As per its tradition, ICHI 2025 will host invited talks from outstanding speakers.
Keynote #1, June 18th – The AI-Driven Hospital of the Future
Speaker: Pierre Baldi, UnivERSITY OF CALIFORNIA AT Irvine, Ca, USA
Speaker’s short bio: Pierre Baldi grew up in Rome and earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Distinguished Professor in the Department of Computer Science, Director of the AI in Science Institute, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine.
The long term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of AI and deep learning, and developed and applied AI and deep learning methods for the natural sciences, to address problems in physics (e.g., exotic particle detection) , chemistry (e.g., reaction prediction), and bio-medicine (e.g., protein structure prediction, biomedical imaging analysis), circadian rhythms. He recently published his fifth book: Deep Learning in Science, Cambridge University Press (2021). His honors include the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, the 2023 Dennis Gabor Award, and election to Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB. He has co-founded several startup companies.
Abstract: Artificial Intelligence (AI) today can pass the Turing test and is in the process of transforming science, technology, society, humans, and beyond. Surprisingly modern AI is built out of two very simple and old ideas, rebranded as deep learning: neural networks and gradient descent learning. I will describe several applications of AI to problems in biomedicine developed in my laboratory, from the molecular level to the patient level using omic data, imaging data, clinical data,text data, and beyond. I will discuss the opportunities and challenges for developing, integrating, and deploying AI in the first AI-driven hospitals of the future and present two frameworks for addressing some of the most pressing societal issues related to AI research and AI safety.
Keynote #2, June 19th – Personalized Clinical Guideline-based Decision Support Based on Domain Knowledge and Machine Learning
Speaker: Mor Peleg, Dept. of Information Systems, University of Haifa, Haifa, Israel
Speaker’s short bio: Mor Peleg holds a BSc and MSc degrees in biology and PhD (1999) in Information Systems, from the Technion and postdoctoral studies (2003) at Stanford’s Medical Informatics program. She is Full Professor at the Department of Information Systems, University of Haifa, which she joined in 2003, Head of the University of Haifa’s Data Science Center, and former Chair of the Department of Information Systems and of the BSc in Data Science program.
She is the Editor-in-Chief of the Journal of Biomedical Informatics and is Fellow of the American College of Medical Informatics and of the International Academy of Health Sciences Informatics.
Prof. Peleg is internationally renowned in the area of clinical decision support, with a focus on clinical-guideline based decision support and using ontologies to integrate patient data with clinical knowledge. She received AMIA’s New Investigator Award in 2005 for her work on the GLIF3 computer-interpretable guideline language.
She led the large-scale European project MobiGuide, providing sensor-monitoring and evidence-based personalized decision-support to patients any time everywhere. Her current research exploits AI and Big Data for a new model of cancer care (https://capable-project.eu/).
Abstract: My research focuses on developing methods that integrate domain knowledge with machine learning to support clinical decision-making. The source of clinical knowledge is evidence based clinical guidelines, and machine learning methods are used to provide personalize the clinical recommendations and their delivery. In my talk, I will focus on two applications.
1. Personalizing Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge (with Aneta Lisowska and Szymon Wilk). We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI) based on behavioral theory. Our proposed system predicts whether a patient will perform the targeted behavior (recommended by the clinical guideline) and uses counterfactual examples with feature control to guide personalization of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design that enables preliminary evaluation of our system.
2. Generating Ontology-Learning Training-Data through Verbalization (with Antonio Zaitoun and Tomer Sagi). Ontologies are very useful tools for health data integration and semantic reasoning. Yet, their maintenance and update is usually manually done. Our overall vision is to use LLMs to support ontology learning based on evidence-based clinical knowledge. Unlike previous work on LLM-based knowledge graph extension, ontologies present unique challenges, and in particular Web Ontology Language (OWL) axiom generation, as such controlled language is different than natural languages. To fine-tune LLMs so they could output OWL axioms, we needed to create a data set of pairs of ontology axioms and their natural language translations. We therefore developed an LLM-assisted verbalizer, specifically for the task of converting OWL statements from existing ontologies into natural text. We evaluated our approach on 322 classes from four different ontologies using two different LLMs, achieving precision and recall as high as 0.99 and 0.96, respectively.
Keynote #3, June 20th – Computational analysis of interactomes: Challenges, solutions, and opportunities
Speaker: Tamer Kahveci, Computer and Information Science and Engineering Dept., University of Florida, Fl, USA
Speaker’s short bio: Tamer Kahveci received his Ph.D. degree in Computer Science from University of California at Santa Barbara in 2004. He is currently a Professor and Associate Chair of Academic Affairs in the Computer and Information Science and Engineering Department at the University of Florida, serving as the Associate Chair of Academic affairs. Dr. Kahveci received the Ralph E. Powe Junior Faculty Enhancement award in 2006, CSB best paper award in 2008, the NSF Career award in 2009, the ACM-BCB (Bioinformatics and Computational Biology) best student paper award in 2010, ACM-BCB honorary best paper award in 2011, BiCoB best paper award in 2018, and ACM BCB best student paper award in 2022. His research focuses on bioinformatics. He has worked on indexing sequence and protein structure databases, sequence alignment and computational analysis of biological networks.
Dr. Kahveci is the Editor in Chief of the IEEE Transactions on Computational Biology and Bioinformatics. He has served as the PC co-chair of the ACM BCB conference in 2012 and 2017, the BioKDD workshop and the International Workshop on Robustness and Stability of Biological Systems and Computational Solutions in 2012, the Workshop on Epigenomics and Cell Function in 2013, and the Workshop on Computational Network Analysis from 2014 to 2020, the Workshops Chair of the ACM-BCB conference in 2014. He served as the Tutorials Chair of the ACM BCB and the IEEE BIBM conferences in 2015, and Workshop Chair in 2016. He was a member of the governing board of the ACM SIGBIO until 2023, and the co-chair of the steering committee member of the ACM-BCB. He served at the editorial review board for of the journal International Journal of Knowledge Discovery in Bioinformatics (IJKDB). He was the lead guest editor of the Journal of Advances in Bioinformatics, special issue on “Computational analysis of biological networks” and associate editor in IEEE/ACM Transactions on Computational Biology and Bioinformatics. In addition to these, he has served on the program committees of numerous computational biology and database conferences.
Abstract: Biological networks of an organism show how different bio-chemical entities, such as enzymes or genes interact with each other to perform vital functions for that organism. Dr. Kahveci’s lab is focusing on developing computational methods that will help in understanding the functions of large-scale biological networks. In this talk, we will focus on how different network models, such as static, probabilistic, dynamic, and multilayer models, address various challenges in computational biology. We will first consider challenges centered on uncertainty in the topology of biological networks. We will discuss our new mathematical model, which represent probabilistic networks as collections of polynomials. We show that this is a powerful model that enables solving seemingly very tough computational problems on probabilistic networks efficiently and precisely. We will then discuss how the dynamic behavior of the network affects how we can approach to some of the fundamental computational problems on biological network analysis such as motif counting and finally talk about how these solutions can enhance our understanding of next generation spatial transcriptomics and spatiotemporal transcriptomics data.
Keynote #4, June 20th – Title TBD
Under construction.