Professor Elzbieta Pustulka is a faculty member at FHNW University's School of Business, affiliated with the Institute for Business Informatics. Her research centers on databases, NLP, and AI-driven business process optimization. Research explores database pedagogy through gamification (SQL Scrolls), NoSQL implementations for ERPs, and automated information extraction techniques. Recent work emphasizes CI/CD practices in database development and educational game design for programming concepts. She supervises graduate students in projects spanning database indexing, logistics simulations, and AI-driven reporting systems. No scientific awards are noted.
Jan Milan Deriu is affiliated with the ZHAW School of Engineering, where he is part of the Centre for Artificial Intelligence. He holds the role of Researcher and has been actively involved in multiple research projects, serving as Project Leader and Deputy Project Leader in areas such as dialogue systems evaluation, speech translation, and misinformation analysis. His work spans academic publications in top conferences and journals, focusing on AI-driven solutions in natural language processing and related fields. His research interests are centered around Natural Language Processing (NLP), including dialogue systems, text generation, and sentiment analysis; Artificial Intelligence evaluation methodologies; Speech technology, particularly dialect recognition and speech-to-text systems; Analysis of organized misinformation in social networks; Machine learning applications for data-centric AI development. Deriu has led or co-led several significant projects, including: Unified Model for Evaluation of Text Generation Systems (UniVal) – Deputy Project Leader (ongoing) Holistic Analysis of Organised Misinformation Activity in Social Networks – Project Leader (ongoing) End-to-End Low-Resource Speech Translation for Swiss German Dialects – Deputy Project Leader (completed) Pre-Study on Generation of Hockey News – Deputy Project Leader (completed) Call-E – Virtual Call Agent – Team Member (completed) DeepText: Intelligent Text Analysis with Deep Learning – Deputy Project Leader (completed) He collaborates extensively with international researchers and institutions, contributing to advancements in dialogue systems, speech technology, and AI evaluation frameworks. His publications emphasize practical applications, such as Swiss German dialect processing and misinformation detection in social media.
Dr. Jonathan Fürst is a Researcher at the ZHAW School of Engineering, part of the Intelligent Information Systems research focus at ZHAW Zurich University of Applied Sciences. His work spans AI-driven data systems, ontology matching, IoT applications, and cross-domain data integration. Key research interests include: AI applications in healthcare and education Machine learning for data integration and systems Multi-modal data exploration and natural language interfaces IoT networks and smart building technologies His projects include: AI-based Quality Management of BIM Models (project leader) DataGEMS : A data discovery platform for generalized exploratory search Multi-lingual data exploration systems Digital Health Zurich: Patient-centered clinical innovation Recent research trends emphasize explainable AI, weakly supervised learning, and real-world system robustness. His work bridges theoretical advancements with practical implementations in domains like healthcare, education, and smart infrastructure. No scientific awards are explicitly listed, though his active role in Horizon Europe projects reflects collaborative excellence. He contributes to interdisciplinary teams across data science, engineering, and healthcare.
Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.
Prof. Martin Braschler is a Lecturer and Director of the Institute of Computer Science at ZHAW School of Engineering (Zurich University of Applied Sciences). He specializes in data science, information retrieval, and enterprise search systems. His interdisciplinary work bridges computer science, machine learning, and multilingual systems. Key projects include leadership in the DataInc, Skillue, and INODE initiatives, focusing on data integration, intelligent systems, and EU-funded research. He has published extensively in top journals like SIGMOD Record and Information Systems , emphasizing practical applications of data science in business environments. His research explores topics such as cross-language retrieval, algorithmic approaches for multilingual systems, and enterprise knowledge management. Research interests revolve around applied data science methodologies, information access evaluation (e.g., CLEF), and real-world implementation of machine learning techniques. He co-authored influential books like Applied Data Science: Lessons Learned for the Data-Driven Business and Multilingual Information Retrieval: From Research to Practice , highlighting transitions from theoretical research to industry solutions. Braschler has led multiple projects in collaboration with industry partners, focusing on scalable data systems and enterprise applications. Notable contributions include developing the INODE end-to-end data exploration system and pioneering work on untrained models for multimodal retrieval. His work on skill-extraction algorithms for job-matching platforms demonstrates practical impact in human resources automation. Braschler’s projects often address challenges in data integration, semantic search, and the evaluation of information systems in real-world contexts.
