Leiden ↔ Chile International Research Seminar on Computer Science and AI

My research sits at the intersection of automated machine learning (AutoML) and open science, with a focus on making AI systems more powerful, transparent, and trustworthy. I work on AutoML methods that take over labor-intensive tasks in the machine learning pipeline (such as hyperparameter optimisation and neural architecture search), freeing researchers and practitioners to focus on higher-level concerns like model fairness, interpretability, and reliability.

A core belief driving my work is that automation should empower humans in the machine learning loop, not replace their judgment. By reducing technical friction, AutoML lowers barriers to entry and enables more thoughtful, human-centred AI development.

I am a co-founder and maintainer of OpenML (openml.org), an open platform for sharing machine learning experiments, datasets, and workflows. OpenML promotes reproducibility and collaboration across the research community, ensuring that scientific progress in machine learning remains open and accessible to all.

This seminar offers researchers a comprehensive and practical introduction to Automated Machine Learning (AutoML) and the OpenML platform, equipping participants with the tools and knowledge to integrate these methods into their own research projects.

The seminar opens with a plenary introduction to AutoML, covering its motivation, core techniques, and real-world applicability. Key methods such as TabPFN will be discussed, illustrating how AutoML can streamline machine learning workflows across a wide range of research domains.

The second session shifts to hands-on practice, walking participants through concrete code examples and guiding them in applying AutoML tools to their own datasets and research questions.

The seminar concludes with an exploration of advanced topics, including ongoing research projects within our group and open discussion of directions for future work, encouraging participants to identify opportunities to extend and build upon existing methods.

Saber Salehkaleybar is an Assistant Professor at Leiden University. Prior to joining Leiden University, he was a research scientist at EPFL.

Salehkaleybar’s research focuses on causal inference, stochastic optimization, and reinforcement learning. His work in causal inference is centered on two main themes: causal inference under latent confounding and experiment design in complex systems. He has developed methods for causal discovery and causal effect estimation when hidden variables may affect the observed data, and for designing efficient experiments in complex networks. Several of these contributions have appeared as spotlight papers at NeurIPS.

More broadly, his research aims to make causal reasoning reliable in realistic settings where standard assumptions may fail, and connects causal inference with modern AI and decision-making systems.

Causal inference provides tools for answering questions that go beyond prediction: What would happen if we intervened? Which relationships are truly causal rather than merely correlational? And how can we make reliable decisions when randomized experiments are impossible, costly, or unethical?

The seminar will begin with an introduction to the foundations of causal inference, including causal graphs, structural causal models, interventions, counterfactuals, causal discovery, and causal effect estimation. As these concepts are introduced, we will work through illustrative examples of causal discovery and causal effect identification, explaining key challenges that arise in real-world settings, such as latent confounding, biased observational data, and identifiability.

The seminar will conclude by exploring opportunities for causal inference in modern AI, with a particular focus on reinforcement learning and generative AI. We will discuss how causal ideas can support hierarchical reinforcement learning, mechanistic interpretability in generative AI, and more robust AI systems, while connecting these topics to recent research on causal discovery and causal effect estimation.

Yingjie Fan is an Assistant Professor at LIACS who leads the group of Logistics Optimization. Her research interests include stochastic programming, multi-objective optimization, robust optimization, learning-based optimization, behavioral operations research and data-driven modeling in transportation science and supply chain risk management.

Yingjie Fan is representing the Natural Computing Cluster for visiting Chile. At the Natural Computing Cluster (NaCo), part of LIACS at Leiden University, we delve into the fascinating intersection of nature-inspired computation and real-world problem-solving. Our cluster integrates six special interest groups, each pushing the boundaries of Artificial Intelligence, Machine Learning, Optimization, and Data Science.

