Student Presentations Series
In the Student Presentations Series, ELLIS Alicante invites PhD students to present their work to the team of ELLIS Alicante. In collaboration with the University of Alicante, the event is open to any student and researcher of the university.
Talks
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Exploring the opportunities and potential risks associated with the use of Artificial Intelligence across different contexts
Abstract: The increasing integration of AI into domains such as healthcare and psychotherapy has created many beneficial opportunities for society. The first half of the presentation offers an overview of current initiatives on how AI is used in the intervention of addictive disorders.
However, besides AI’s vast potential, there is a growing prevalence of AI anxiety, for which a valid and reliable measurement tool becomes vital. The second half of the presentation will provide insights into the development and validation process of a brief psychometric instrument for AI anxiety.Short bio: Ágnes is a second-year PhD student in Clinical Psychology and Addiction at Eötvös Loránd University. She holds a Master’s degree in Psychology from Pázmány Péter Catholic University, from which she also completed correspondence training in the Application of Artificial Intelligence. In her doctoral research, Ágnes investigates the opportunities and psychological risks associated with the use of AI.
Presenter: Ágnes Magyary
Date: 18 March 2026 at 12:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Can LLMs Have a Personality - And Should They?
Abstract: Personality is what makes people unique. Personality traits describe individuals, explain differences between people and influence behavior and social dynamics. As large language models (LLMs) become increasingly human-like and evolve from helpful assistants to conversational companions, questions arise about whether we can identify and modify LLMs’ personality traits and what the implications are. This talk explores the literature on personality research, computational methods to measure and induce LLM personalities, and their effects on human-AI interaction and collaboration. It examines the potential advantages of LLM personality as well as the risks. Finally, it discusses research gaps and future directions towards personalized and trustworthy human-chatbot interactions.
Short bio: Kim is a first-year PhD student at ELLIS Alicante working in the intersection of psychology and computer science. She holds a Bachelor’s degree in Psychology from the University of Münster and a Master’s degree in Quantitative Data Science Methods from the University of Tübingen. Her research focuses on human-centered chatbot interactions and personality in LLMs. To develop more trustworthy human-AI relationships, she is examining how LLMs can be personalized to the user’s psychological profile. Her work includes investigating the motivation, methods, consequences and applications of LLM personality. Kim is supervised by Dr. Nuria Oliver (ELLIS Alicante), Dr. Cristina Cachero (University of Alicante) and Dr. Anna Korhonen (University of Cambridge).
Presenter: Kim Zierahn
Date: 4 February 2026 at 11:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Say No More! Exploring Linguistic Negation in (M)LLMs for AI Safety
Abstract: Large Language Models (LLMs) and their multimodal counterparts (MLLMs) have shown outstanding performance across a wide range of tasks. However, they often remain surprisingly incapable of handling assignments that are straightforward for humans. One of these is linguistic negation. Previous work on the topic is still scarce, but there is already evidence showing that the negation problem is pervasive in both pure NLP tasks, such as semantic textual similarity or text-to-text retrieval, as well as multimodal settings like text-to-image generation, image segmentation, visual question answering, or cross-modal retrieval. In my work, I will focus on two different avenues related to this linguistic construct. On the one hand, it appears that negation is an easy and effective method to jailbreak (M)LLMs, getting them to output harmful or unethical content with implications in AI Safety and Security. On the other hand, there is still great room for improvement in negation understanding in the aforementioned multimodal tasks. As an amusing example, models have a hard time generating an image of a "room without an elephant" or "a tennis ball with no white lines."
Short bio: Diego Miguel Lozano is a PhD student at ELLIS Alicante. He holds a Bachelor's Degree in Computer Science from the University of Burgos, with a thesis focused on Abstractive Summarization, and a Master’s Degree in Computer Science from the Technical University of Munich (TUM) with a focus on NLP and, more concretely, Semantic Textual Similarity. He has also several years of industry experience in Information Retrieval and Question Answering. Diego’s current PhD is framed within the field of AI Security, with a focus on multimodal LLMs. His supervisors are Dr. Nuria Oliver (ELLIS Alicante), Dr. Erik Derner (Czech Technical University), and Dr. Juan Antonio Pérez Ortiz (University of Alicante).