Prof. Dr. Maria Anisimova is a Professor at the Institute of Computational Life Sciences within the ZHAW School of Life Sciences and Facility Management. Her research focuses on computational methods in evolutionary genomics, bioinformatics, and molecular evolution. Key areas include tandem repeat analysis, ancestral sequence reconstruction, and cancer genomics. She leads multiple projects on colorectal cancer mechanisms, computational drug discovery, and evolutionary biology. Her work integrates statistical models and algorithms to address challenges in genome analysis, including indel evolution, phylogenetics, and natural language-based database querying. Notable contributions include tools like ARPIP, ProPIP, and TRAL, advancing sequence alignment and tandem repeat detection. She also explores the role of protein intrinsic disorder and tandem repeats in cancer biology. Prof. Anisimova’s interdisciplinary approach spans bioinformatics, computational biology, and clinical applications, with publications in journals like *Nature*, *Genome Biology*, and *Molecular Biology and Evolution*. She has authored a textbook on evolutionary genomics and contributed to major conferences and workshops in the field.
Valentin Hartmann is a Researcher at the Data Science Laboratory (DLAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on advanced topics in data science, including differential privacy, optimal transport theory, and privacy-preserving machine learning. He holds a postdoctoral position and contributes to cutting-edge research in computational statistics and secure distributed learning. His research interests span multiple domains: (1) Development of privacy-preserving techniques for machine learning and data analysis, (2) Optimization of transport problems with applications in statistics and geometry, (3) Design of secure algorithms for distributed systems, and (4) Implementation of robust statistical methods through R packages like 'transport'. Valentin actively publishes in top-tier venues, with recent contributions addressing neural network behavior analysis, privacy risks in data distribution inference, and novel cryptographic approaches for collaborative learning. His work bridges theoretical foundations with practical software solutions, as evidenced by his development of the 'transport' R package for optimal transport computations. He is affiliated with the DLAB research group at EPFL's INN building (Office INN 315), contributing to interdisciplinary projects at the intersection of computer science, mathematics, and privacy engineering.
Dr. Teodora Vuković is a computational linguist at the University of Zurich's Faculty of Arts, specializing in multimodal analysis of human interaction and language technology. As Principal Investigator of the CAPIRE and LCP projects, she leads development of automatic tools for analyzing speech, gestures, and facial expressions to identify person-specific patterns while implementing data anonymization techniques.
Marco Raglianti is a Postdoctoral Fellow and Research Assistant in the Reverse Engineering, Visualization, and Evolution Analysis Lab (REVEAL) at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on software engineering, documentation landscapes, developer communities, and visualization tools. He holds a PhD in Informatics from USI (2025) and M.Sc./B.Sc. degrees in Computer Science from the University of Pisa (Cum Laude). Key contributions include tools like DwarvenMail for documentation analysis, DiscOrDance for Discord community visualization, and Vizor for interactive graph exploration. He co-supervised multiple thesis projects and taught courses in software engineering at USI. His work bridges empirical software engineering with practical tool development, emphasizing developer-centric solutions. Raglianti's publications address topics like UML evolution, VR-based refactoring, and microservices data access patterns. He actively reviews for journals like ACM Transactions on Software Engineering and conferences such as ICSE and ESEC/FSE. His lab's focus on reifying software documentation reflects a commitment to improving developer workflows through systematic analysis and visualization.