Our mission is to foster a deep understanding of natural computing and to develop innovative solutions that can address some of the world's most complex problems. By harnessing the power of nature-inspired computing, we aim to contribute to scientific advancement and create a positive impact on society. Research in natural computing is at the core of our activities, covering theoretical foundations, the development of new algorithms, and interdisciplinary applications of natural computing methods. Our cluster focuses on a wide array of topics, combining foundational research with practical applications. These include:

• Nature-Inspired Algorithms: foundational work in evolutionary computing, metaheuristics and related methodologies.
• Explainable AI (XAI): Advancing fair, interpretable, and transparent AI solutions tailored to diverse industries.
• Algorithm Benchmarking and Configuration: Rigorous evaluation and fine-tuning for optimal algorithmic performance.
• Generative AI and LLM-Aided Design: Exploring creative and automated approaches for designing algorithms.
• Quantum Computing: Tackling optimization challenges using cutting-edge quantum methodologies.
• Logistics and Sustainability: Machine learning-driven solutions for supply chain, vehicle routing, and green manufacturing.
• Multi-Objective Optimization: Efficient techniques for robust, multimodal, and scalable optimization challenges.

The driving force behind our research is the mission to increase our understanding of natural systems as models of computation, with a focus on the development of new algorithms and applications to challenging problems.

In this seminar, I will present my research topics and those of the NaCo cluster, covering a broad range of areas, including multi-objective optimization, evolutionary computation, Bayesian optimization, metaheuristics, learning-based optimization, hyperparameter optimization, automated algorithm design using LLMs, explainable AI, and decision-support applications in engineering, medicine, supply chains, logistics, and hydrogen transport and production.

Alfons Laarman is an associate professor at Leiden University, where he leads the theory cluster and is co-founder of the inter-departmental Applied Quantum Algorithms (aQa) initiative. Prior to joining Leiden, he was a postdoc at TU Vienna and completed his PhD at the University of Twente.

Laarman is known for parallel DFS-based graph algorithms and for knowledge representation and reasoning approaches to quantum computing. In general, his research spans reliable computing, parallel computing, and quantum computing, often bridging theory and practice. His research is funded by several national and European consortia as well as by personal grants from the Dutch Scientific Research Council.

The first part of this lecture comprises a mini-tutorial on quantum computing. This tutorial is aimed at anyone with a computer science background and an interest in quantum computing. Starting with classical circuits as a computational model, we gradually introduce reversible and probabilistic computing as natural extensions for which computer scientists already have a strong intuition. We then show that probabilistic behavior breaks the reversibility required for quantum computing. The solution is to switch to a norm that allows for “negative probabilities”. With only two simple gates, we can then gain a full understanding of universal quantum computing.

In the final part, we will discuss various knowledge representation and reasoning techniques for analyzing and optimizing quantum circuits. Using model counting (#SAT), we show how to classically simulate quantum circuits and verify their equivalence. We then introduce a solution for quantum circuit synthesis based on maximum model counting. If time permits, we will relate the succinctness and tractability properties of tensor networks and decision diagrams, two formalisms often used to represent quantum information.

Dr. Akrati Saxena is an Assistant Professor at the LIACS, Leiden University, and an Adjunct Professor at the University of Victoria, Canada. Her research lies at the intersection of social network analysis, complex networks, computational social science, data science, and algorithmic fairness. She established and leads the AlFa (Algorithmic Fairness, https://alfa.liacs.nl/) research group, which focuses on understanding structural inequalities in complex networks and advancing fairness-aware algorithms in network and data science, including analyzing biases in existing systems, defining fairness constraints and evaluation metrics, and designing fair computational frameworks. She is actively building a research community on algorithmic fairness in network science and has organized several satellite events, thematic sessions, and tutorials to support this effort. She is an Associate Editor for SNAM and the PLOS Complex Systems journals. In addition to her research, she serves on the Diversity Committee at LIACS, contributing to initiatives that foster inclusion and equity within the academic community.