Presenter: Diego Miguel Lozano
Date: 30 January 2026 at 11:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Youth Labour Markets, Green Transition, and High-Tech Economies: Research Perspectives from Europe and Asia
Abstract: This Student Talk offers research insights on the impact of large-scale economic shifts on labor markets, emphasizing youth employment, sustainability, and high-tech growth. The presentation analyzes the macroeconomic, institutional, and structural determinants of labor market outcomes in Europe, referencing two peer-reviewed articles: “Factors Affecting Youth Unemployment: Empirical Assessment in the European Union Countries” and “The Impact of the Green Transition on the Labor Market in the EU27 and the Baltic States.” The discourse goes beyond Europe by including the presenter’s current master’s study on “The Asian Tiger economies’ impact and role to the establishment and development of global high-tech and innovation market.” This work examines the contributions of South Korea, Taiwan, Singapore, and Hong Kong to the worldwide proliferation of high-tech sectors and innovation ecosystems, as well as the impact of their growth strategies on employment structures and technological dissemination.
Short bio: Tetiana Koroi is an intern at ELLIS Alicante and is currently enrolled in a master’s programme in Diplomacy and International Relations. She holds bachelor’s degrees in International Economics and in Economics and Finance from Taras Shevchenko National University of Kyiv and Vytautas Magnus University. She is a co-author of two peer-reviewed scientific articles on youth unemployment and the impact of the green transition on labour markets in the European Union. Her current research focuses on the role of the Asian Tiger economies in the development of the global high-tech and innovation market.
Presenter: Tetiana Koroi
Date: 16 January 2026 at 14:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Faces of Emotion: Investigating How Generative AI Responds to Human Feelings
Abstract: This talk examines how generative AI systems respond to different human emotions and how these emotions influence the kinds of faces the models produce. By generating large sets of portraits across various emotional expressions, the study looks at how factors such as age, gender, and ethnicity emerge in the outputs of several widely used text-to-image models. Using established tools to automatically identify demographic attributes, the work explores the relationship between emotional prompts and the demographic patterns that appear in the generated images. The goal is to provide an accessible view of how AI interprets human emotions and how those interpretations shape the distributions of the faces it creates.
Short bio: Mengting Wei is now a PhD student in University of Oulu where she works in the Center for Machine Vision and Signal Analysis (CMVS) under the supervision of Prof. Guoying Zhao. She is also a PhD student in European Laboratory for Learning and Intelligent Systems (ELLIS), co-supervised by Prof. Nuria Oliver. Previously, she graduated from Southeast University with a master degree, supervised by Prof. Wenming Zheng. She received her bechelor degree from Hebei University of Technology. Her current research interests lie at face generation and editing.
Presenter: Mengting Wei
Date: 10 December 2025 at 10:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Interacting in Natural Language in the era of LLMs: NLP for Online Discussions and Multi-Party Conversations
Abstract: With the rise of online social media platforms, written conversations among multiple participants have become an essential part of daily communication, enabling users to exchange ideas, share opinions, and engage in discussions. Beyond the textual content of individual messages, the dynamics of participant interactions influence the meaning and the pragmatic aspects of the conversations . In this talk, we will explore the challenges faced by models in processing Multi-Party Conversations (MPCs), the key downstream tasks in this domain, and the importance of incorporating interaction dynamics. Specifically, we will discuss the impact of training data size, highlight critical evaluation challenges, and propose a potential solution to address these issues based on the generation of synthetic MPCs. We will mainly focus on the latter, exploring different models and strategies for generating MPCs under precise constraints. We also introduce an evaluation framework to assess both constraint compliance and generation quality, in order to identify which model-strategy combinations are more effective.
Short bio: Nicolò Penzo is a third-year PhD Student in Information Engineering and Computer Science at the University of Trento, holding a scholarship funded by the Digital Humanities group at Fondazione Bruno Kessler. He is also part of the ELLIS PhD programme and is currently spending six months at the Center for Artificial Intelligence and Data Science, University of Würzburg. He holds a Master’s degree in Computer Engineering from University of Padua.
His PhD project deals with the design, analysis and evaluation of models to perform downstream tasks on Multi-Party Conversations (MPCs). The thesis's main focus is the generation of synthetic MPCs, by combining NLP solutions (i.e., transformer-based models) with techniques from network analysis. The goal is to diagnose the models' behavior in different interactional scenarios and favour privacy-preserving solutions. Other research interests involve detection and contrast of toxic language on social media, and interactions among LLMs.This talk is part of the ELLIS / FBK-MobS Workshop.
