-details/666c7ef65101a2ffa89a6c21
teaching
Supervision
Supervision, Università della Svizzera italiana and Politecnico di Milano, 2023
Supervision bridges teaching and research, fostering student growth while advancing academic inquiry. Through mentoring theses, I aim to cultivate critical thinking and contribute to cutting-edge research.
Probability & Statistics
2023-fall, Bachelor of Science in Informatics, Lecture, 2nd year. Università della Svizzera italiana, Faculty of Informatics, 2023
This course provides a comprehensive foundation in probability and statistics, essential for understanding randomness and applying statistical models to real-world phenomena.
Analysis of Social Networks
2024-fall, Master of Science in Computational Science, Lecture, Elective, 2nd year. Università della Svizzera italiana, Faculty of Informatics, 2024
Social network analysis reveals how connections between individuals and groups influence their actions. This course covers fundamental principles, statistical methodologies, and practical applications in the field.
Introduction to Ordinary Differential Equations
2024-fall, Master of Science in Artificial Intelligence and Computational Science, Lecture, Elective, 1st and 2nd year. Università della Svizzera italiana, Faculty of Informatics, 2024
This course provides a comprehensive foundation in ordinary differential equations (ODEs), emphasizing their importance in modeling real-world systems. Students will gain both theoretical insights and practical skills through analytical methods, numerical techniques, and real-world applications.
Stochastic Methods
2024-spring, Università della Svizzera italiana, Faculty of Informatics, 2024
This course covers foundational and advanced topics in stochastic processes, with a focus on both theory and practical applications. Students learn to model randomness in systems and analyze probabilistic phenomena through various methods.
Probability & Statistics
2024-fall, Bachelor of Science in Informatics, Lecture, 2nd year. Università della Svizzera italiana, Faculty of Informatics, 2024
This course provides a comprehensive foundation in probability and statistics, essential for understanding randomness and applying statistical models to real-world phenomena. The course integrates theoretical concepts, practical applications, and computational techniques.
Stochastic Methods
2025-spring, Università della Svizzera italiana, Faculty of Informatics, 2025
This course covers foundational and advanced topics in stochastic processes, focusing on both theoretical principles and practical applications. Students learn to model randomness in systems, analyze probabilistic phenomena, and apply stochastic methods in various domains, including optimization, inference, networks, and simulation.