Complex networks, such as social, financial, e-commerce, and criminal networks, provide a powerful framework for representing real-world systems by capturing intricate structural patterns and interactions, consisting of nodes (entities) and edges (connections or interactions). For example, in social networks, nodes represent individuals, and edges denote social connections, while in banking transaction networks, nodes correspond to bank accounts, and edges represent financial transactions. Complex networks are analyzed to understand individual and group behavior at a large scale and solve critical research problems, such as fraud detection, link prediction, social media surveillance, and resource allocation. However, these networks often encode structural inequalities related to gender, ethnicity, race, or socioeconomic status. Moreover, groups’ distribution may be inherently imbalanced, with certain groups being underrepresented or more sparsely connected. If such structural inequalities are not considered while designing network analysis algorithms, the outcome might be unfair, particularly disadvantaging minorities or underrepresented groups.

In this lecture, I will highlight how the structural inequalities of complex networks impact the fairness of different network analysis methods using a case study of link prediction. Next, I will discuss a few approaches in depth to address structural biases for fair and diverse link prediction, specifically network-embedding-based methods. Finally, I will briefly introduce other approaches for developing fair solutions across diverse downstream network analysis tasks, along with the primary research focus of our group.

This session will be dedicated to discussing opportunites for student exchange between Chile and Leiden. The session will start with presentations on different potential postgraduate programmes, scholarships, etc., available in Leiden and Chile so students and academics are aware of the opportunties available. Thereafter, participants will form breakout groups to identify potential themes for collaboration, with a focus on defining topics that they can co-supervise as part of postgraduate degrees. These topics will be pitched at the end of the session, providing students space to discuss opportunities with academics from Chile and Leiden.

• Identify opportunities in Chile and Leiden for postgraduate studies.
• Identify technical topics that could lead to co-supervision.
• Discuss these opportunities and topics with interested students.

This session will provide space for Chilean participants to present short talks on their current research topics. The goal is to provide colleagues from Leiden and Chile an overview of the topics investigated by local participants, while identifying possible points of technical connection with the themes developed during the seminar.

Students are invited to present their topic (and advances) in 2 minutes (please add your slides to this deck). Academics, postdocs and professionals can present a short 5 minute talk (please drop your slides into the following drive). The format is intended to be concise, interactive, and accessible to participants from different areas of computer science and related disciplines.

• Showcase current research topics being developed by Chilean participants.
• Create opportunities for technical questions and informal discussion.
• Identify shared interests that can feed into the breakout groups and collaboration sessions.

This session will provide space for breakout groups to discuss specific themes arising from the seminar talks, the Chilean research presentations, and the shared interests of participants. Groups will be formed around technical topics and open questions that can support future research interaction between Leiden and Chile.

• Identify important open research questions and technical bottlenecks.
• Explore opportunities for Chile–Leiden collaboration.
• Define possible topics for joint supervision, exchange, projects, or future workshops.

The Friday morning session will begin in a panel format, bringing together invited speakers and local organisers to discuss challenges and trends for computer science in the context of emergent topics such as artificial intelligence and quantum computing.

The discussion will focus on society and the future: what may come next, which opportunities are emerging, what risks and dangers deserve attention, and which open technical challenges and gaps must be addressed by the research community.

• How will AI and emerging computational paradigms reshape science, engineering, and society?
• What technical breakthroughs are still needed to make future systems reliable, fair, interpretable, and beneficial?
• What are the key risks associated with increasing automation, opacity, and concentration of technological power?
• How should computer science education and research adapt to these developments?

The final session is dedicated to defining follow-ups. Participants will use this time to consolidate the ideas developed during the week, identify concrete next steps, and define possible mechanisms for maintaining collaboration after the seminar and workshop.

Possible follow-ups include joint research proposals, student exchange opportunities, co-supervision of postgraduate students, research visits, future workshops, shared datasets or software, and collaborative publications.

• Summarise the main conclusions and opportunities identified during the week.
• Define concrete follow-up actions and responsible participants.
• Strengthen long-term academic links between Leiden University and Chilean institutions.