Presenter: Nicolò Penzo
Date: 9 April 2025 at 11:30 CEST
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Fairness and Alignment in Collective Decision-Making under Uncertainty
Abstract: Collective decision-making (CDM) is a process where multiple individuals often with diverse goals and preferences interact to arrive at a shared decision, like friends choosing a restaurant or political voting. These diverse and sometimes conflicting goals and preferences introduce complexities of alignment in the optimisation process. While current techniques attempt to tackle these issues it is unclear how online machine-learning approaches can be effectively integrated to optimize the CDM process in the absence of explicit feedback. In addition to this, it is crucial to make the collective decision fair for social cohesion and harmony. To address this challenge, we explore aggregation and preference elicitation methods that are fair and align collective decisions with the values of participants, emphasizing the importance of managing inherent uncertainties in both the outcomes of decisions and the preferences of decision-makers.
Short bio: Apurva Shah is a first-year Joint PhD student at Université Libre de Bruxelles and Vrije Universiteit Brussel in Belgium. She is currently working on fairness and alignment in collective decision-making under uncertainty. She completed her MSc in Behavioural and Data Science from the University of Warwick, UK and her BA in Psychology from Fergusson College, India.
This talk is part of the ELLIS / ULB-MLG Workshop.
Presenter: Apurva Shah
Date: 28 February 2025 at 12:30 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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Empowering African Languages through NLP: The Masakhane Project
Abstract: Despite representing approximately one-third of the world's languages, African languages are vastly underrepresented in the field of Natural Language Processing (NLP) and related technologies. The Masakhane Project —a collaborative, open-source initiative led by African researchers— aims to bridge this gap by developing NLP tools tailored for African languages. This talk will introduce the mission and methodologies of Masakhane, explore how they tackle the unique linguistic challenges of the continent, and assess the impact of their work in harnessing Artificial Intelligence to empower African communities.
Short bio: Ayodele Awokoya is a lecturer in the Computer Science Department at McPherson University and a PhD candidate at the University of Ibadan, both located in Nigeria. With a deep passion for Natural Language Processing (NLP), Ayodele's research is centered on advancing Machine Translation, particularly for low-resource African languages, aiming to bridge the linguistic gap in technology. Beyond academia, Ayodele is an avid community builder and developer, contributing to open-source projects and fostering collaborations within the NLP research community. As a fellow of the prestigious Heidelberg Laureate Forum (HLF), Ayodele has engaged with global leaders in mathematics and computer science, leveraging the platform to deliver science-oriented talks to secondary school girls, inspiring the next generation of scientists. Through partnerships with initiatives like the Masakhane project, Ayodele remains dedicated to making NLP tools more inclusive and accessible, empowering underrepresented languages and communities across Africa and beyond.
Presenter: Ayodele Awokoya
Date: 24 January 2025 at 11:30 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
Online: Meeting link
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On the Relationship of Fairness and Uncertainty
Abstract: To ensure the reliability and trustworthiness of machine learning models in real-world applications, it's crucial to assess the uncertainty about their predictions (predictive uncertainty). In a practical scenario, uncertain decisions will be deferred to human experts or opted for a failsafe alternative. Machine learning models already operational in our society to make inferences or predictions in consequential domains in people's lives ---such as healthcare, access to credit, social programs or promotions--- have been found to exhibit differences in their performance depending on the person's gender, race, or age, leading to unfair treatment to certain groups of individuals. Fairness research aims to identify, analyze, and address these systematic biases in machine learning models. This presentation will delve into the interplay between predictive uncertainty and algorithmic fairness, addressing questions such as: Do models exhibit the same levels of unfairness at high or low certainty levels? What kind of uncertainty leads to unfairness? Is it possible to leverage the predictive uncertainty to develop fairer models? I will provide a literature review of current knowledge about those questions, present our latest experimental insights, and discuss future research avenues.
