About GIIA

Who We Are

GIIA is a collaborative and multidisciplinary interest group dedicated to Artificial Intelligence, Machine Learning, and Data Science at the University of Concepción. Born from the Innovation teams of CEEIND 2025 in collaboration with CAINF 2025, we identified the clear need for a dedicated space focused on these transformative technologies within the Faculty of Engineering.

Our group serves as a dynamic ecosystem where students, professors, and technology enthusiasts converge to explore, learn, and apply cutting-edge solutions to real-world challenges.

Our Mission

We foster comprehensive education, practical development, and innovative application of knowledge in AI, ML, and Data Science. Through collaborative projects and continuous learning, we bridge the gap between theoretical concepts and tangible solutions that address both internal university needs and external community challenges.

Strategic Objectives

• Promote Practical Learning: Create environments where theoretical knowledge meets hands-on application in solving concrete challenges
• Drive Real-World Solutions: Utilize analytical and automation tools to address relevant problems with measurable impact
• Build Collaborative Communities: Establish inclusive spaces where diverse talents can interact, share ideas, and innovate together
• Strengthen Technological Ecosystems: Contribute to positioning the University of Concepción at the forefront of digital innovation

Focus Areas

Our work spans the complete spectrum of data intelligence:

• AI & ML Fundamentals: Exploring theoretical principles and core algorithms
• Data Processing & Analysis: Mastering techniques for managing, cleaning, and transforming complex datasets
• Applied Digital Projects: Developing innovative solutions for real-world problems
• Data-Driven Engineering: Approaching challenges with engineering rigor and data-centric perspectives

Our team

Get to know the team that is behind everything that we're building.

Juan Carlos Caro

I am an Assistant Professor at the Department of Industrial Engineering, at the University of Concepcion, Chile. Previously, I conducted my PhD in Health Economics at the University of North Carolina, Chapel Hill, and Post-doctoral work at the Department of Behavioral and Cognitive Sciences, at the University of Luxembourg.

Currently, I am dedicated to study social vulnerability, parental behavior, malnutrition, and early human capital accumulation. In particular, I focus on promising tailored interventions that promote adequate health equity and inclusion among families and communities. This work involves close collaboration with ongoing programs targeting children, families and schools.

Caroll Beltrán

Dr. Beltrán is Pharmaceutical Chemist, PhD. in Biomedical Sciences. As an associate professor in the Department of Clinical Biochemistry and Immunology, School of Pharmacy, University of Concepción, and in the Department of Medicine, School of Medicine, University of Chile, she leads the Immunogastroenterology Laboratory. Her research is focused on discovering the molecular and cellular mechanisms of the intestinal mucosal immune response underlying disorders of the brain-gut axis and the gut-liver-brain axis. Specifically, based on the perspective of personalized medicine and the biopsychosocial model, her research aim on elucidating the etiopathogenic mechanisms that contribute to the identification of biomarkers for inflammatory bowel disorders, colorectal cancer and Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD). Her scientific activity involves observational studies in patients, as well as in vitro and in vivo approaches. In addition to the competitive government projects awarded since 2012, her research is strengthened by the support and collaboration of medical professionals, as well as leading national and international researchers in the fields of mucosal immunology and neurogastroenterology. Additionally, her studies include evaluations of the use of integrative therapies, such as the Mindfulness-Based Stress Reduction Program, to modulate clinical symptoms and the immune response of patients. Collectively, the results of her research have been disseminated in various presentations, both to specialists in the field and to the public; and are included in published manuscripts or those in the process of being published.

Ricardo Flores

I’m a PhD in Data Science with expertise in AI, Data Science, Mental Health, Deep Learning for Healthcare, and Multimodal Models involving image, text, and audio data. I am a passionate data science teacher with a deep commitment to empowering students to become skilled and versatile and data scientists. My primary goal as a data science professor is to guide students through the intricacies of data analysis, fostering a comprehensive understanding of statistical techniques, machine learning algorithms, and data mining methodologies. My current research focuses on applying multimodal models for mental health screening. The features from multiple modalities, such as facial, voice, and physical gestures, can help to understand the complexity of human behavior. Based on the same argument, some authors suggest these modalities are appropriate for mental health screening by using machine and deep learning models. In the mental health community, while the audio and transcript from interviews have received more attention, facial features extracted from videos offer an attractive privacy-preserving screening modality. Thus, I extract the time series of eye gaze, facial landmarks, and facial action units from real dataset videos. Because I have a sequential-spatial dataset, I leverage state-of-the-art pre-trained deep learning models. In this context, I have proposed several multi-modal deep learning models that input temporal facial features, audio, and transcripts to screen for mental health tasks. These projects have been published in several journals and conferences such as Journal of Biomedical and Health Informatics (JBHI), ACM for Healthcare, Machine Learning for Healthcare, IEEE International Conference on Big Data (BigData), and IEEE International Conference on Machine Learning and Applications (ICMLA). For more information, please visit here.