Dr. Arnaud Gaudinat is an Associate Professor at the Geneva School of Business Administration (HEG Genève), part of the University of Applied Sciences and Arts of Western Switzerland (HES-SO). He is affiliated with the Economy and Services faculty and primarily works within the Department of Information Science. His educational background includes expertise in information science, with teaching responsibilities spanning multiple programs: MSc HES-SO in Information Science - teaching Data Curation and Information Architecture BSc HES-SO in Management Information Systems - teaching On-chain Analysis BSc HES-SO in Information Science - teaching Web Analytics, Survey Techniques, and Advanced Drupal Content Management BSc HES-SO in Business Economics - teaching Data Analytics and Data 4.0 Dr. Gaudinat's research focuses on the intersection of information science, web technologies, and data analytics. His work spans Web Mining, Information Retrieval, Blockchain applications, and Web Analytics. He has made significant contributions to biomedical text mining, developing systems for dataset retrieval and exploring applications of blockchain in information science. His recent work includes stablecoin taxonomy and using AI for information monitoring. Dr. Gaudinat approaches information science with a practical perspective, often developing tools and methods that address real-world information challenges in both academic and industry contexts. His research output shows a consistent focus on practical applications of information retrieval and text mining techniques, with a growing interest in blockchain technologies and their implications for information management. His most recent publications demonstrate expertise in cross-lingual analysis, stablecoin classification, and AI-assisted information monitoring. Dr. Gaudinat has received research funding from multiple sources including Innosuisse, swissuniversities-P5, and HES-SO. His projects often involve collaboration with both academic and industry partners, reflecting his commitment to bridging theoretical research with practical applications. His current research projects include: Precise Intelligence : Big Data Analytics for comprehensive Global Trade Flow Intelligence INCIPIT : Infrastructure Nationale d'un Complément d'Identifiants Pérennes, Interopérables et Traçables GOES : Global Online Expertise Search, a Web mining project for finding relevant people for specific surveys Dr. Gaudinat's work demonstrates a strong commitment to advancing information science through both theoretical contributions and practical applications that address contemporary challenges in data management and analysis.
Wolf Beat is an Associate Professor at the Fribourg School of Engineering and Architecture, part of the University of Applied Sciences of Western Switzerland (HES-SO). He is affiliated with the Institute of Complex Systems (iCoSys) where he conducts interdisciplinary research at the intersection of machine learning, bioinformatics, and document analysis. His educational background includes: BA HES-SO in Architecture from Fribourg School of Engineering and Architecture MSc HES-SO in Business Administration MSc HES-SO in Engineering PhD in "Reducing the complexity of OMICS data analysis" from University of Würzburg (2017) Dr. Beat's research spans multiple disciplines with a focus on applying machine learning techniques to solve complex problems. His work in bioinformatics has contributed to advancements in DNA sequencing analysis and genomic research, while his more recent work explores time series forecasting, document analysis, and anomaly detection. He has developed innovative approaches for handling historical manuscripts through computer vision techniques and has applied machine learning to geotechnical engineering problems. His research often bridges theoretical machine learning concepts with practical industrial applications. An analysis of his recent publications reveals a strong trend toward foundation models and diffusion models for time series analysis, alongside continued work in bioinformatics and document analysis. His research demonstrates a consistent pattern of applying cutting-edge AI techniques to domain-specific problems across diverse fields including healthcare, engineering, and digital humanities. Dr. Beat is actively involved in multiple research projects including ModIA (2024-2025, 15,000 CHF) and as principal investigator for the Foundation model for time series forecasting project (2024-2025, 100,000 CHF). His past projects include Monitoring des Transformateurs de Puissance (2023-2024, 220,000 CHF) and GREENum (2022, 24,700 CHF), demonstrating his ability to secure funding for interdisciplinary research. He collaborates extensively within the HES-SO network and with external partners, often serving as a co-researcher or principal investigator on projects that combine expertise from engineering, computer science, and domain-specific applications. His research team at iCoSys Institute includes multiple researchers working on AI and complex systems, and he has been involved in numerous projects with colleagues including Montet Frédéric, Pasquier Benjamin, Von Barnekow Alec, and Maillard Philippe. His work demonstrates a consistent commitment to developing practical AI solutions that address real-world challenges across multiple domains.
