Artificial intelligence • Bayesian data analysis • bioinformatics • casual discovery • causal inference • chemoinformatics • data and knowledge fusion • decision support • federated learning • image processing • machine learning • multi-agent systems • multimodal diagnostics • natural language processing • semantic technologies
(+36) 1 463-2677
A kutatócsoport tagjai:
Hullám Gábor István
assistant research fellow
Bolgár Bence Márton
assistant research fellow
Millinghoffer András Dániel
Activity of the research group:
Our research covers the full spectrum of artificial intelligence (MI), organically linked to our group's decades-long faculty role in general AI education, which is now present in BProf, BSc, MSc and PhD programs. Our working groups within the research group are Digital Humanities, Image Processing and Multimodal Diagnostics, Cooperative Intelligence, and Computational Biomedicine (ComBine). Digital Humanities examines semantic technologies through the processing of natural language. Image Processing and Multimodal Diagnostics explores the integration of classical image processing and information fusion methods with new artificial intelligence-based methods. Cooperative intelligence research examines federated learning, sensor networks, autonomous vehicles, multi-agent systems, and distributed computing methods. The ComBine team is developing artificial intelligence methods for bioinformatics, chemoinformatics and health informatics, with a particular focus on data and knowledge fusion, systems-based data analysis, and causal discovery.
We have launched an Artificial General Intelligence course to teach about the grand challenges, limits, trends, interdisciplinary nature and ethical, social dimensions of artificial intelligence. Our group is also participating in the development of a new Human-Centered Artificial Intelligence MSc program, which is part of an international EU project. We have launched and organized a student competition at the faculty in Artificial Intelligence and Machine Learning for the third time. In our research, we routinely use and constantly expand our Bayesian systems-based analysis to support biomarker discovery in several projects (UKB1602, TRAJECTOME, OTKA119866, OTKA139330). In addition to our statistical genetic research, we also focus on the development of AI methods for chemoninformatics and drug discovery, which is strengthened by our participation in national and international projects (MELLODDY). Our intelligent image processing and multimodal data fusion research has also been boosted by new projects, and we are examining the use of modern artificial intelligence tools in natural language processing in several collaborations.
H2020/IMI2, MELLODDY • EUREKA, iCare4NextGen • OTKA K139330
University of Minnesota • K.U.Leuven
SOTE • Richter • Dealogic • Abylon Kft. • E-Group • Continental