Short bio: Kajetan Schweighofer is an ELLIS PhD student and holds a bachelor’s and master’s degree in physics as well as a second master’s degree in artificial intelligence from Johannes Kepler University Linz. The master’s thesis in artificial intelligence supervised by Dr. Sepp Hochreiter focused on Offline Reinforcement Learning. The master’s thesis in physics supervised by Dr. Armando Rastelli focused on utilizing artificial intelligence for quantum optical experiments. Kajetan conducts his PhD under the supervision of Dr. Sepp Hochreiter at Johannes Kepler University Linz and Dr. Nuria Oliver at ELLIS Alicante. He focuses on improving uncertainty quantification for deep learning methods, to make those methods reliable and trustworthy when applied in critical settings. Furthermore, during the exchange in Alicante throughout the winter semester 2023/24, Kajetan investigates implications of uncertainty on the fairness of deep learning methods.
Presenter: Kajetan Scheweighofer
Date: 26 March 2024 at 12:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
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Towards Student-Centric AI-Supported Learning: Teaching Chatbots to Ask the Right Questions
Abstract: In the rapidly evolving landscape of educational technology, the integration of Large Language Model (LLM)-based chatbots presents a transformative opportunity to enhance learning experiences. Our project introduces "Maike", an innovative educational chatbot designed to revolutionize the traditional learning paradigm by fostering critical thinking, purposeful learning, and self-efficacy among students. Unlike conventional chatbots that provide immediate answers, Maike employs Socratic dialogues to engage students, prompting them to explore and reflect upon their queries through guided questions. During this presentation, we will delve into a comprehensive literature review highlighting the significance of such technologies in education and detail the theoretical framework underpinning Maike.
Short bio: Lucile Favero is an ELLIS PhD student, with a bachelor’s degree in mathematics from the University of Geneva and a master’s degree in Mathematics and Neuroscience. In her master thesis in Mathematics, directed by Dr. Sylvain Sardy, she developed a machine learning model to optimize heat pump systems. In parallel, in her neuroscience studies, Lucile did a full-time internship for three years under the supervision of Dr. Giulio Matteucci at the El-Boustani laboratory. There, she implemented a decision-making model that promises to provide invaluable information to experimenters when designing, analyzing and refining behavioral tasks. Lucile is currently pursuing her PhD studies under the guidance of Dr. Nuria Oliver (ELLIS Alicante), Dr. Tanja Kaser (EPFL) and Dr. Juan Antonio Pérez Ortiz (UA) focusing on the integration of the Socratic method in chatbot development to improve educational outcomes.
Presenter: Lucile Favero
Date: 1 March 2024 at 11:30 CET
Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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Navigating Privacy in Healthcare tech for Senior Care, User Side of the Story
Abstract: Active and Assisted Living (AAL) technologies strive to enrich the quality of life of older adults and facilitate successful aging. While video-based AAL solutions offer rich capabilities for better healthcare management in older age, they pose significant privacy risks. In response to these challenges, the AMI4AHA (Ambient Intelligence for Active and Healthy Ageing) research team at the University of Alicante has developed a video-based monitoring system for senior care. The system incorporates various privacy-preserving filters to address the privacy risks effectively. The presenter aims to offer insights from the user perspective regarding such technologies. She will delve into her three-year research process, which focuses on comprehending the diverse perspectives of stakeholders involved in the caregiving and receiving process.
Short bio: Tamara Mujirishvili is an Early Stage researcher at the Marie Curie ITN VisuAAL, based at the university of Alicante. Her research project focuses on the perceptions of personal safety and privacy in older adults in the context of video-based lifelogging technologies. Tamara received her Master's Degree in Neuroscience from the University of Bordeaux (France) in 2020. She executed her master thesis research at the University of Cambridge investigating how prior expectations influence perception. She obtained her BA Degree in Psychology from Tbilisi State University (Georgia) in 2015. She has spent the 2013-2014 academic year at the University of Groningen (The Netherlands) as an exchange student at the faculty of Behavioral and Social Sciences, specializing in Psychology.