Jorge Maluenda

Jorge Maluenda Albornoz is a psychologist, holds a Master’s in Politics and Government, and a Ph.D. in Psychology from the University of Concepción. He currently serves as an Assistant Professor and Deputy Director of Teaching at the Faculty of Engineering at the same university.

His research focuses on the psychological and social factors that influence learning and academic performance, such as motivation, engagement, and sense of belonging, both at the individual and collective levels. In addition, he is particularly interested in the development of high-quality measurement instruments for assessing individuals and human processes.

At the HCI-Lab, his work centers on the use of technology to strengthen the measurement of human and organizational behavior, as well as the factors that influence the adoption and beneficial use of technology.

At both local and international levels, he has led research projects and advised educational transformation processes in universities in Chile, Colombia, Peru, Panama, and France, contributing to the redesign of educational models and evidence-based teaching practices.

His recognitions include the Best Research Award from the Ministry of the Interior (2015), the Science with Impact Award from the University of Concepción (2022), and the distinction as Outstanding Latin American Professor in Educational Innovation (2021).

Mabel Vidal

Mabel Vidal Miranda, Ph.D. is an Associate Professor at the University of Concepción and a faculty member of the Ph.D. program in Artificial Intelligence, part of the FIC-funded Advanced Human Capital in AI initiative for the Biobío region. She holds a degree in Bioinformatics Engineering and a Ph.D. in Computer Science. Her research focuses on the application of machine learning and deep learning to large-scale sequencing data, with emphasis on genetic variation analysis, animal welfare, and the impact of environmental changes on living organisms. Dr. Vidal’s interdisciplinary work integrates computational methods with real-world biological and environmental challenges, fostering collaborations across academia, industry, and government. She has extensive experience teaching undergraduate and graduate-level courses in her areas of expertise and supervising graduate research in data science and artificial intelligence. Beyond her academic work, she is a member of IEEE and serves as secretary and board member of Fundación Niñas Pro, an organization dedicated to promoting the participation of women in STEM fields. Dr. Vidal has received several distinctions recognizing her scientific and outreach contributions: in 2019 she was honored by the Chilean Ministry of Women and Gender Equality as an Outstanding Woman in Science and Technology; in 2020 she received the L’Oréal–UNESCO For Women in Science Award; and in 2024 she was named one of the “50 Genias of the Year” in STEM.

Proyects

Dr. Mabel Vidal Miranda leads research initiatives that integrate data science, bioinformatics, and artificial intelligence to address complex biological and environmental challenges. One of her primary projects, Computational Exploration of the Immune Response in the Skin of Atlantic Salmon, focuses on understanding host–pathogen interactions using high-throughput transcriptomic data. The study investigates how Atlantic salmon respond immunologically to infestation by the ectoparasite Caligus rogercresseyi and how environmental water parameters modulate this response. By applying machine learning and deep learning algorithms, the project aims to identify molecular markers and develop predictive models that inform sustainable aquaculture practices, ultimately improving fish health and welfare. In parallel, Dr. Vidal is part of the Millennium Nucleus for Data Science and Plant Resilience (PhytoLearning), a multidisciplinary initiative that applies advanced computational methods to study plant adaptation and resilience in response to environmental stressors, such as climate change. Through the integration of multi-omics data and AI-driven approaches, PhytoLearning seeks to uncover mechanisms that enhance plant survival and productivity, providing key insights for agriculture and food security. Both projects exemplify Dr. Vidal’s interdisciplinary approach, combining computational innovation with biological applications. Her leadership fosters collaborations between academia, industry, and government, advancing the responsible use of AI to address critical issues in aquaculture, agriculture, and environmental sustainability.

Jérémy Barbay

Jérémy was born in France, in June 1976. He received a Bachelor of Science degree in Mathematics in 1997 in Rouen, a Master degree in 1998 and a Philosophy Doctorate in 2002, both in Computer Science at the University of Orsay. He worked as a posdoctoral fellow at the University of British Columbia until 2004, as an assistant professor at the Cheriton School of Computer Science of the University of Waterloo until 2008, and as an assistant professor at the department of Computer Science of the University of Chile in Santiago, Chile until 2023. He is currently working as an associate professor at the the Department of Ingenieria Informatica en Ciencias de la Computación (DIICC) of the Faculty of Engineering of the University of Concepción in Concepción, Chile. His main research is about the analysis of algorithms and data-structures on finer classes of instances than those merely defined by their size: this is known as "Adaptive (analysis of) Algorithms (and Data Structures)". He is interested in other topics of research, in particular in applying information technologies to the education of humans teachingislearning and of Other Animals Than Humans incalab.

Join Our Innovation Journey

We're building more than just a study group - we're cultivating a movement of innovators, problem-solvers, and future technology leaders. Whether you're passionate about machine learning algorithms, data analytics, or AI applications, there's a place for you in our collaborative community.

Together, we're shaping the future of technology at Universidad de Concepción and beyond.