Dr. Mykola Makhortykh is a Lecturer at the Institute of Communication and Media Studies (University of Bern), where he examines how algorithmic systems and AI shape Holocaust memory transmission. Previously, he served as a postdoctoral researcher at the University of Amsterdam and University of Bern, focusing on algorithmic fairness in news personalization and political information behavior in high-choice environments. Education : BA in History (Kyiv Taras Shevchenko University), MA in Archaeology (Kyiv Taras Shevchenko University), Joint MA in Euroculture (University of Goettingen & Jagiellonian University), BA in Computer Science (University of the People), PhD in Communication Science (University of Amsterdam). Research Interests span algorithmic auditing, computational propaganda, trauma/memory studies, cybersecurity, and digital cultural heritage. He combines traditional social science methods with computational approaches like deep learning and agent-based testing to analyze how search engines and recommender systems influence historical memory and political discourse. Recent Work includes audits of search engine biases in representing mass atrocities, studies on political trolling, and analyses of hyperpartisan media. His scientific awards include the Alfred Landecker Lecturer position. As an editor , he contributes to the Studies in Russian, Eurasian and Central European New Media journal and the Transdisciplinary Trauma Studies book series. Teaching includes courses on algorithmic auditing, while his editorial work and conference presentations (over 50) highlight his interdisciplinary impact.
Anne-Marie Kermarrec is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL) since 2020, specializing in large-scale distributed systems, epidemic algorithms, and system support for machine learning. She previously served as CEO of Mediego (2015–2020), a startup providing content personalization services, and held a Research Director role at Inria, France (2004–2015). Her academic affiliations include the School of Computer and Communication Sciences (IC), specifically the Institute of Communication Systems (IINFCOM) and the Scalable and Adaptive Distributed Systems Lab (SACS). She also holds leadership roles in education and governance at EPFL, including Vice-President for Doctoral Education and Continuing Education. Her research focuses on distributed systems, federated learning, privacy-preserving technologies, and scalable machine learning architectures. Notable achievements include an ERC Grant (2008), ERC Proof of Concept Grant (2013), ACM Fellowship (2016), and election to the European Academy (2013). She advises multiple PhD students and has authored influential papers on decentralized learning systems, graph watermarking, and federated learning optimizations. Professional roles include membership in the Global Ethics and Partnerships Committee (GEP) and leadership in the Commission Doctorale (CDOCT). Her work bridges theoretical computer science with practical applications, emphasizing ethical AI and system interoperability.
Prof. Dr. Jasmina Bogojeska is a Professor for Artificial Intelligence and Machine Learning at ZHAW School of Engineering, where she leads the Explainable Artificial Intelligence Group since March 2024. She previously held roles as Senior Principal Data Scientist at Roche (2022-2024) and Research Staff Member at IBM Research Zurich (2013-2022), with postdoctoral work at Max Planck Institute for Informatics (2011-2013). Her affiliations include ZHAW Datalab and Digital Health Lab. PhD in Computer Science (2011), Saarland University/Max Planck Institute MSc in Computer Science (2007), Saarland University BSc in Computer Science (2004), University Ss. Cyril and Methodius Her research bridges Explainable AI with applications in Healthcare , IT Infrastructure Management , and Critical Care Time-Series Analysis . Key focus areas include domain-specific foundation models , conversational data systems , and low-resource NLP solutions . Recent work explores multi-modal medical AI for chest X-ray interpretation and large-scale clinical time-series datasets . Notable contributions include GIT-CXR for automated radiology reports and domain-specific protein language models in immunology. Her publications span IEEE, BMC, and Nature journals, emphasizing practical AI deployment in healthcare and IT operations. Scientific Recognition: Edelman Prize Finalist (2020) IBM Research Accomplishment Award (2019) IBM Corporate Award (2017) Best Paper Award at CNSM (2013) She actively develops reliable AI systems for server incident reduction (PASIR) and child injury monitoring, while advancing automated medical analytics through projects like Antibiotika-Resistenz Tracker. Her teaching covers Machine Learning , Data Mining , and Safer AI at bachelor and master levels.
Prof. Angelika Steger is a Full Professor in the Department of Computer Science at ETH Zurich, leading research in theoretical computer science since 2003. She holds a Master's in Applied Mathematics from Stony Brook University (1985) and a PhD from the University of Bonn (1990). Her career includes roles at Kiel, Duisburg, and TU München before joining ETH. She is a Leopoldina member (2007), ICM speaker (2014), and Collegium Helveticum Fellow (2009+). Research focuses on probabilistic methods, randomized algorithms, graph theory, and combinatorial optimization. She has contributed to understanding discrete structures, neural networks, and algorithmic resilience. Awards include recognition in both computer science and mathematics circles. Her work bridges theoretical foundations with applications in AI, neuroscience, and distributed systems.