Presenter: Tamara Mujirishvili
Date: 22 February 2024 at 14:00 CET
Location: Distrito Digital 5, Muelle Pte., 5 – Edificio D, Alicante 03001, Alicante ES
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Probability and Machine Learning
Abstract: In this talk, I will challenge common ideas about probability, particularly in the context of machine learning. The main point is that there are no correct probabilities, that they are always constructed rather than discovered. I will show that constructed probabilities can still be useful and specify the assumptions under which e.g. expected utility maximisation is a sensible policy (which is usually taken for granted). Based on these general considerations, I will outline some insights for algorithmic fairness. As for other parts of my talk, central aspects are the tension between individuals and groups as well as a general notion of calibration. I will then draw on previously discussed issues to criticise the way individual predictions are typically presented in machine learning. In the last part, I will argue against the true distribution framework that underlies almost every analysis of theoretical guarantees in machine learning.
Short bio: Ben holds Bachelor’s degrees in Mathematics and Philosophy from LMU Munich as well as Master’s degrees in Philosophy of Science from LMU and in Computer Science from the University of Oxford. He continued to work there as a research assistant with the OATML group before starting a PhD at the University of Tübingen with Bob Williamson. Ben is co-supervised by Nuria Oliver within the ELLIS programme and will spend the winter semester 2024/25 in Alicante. His main interests lie at the intersection between the social impact of algorithmic predictions and the foundations of machine learning and probability.
Presenter: Benedikt Höltgen
Date: 14 February 2024 at 11:00 CET
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
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Variational Mixture of HyperGenerators for learning distributions over functions
Abstract: Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing inference tasks, such as missing data imputation, or directly cannot tackle them. In this presentation, we will talk about a novel deep generative model, Variational Mixture of HyperGenerators (VAMoH). VAMoH combines the capabilities of modeling continuous functions using INRs and the inference capabilities of Variational Autoencoders (VAEs). Through experiments on a diverse range of data types, such as images, voxels, and climate data, we show that VAMoH can effectively learn rich distributions over continuous functions. Furthermore, it can perform inference-related tasks, such as conditional super-resolution generation and in-painting, as well or better than previous approaches, while being less computationally demanding.
Short bio: Batuhan Koyuncu is an ELLIS PhD student at Saarland University, advised by Isabel Valera and co-advised by Ole Winther. His research interests include building expressive, efficient, and interpretable deep generative models, and utilizing their applications in psychiatry and healthcare.
Presenter: Batuhan Koyuncu
Date: 5 April 2023 at 10:00 CEST
Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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Towards Explainable AI
Abstract: AI systems have become the de facto tools to solve complex problems in computer vision. Yet, it has been shown that these systems might not actually be safe to be deployed in the real world, as they too often tend to rely on dataset biases and other statistical shortcuts to achieve high performance. A growing body of research thus focuses on the development of explainability methods to better interpret these systems, to make them more trustworthy. In this talk, I will first give a general overview of the methods commonly used in eXplainable AI, before discussing the challenges that still need to be overcome by the community.
Short bio: Julien Colin is an ELLIS PhD student. He holds a Bachelor’s degree in Physics and Chemistry (2019, University of Lorraine) and a Master’s degree in Cognitive Sciences: Natural and Artificial Cognition (2021, INP Grenoble). Before the start of his PhD, he worked as a research assistant; first at ANITI for 6 months (2021, Toulouse) then at Brown University for 5 months (2022, Providence). His PhD topic is centered around eXplainable AI and Cognitive Science. In his research, he is interested in developing methods to better understand Intelligent systems. His supervisors are Nuria Oliver (ELLIS Alicante) and Thomas Serre (ANITI).
Presenter: Julien Colin
Date: 30 January 2023 at 11:30 CET
Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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Modeling Irregular Time Series with Continuous Recurrent Units
Abstract: Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Modern RNN architectures assume constant time-intervals between observations. However, in many datasets (e.g. medical records) observation times are irregular and can carry important information. To address this challenge, we propose continuous recurrent units (CRUs) -- a neural architecture that can naturally handle irregular intervals between observations. The CRU assumes a hidden state, which evolves according to a linear stochastic differential equation and is integrated into an encoder-decoder framework. The recursive computations of the CRU can be derived using the continuous-discrete Kalman filter and are in closed form. The resulting recurrent architecture has temporal continuity between hidden states and a gating mechanism that can optimally integrate noisy observations. We derive an efficient parameterization scheme for the CRU that leads to a fast implementation f-CRU. We empirically study the CRU on a number of challenging datasets and find that it can interpolate irregular time series better than methods based on neural ordinary differential equations.
Short bio: Mona Schirmer is an ELLIS PhD student at the University of Amsterdam and a machine learning consultant at the World Bank. Before that, she completed a master’s in statistics at Humboldt University and Technical University in Berlin as well as a French engineering diploma at ENSAE. In her Bachelor’s, she studied economics and political science at Humboldt University of Berlin and the University of Munich. Her research interests lie in probabilistic machine learning und machine learning for social good. She visited ELLIS Alicante in May 2022.
Presenter: Mona Schirmer
Date: 25 May 2022 at 11:30 CEST
Location: Salon de Actos Politecnica I, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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AI and Human Biases
Abstract: Picture this - you are at the movies waiting in line to buy popcorn. On the big, bright screen behind the counter you see multiple options. You take your time and carefully crunch all the numbers in front of you and order what you think is the optimal choice. You walk away with a big smile on your face because you just got a very good deal. However, chances are you just got tricked into buying something you didn’t even want to begin with. This is a classic example of sub-optimal human decision making that has been studied in behavioral economics. However, the penetration of these ideas into Artificial Intelligence has been fairly limited. Given that in today's world humans and machines work closely together, it’s important to design systems that can account for the fact that our decisions are heavily influenced by multiple cognitive biases. This talk is intended to be an introduction to the design of such systems. We will look at multiple biases and how they have been used in the real world. We will also look at work in Artificial Intelligence that tries to account for these biases and propose future directions for this area.
Short bio: Aditya Gulati is an ELLIS PhD student. He holds a Bachelor’s and Master’s degree in Computer Science Engineering from the International Institute of Information Technology Bangalore. His research interests lie in modelling human behavior and artificial intelligence. His supervisors are Nuria Oliver (ELLIS Alicante), Miguel Angel Lozano (University of Alicante) and Bruno Lepri (Fondazione Bruno Kessler).
Presenter: Aditya Gulati
Date: 28 February 2022 at 11:30 CET
Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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AI on social media: cultural impact and proposed solutions
Abstract: Social media platforms have an unprecedented impact on our culture, exacerbating existing social disparities and filtering our vision of the world. Unlike previous media, these platforms offer enormous possibilities to carry out analytical research. Given the wide availability of data, we can deepen our understanding of inequalities and take concrete actions to mitigate them. In this talk, I will present cultural paradigms that create detriment for the life of women, both online and offline. In particular, I will focus on the role of Artificial Intelligence, and specifically the impact that autonomous algorithms -pervasive on social media- have on women. I will propose a proactive research perspective to achieve positive cultural impact with AI.
Short bio: Piera Riccio is an ELLIS PhD student. She holds a bachelor’s degree in Cinema and Media Engineering (2018, Politecnico di Torino), a Master’s degree in ICT for Smart Societies (2021, Politecnico di Torino), and a Master’s degree in Data Science and Engineering (2021, Télécom Paris – EURECOM). In 2020, she was an affiliate at Metalab (at) Harvard. In 2021, she was a research assistant at the Oslo Metropolitan University. In her research, she is interested in exploring the cultural, social, and artistic possibilities of AI. In her PhD, she focuses on the effect that social media have on the lives of women and the way they are perceived in the social media cultural ecosystem. Her supervisors are Nuria Oliver (ELLIS Alicante), Thomas Hofmann (ETH Zurich) and Francisco Escolano (Universidad de Alicante).
Presenter: Piera Ricco
Date: 14 February 2022 at 11:30 CET
Location: Salon de Grados Derecho, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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Fairness in Federated Learning: Challenges and Opportunities
Abstract: Federated Learning is a popular, emergent field within Machine Learning. It has advantages and challenges. It can help implement Machine Learning in tasks with low, sparse data sources or without strong central computing power. It can be a good answer for clients who do not want to share their data with the rest of the world, yet they want to benefit from the generalized performance of a global model. However, federated learning models can suffer from bias. Most of the current fairness-ensuring methods in machine learning use the relative position of the individuals within the user space to ensure the model's fairness when compared to the rest of the users. Unfortunately, it is not trivial to apply such an approach in a federated architecture. For example, how can we identify bias against a certain group (e.g. race, sex) if we don't have a global evaluation of fairness metrics? Investigating the trade-offs between privacy and fairness is an open question in Federated Learning that I will investigate in my PhD thesis. This talk provides a general overview of Federated Learning research and shows the unique challenges and solutions of fairness guarantees in a federated learning setup.
Short bio: Gergely Dániel Németh is a PhD student at ELLIS Alicante. His PhD topics are AI Ethics and Federated Learning. His supervisors are Nuria Oliver (ELLIS Alicante), Miguel Angel Lozano (University of Alicante) and Novi Quadrianto (University of Sussex). He holds a CS MSc degree from The University of Manchester and a BSc from Budapest University of Technology and Economics. His university topic was about Natural Language Processing, but he also worked on Computer Vision in a Hungarian StartUp.
Presenter: Gergely D Németh
Date: 31 January 2022 at 11:30 CET
Location: Salon de Actos Politecnica I, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
Online: Meeting link
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Fairness in Machine Learning
Abstract: Machine learning models are becoming the main tools for addressing complex societal problems and are also increasingly deployed to make or support decisions about individuals in many consequential areas of their lives, from justice to healthcare. Therefore, the ethical implications of such decisions, including concepts such as privacy, transparency, accountability, reliability, autonomy, and fairness need to be taken into account. Specifically, we will explain the current landscape in AI Fairness, from the sources of the bias and different algorithmic fairness approaches to their limitations and cutting-edge approaches. The main goal is to provide a general overview of what is Fairness as well as the main research challenges that the community has to address.
Short bio: Adrián Arnaiz Rodríguez is an ELLIS PhD student. He holds a Bachelor’s degree in Computer Engineering (2019, Universidad de Burgos) and a Master’s degree in Data Science and Artificial Intelligence (2021, Universitat Oberta de Catalunya) doing the MSc thesis with Baris Kanber (University College London) in medical neuroimaging. His PhD supervisors are Nuria Oliver (ELLIS Alicante), Francisco Escolano (Universidad de Alicante) and Manuel Gómez Rodríguez (Max Planck Institute for Software Systems). His PhD topics are AI Fairness, Causality and Graph Theory to enhance ethics, accountability, and transparency in algorithmic decision-making.
Presenter: Adrián Arnaiz Rodríguez
Date: 17 January 2022 at 10:00 CET
Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES
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Data-efficient methods for model learning and control
Abstract: In this talk, I will present an overview of the research I have been working on during my Ph.D. project, focused on data-efficient methods in machine learning. The first part will be dedicated to learning models of dynamic systems. Models facilitate simulations, analysis of the system's behavior, decision making, and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning have been shown to benefit from the use of models. However, obtaining informative data for constructing dynamic models can be difficult, especially when the models are to be learned during task execution. To this end, symbolic regression proves to be a suitable method to automatically build such models. This technique, based on genetic programming, constructs from data parsimonious models in the form of analytic equations. It represents an alternative to the currently popular data-hungry deep learning methods, which typically produce black-box models. One of the challenges in continual model learning is posed by the large amount of data collected from the system. I will present a comparison of methods for selecting informative training samples and show that symbolic regression can be used to construct accurate models from very small informative data sets. Furthermore, I will explain how symbolic regression can be naturally extended to account for physical constraints and a partially known theoretical or empirical model of the system. However, data-efficient methods are needed not only to learn models of the dynamic systems but also to learn models of the environment. Therefore, the talk will be concluded by presenting a method from the field of computer vision for robotics. Ubiquitous changes of the scene structure and appearance are typical for real-world dynamic environments and make many conventional localization and navigation methods fail. I will present a method for change detection based on weighted local visual features that improves the localization accuracy by distinguishing between stable parts of the scene and potentially confusing changing regions.
Short bio: Erik Derner received the M.Sc. degree in artificial intelligence and computer vision from the Czech Technical University (CTU) in Prague, where he is currently pursuing a Ph.D. degree. Throughout his studies, he acquired a broad international experience during five stays at universities in four different countries, ranging from a few months to the full academic year. His research interests are focused on sample-efficient methods for model learning applied to dynamic systems such as mobile robots and their environments. The main areas of research comprise genetic programming, reinforcement learning, and computer vision. The central topic in his research is the use of symbolic regression to automatically construct nonlinear models of dynamic systems.
Presenter: Erik Derner
Date: 13 December 2021 at 12:00 CET
Location: Laboratorio de Grados Politecnica I, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES