Jakob Ambsdorf is a PhD Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Pioneer AI (P1AI) research group. His work focuses on medical image analysis, particularly in fetal ultrasound and brain MRI data, leveraging AI and machine learning techniques for anomaly detection and image quality assessment. Recent research includes unsupervised detection of fetal brain anomalies using denoising diffusion models and learning semantic image quality from noisy ranking annotations. Key collaborations involve researchers from institutions like the University of Copenhagen and international partners in medical imaging.
Prof. Dr. Ulf Leser serves as Professor and Deputy Director at the Institute of Computer Science within Humboldt University of Berlin's Faculty of Mathematics and Natural Sciences. His work bridges computer science and life sciences through bioinformatics and knowledge management systems, with significant contributions to scientific workflow optimization and biomedical text mining. His research spans multiple high-impact areas: Bioinformatics and precision oncology tool development (e.g., OncoTagger for cancer gene curation) Scientific workflow systems with focus on carbon-aware execution and energy efficiency Biomedical natural language processing for relation extraction and knowledge base curation Advanced time series analysis methods (e.g., ClaSP for segmentation) Environmental monitoring through satellite data analysis He actively develops infrastructure for reproducible scientific computing while addressing sustainability challenges in HPC environments. Recent publications (2023-2026) reveal three dominant trends: (1) Integration of explainable AI in healthcare assessment systems, (2) Sustainable computing approaches for scientific workflows including carbon-aware scheduling, and (3) Advancement of biomedical text mining through knowledge-augmented language models. His work consistently targets real-world applications in precision medicine and environmental science. Prof. Leser currently supervises students including Michael Piechotta (Diplom in Bioinformatics, defense scheduled September 2025). As Deputy Director and Faculty Council member, he shapes institutional research strategy while leading projects at the intersection of computer science and life sciences. His group maintains active collaborations with biomedical research institutions and contributes to community standards in scientific workflow systems.
Yen-Chia Hsu is an Assistant Professor at the Informatics Institute, University of Amsterdam, where they teach courses in Information Visualization and Data Science. Previously, they served as a Postdoctoral Researcher at the Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, TU Delft, and as a Project Scientist in the CREATE Lab at Carnegie Mellon University (CMU). Their academic journey reflects a unique interdisciplinary background bridging computer science and architectural design. Dr. Hsu earned their Ph.D. degree in Robotics in 2018 from the Robotics Institute at CMU, where they conducted research on using technology to empower local citizens and communities. Prior to that, they received their Master's degree in tangible interaction design in 2012 from the School of Architecture at CMU, where they studied and built prototypes of interactive robots and wearable devices. Before CMU, they earned a dual Bachelor's degree in both architecture and computer science in 2010 at National Cheng Kung University, Taiwan. Dr. Hsu is a computer scientist with an architectural design background whose research focuses on Community-Empowered Artificial Intelligence (AI) , where they co-design, implement, deploy, and evaluate interactive AI systems that empower communities, especially in addressing environmental and social issues. Their work spans both social and technical aspects of community engagement with technology. On the social side, they have proposed an alternative framework called Community Citizen Science (CCS) , which extends traditional citizen science methods to a hyper-local scale, emphasizing continued community engagement after technology interventions. On the technical side, they investigate human feedback in AI pipelines and algorithms that enable machine learning models to incorporate different types of human input. Dr. Hsu's scholarly output demonstrates a consistent focus on applying computer vision, machine learning, and data science to environmental monitoring and community empowerment. Their recent work shows an evolution from developing specific tools for pollution monitoring toward more comprehensive frameworks for community engagement with AI systems. A notable trend is the increasing emphasis on empathy-centered design and policy implications of community-driven data collection systems. Their research bridges the gap between technical innovation and social impact, particularly in the domains of air quality monitoring and environmental justice. Outstanding Student Academic Achievement (2005, 2006, 2007) from Department of Architecture, National Cheng Kung University, Taiwan Third Prize, National Country House Design Competition (2008) from Ministry of the Interior, Taiwan Best New Artist, The National Golden Award for Architecture (2009), Taiwan Webby People's Voice Award, Best Use of Video or Moving Image (2014) Best Paper Honorable Mention Award (Top 5%) at ACM CHI Conference (2017) Best Paper Honorable Mention Award (Top 2.5%) at ACM IUI Conference (2019) Prize for Community Collaboration, The Constellation Prize (2020) Dr. Hsu has been actively involved in numerous research projects that bridge academia and community action. Their work on the Smell Pittsburgh platform, which allows citizens to report pollution odors to regulators, has been particularly influential in environmental advocacy. They have collaborated with organizations including ACCAN, PennEnvironment, GASP, Sierra Club, ROCIS, Blue Lens, LLC, PennFuture, Clean Water Action, and Clean Air Council. Their research has received support from the Heinz Endowments and has been featured in TIME, Pittsburgh Post-Gazette, PC Magazine, and other media outlets. Dr. Hsu also maintains an active open-source presence, with several tools and datasets released to support community-driven environmental monitoring. Dr. Hsu leads projects that focus on developing tools for community engagement at scale, including COCTEAU, an empathy-based tool for decision-making, and Project RISE, which recognizes industrial smoke emissions. Their work connects with the Multimedia Analytics Lab Amsterdam, where they contribute to data science education and research. Their approach emphasizes co-creation with communities rather than top-down technology deployment, positioning them at the forefront of human-centered AI research with real-world social impact.
Johanna Gerlach is a Professor at the Faculty of Translation and Interpretation at the University of Geneva, affiliated with the TIM/ISSCO research group. With an extensive publication record spanning from 2010 to 2025, she has established herself as a leading researcher in the field of medical translation technology and accessible healthcare communication. Her research focuses on developing innovative translation systems that bridge communication gaps in medical settings, particularly through her work on the BabelDr platform and PROPICTO project. Dr. Gerlach's work spans multiple dimensions of accessible communication including speech-to-pictograph translation systems, sign language production for healthcare, and multilingual medical communication solutions for emergency departments. Her research demonstrates a strong commitment to improving healthcare accessibility for non-native speakers and people with communication difficulties. Analysis of her recent publications (2023-2025) reveals a clear trend toward practical implementation of translation technologies in real-world healthcare settings, with particular emphasis on emergency medicine contexts. Her work integrates computational linguistics, machine learning, and human-centered design to create accessible communication tools that address critical gaps in multilingual healthcare delivery. The research spans both contemporary medical communication challenges and historical text processing applications. Dr. Gerlach has supervised 3 academic works and maintains an active research program with numerous ongoing projects including UNI-ACCESS (focusing on web accessibility for higher education institutions) and PASSAGE (addressing Swiss German TV content subtitling). Her collaborations span multiple disciplines including medical informatics, computational linguistics, and accessibility studies.
Brett Reynolds is an Adjunct Professor at the University of Toronto (St. George Campus). His research focuses on English syntax, particularly determiners, pronouns, and syntactic annotation. He explores intersections between cognitive and functional linguistics, and questions regarding the philosophy of grammaticality. Fields: Applied Linguistics, Syntax, Syntactic Annotation Key Interests: Determiner systems, functional syntax, cross-disciplinary grammatical theory His recent publications (2021-2025) analyze negation structures across languages, adverbial categorization debates, and introductory syntax pedagogy. No major awards are listed, though his work contributes to foundational linguistic theory. No advising/grants information available. Research activities include treebanking projects and theoretical syntax development.
Olac Fuentes is an Associate Professor in the Computer Science Department at the University of Texas at El Paso. His primary research focuses on developing machine learning systems for scientific data analysis, with applications in astronomy, geology, biology, and optics. He specializes in leveraging unlabeled data, active learning, feature selection, and noise-aware algorithms to extract insights from complex datasets. Education: B.S. in Industrial Engineering from Instituto Tecnológico de Chihuahua M.S. in Computer Science from University of Texas at El Paso Ph.D. in Computer Science from University of Rochester Research Focus: Dr. Fuentes develops intelligent systems for interdisciplinary scientific challenges. His work spans machine learning theory (optimization, noise handling) and applied domains including: computer vision for environmental monitoring (Arctic change detection, glacier segmentation), neuroinformatics (brain mapping), and multimodal data fusion (prosodic analysis for communication disorders). He frequently employs deep neural networks, active learning, and novel regularization techniques. Publication Trends: Recent works demonstrate strong cross-disciplinary focus on climate science (satellite image analysis for coastal erosion), biomedical applications (neuroanatomy mapping, histology), and fundamental ML theory. His publications consistently integrate advanced neural architectures (CNNs, LSTMs) with domain-specific challenges in geosciences, neuroscience, and linguistics.
Aleksandra Ćwiek is a Researcher at the Leibniz Centre General Linguistics (ZAS), Berlin, where she leads the DFG project FLESH (On the Flexibility and Stability of gesture-speech coordination). She holds a Ph.D. in General Linguistics from Humboldt-Universität zu Berlin (2022) and has held roles such as Deputy Equal Opportunities Officer at ZAS and Mentee in the Leibniz Mentoring Program. Her research focuses on acoustic/prosodic iconicity, sound symbolism, and multimodal communication, with cross-linguistic studies exploring gesture-speech coordination and language evolution. Education includes a Master's in Linguistics from Bielefeld University (2016) and a Bachelor's in Linguistics/German as a Foreign Language (2015). She co-initiated envisionBOX , a platform for multimodal data analysis. Research interests span linguistic prominence, kinematic properties of gestures, and the evolution of language. Awards include the 2024 DGfS Wilhelm von Humboldt Young Talent Award. Her work bridges experimental phonetics, cognitive linguistics, and computational methods, with recent contributions to special issues on iconicity and sound symbolism.
Kazuhiro Aoki, PhD, is an Associate Professor at the Medical College of Wisconsin (MCW) and Technical Director of the Translational Metabolomics Shared Resource at the MCW Cancer Center. Previously, he served as faculty at the University of Georgia (2008-2022) and as an invited professor at Lille University of Science and Technology, France (2017). His expertise spans analytical methodologies for multi-Omics studies, mass spectrometry-based technologies, and glycomics research. Dr. Aoki holds a PhD in Life Science from Kyoto University (2001) and advanced training in glycobiology and pharmaceutical assessment. Education: PhD in Life Science, Kyoto University, Japan (2001) MS in Science Pedagogy, Shiga University, Japan (2000) BS in Science, Shiga University, Japan (1995) Research Focus: Dr. Aoki develops and applies cutting-edge glycomics tools to study biomolecule structure-function relationships across organisms, including embryonic stem cells, cancer tissues, and microbial systems. His work emphasizes multi-Omics integration, with applications in drug metabolism, disease progression (e.g., Fabry disease), and cell differentiation mechanisms. Key techniques include mass spectrometry-based glycoproteomics and linked-Omics approaches. Grants & Labs: Led development of MicroGlycoDB, a microbial glycan database Pioneered tools like DANGO for glycolipidomics analysis Collaborates internationally on glycobiology projects Labs/Teams: Directs the Translational Metabolomics Shared Resource, supporting metabolomic and glycomic analyses for cancer research.
Yu Liu is a Professor at the School of Computing, Binghamton University, where he has been a faculty member since 2008. His research focuses on sustainable computing, energy efficiency, and security in emerging software systems, with significant contributions to programming languages, robotics, and software-hardware interfaces. Education: MSE, Johns Hopkins University PhD, Johns Hopkins University Professor Liu's research centers on sustainable computing and energy-aware software systems. He investigates programming language designs for energy prediction, develops reliable robotics software through managed languages, and creates secure software-hardware interfaces. His work bridges theoretical foundations with practical applications in data centers, UAV systems, and multi-threaded environments, emphasizing verifiable sustainability metrics and energy-efficient resource management. His publication trends reveal a consistent focus on sustainable computing evolution, with recent work advancing verifiable sustainability frameworks in data centers (2023-2024), energy accounting for multi-threaded applications (2020), and secure cache management techniques (2022). The research spans programming language theory, robotics safety, and hardware security, demonstrating interdisciplinary integration across computer science subfields. Scientific Awards: NSF CAREER Award Google Faculty Research Award Fulbright Scholarship Professor Liu has secured significant research funding through his NSF CAREER Award and Google Faculty Research Award, supporting innovations in sustainable computing infrastructure. His Fulbright Scholarship enabled international collaboration in Slovenia, advancing global research in energy-efficient software systems. He regularly teaches core courses including Programming Languages (CS471/571) and Sustainable Computing (CS680G), mentoring the next generation of computer scientists. While specific lab affiliations aren't detailed in the source text, his research on UAV software (Jcopter) and cache security (Composable cachelets) suggests active involvement in robotics and systems security research groups at Binghamton University.
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. 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. 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.
Barbara Plank is a Visiting Professor at the IT-University of Copenhagen, affiliated with the NLPnorth research group. She specializes in Natural Language Processing (NLP), focusing on multilingual language understanding, computational job market analysis, and annotation tools. Her work bridges machine learning techniques with practical applications in domains like skill extraction and cross-lingual systems. Affiliations: NLPnorth, IT University of Copenhagen Key Research Areas: Multitask learning, domain adaptation, entity linking Education: Not explicitly listed in text. Research Interests: Barbara’s research emphasizes advancing NLP through robust models for under-resourced languages and real-world applications. She explores techniques like nearest neighbor methods for occupational analysis, domain-aware relation classification, and easy-to-use annotation tools (e.g., EEVEE) to democratize NLP tasks. Her work often addresses challenges in multilingual systems and ethical AI deployment. Grants & Projects: MultiSkill : Multilingual information extraction for job market analysis (2020–2024) MultiVaLUe : Multilingual variety-aware language understanding (2020–2024) Pioneer Centre for Artificial Intelligence : Focus on pre-registered replication and ethical AI (2021–2034) Awards: Amazon Research Award (2019) Outstanding paper awards at EACL 2021 and ML Evaluation Standards Workshop 2022 Labs/Teams: Active in the NLPnorth group, collaborating on projects like the 18th Linguistic Annotation Workshop and SemEval tasks.
Sina Zarrieß is a Professor of Computational Linguistics at the University of Bielefeld, affiliated with the Faculty for Linguistics and Literature Studies. His research focuses on computational models of language use, with applications in natural language generation, dialogue systems, and language-vision integration. He leads the Computational Linguistics Bielefeld group and collaborates on interdisciplinary projects in ecology, hate speech analysis, and multimodal interaction. Research interests include language model architecture optimization, pragmatic reasoning in generation, and ethical NLP. He actively contributes to workshops such as ACL, EMNLP, and NLP4Ecology, publishing on topics like character-based LMs, gaze-driven hate speech detection, and visualization-oriented dialog systems. His work bridges theoretical linguistics with applied machine learning, emphasizing interpretability and real-world impact. Notable projects include developing BabyLM character-based models, studying gender-inclusive language benchmarks (SlayQA), and creating tools like VIST5 for adaptive visualization dialog. He explores how LMs encode linguistic principles like Maximize Presupposition! and investigates zero-shot learning for novel object categories. Current research trends show a focus on model efficiency (e.g., sentence selection for classification), multimodal grounding (e.g., scene context in visual tasks), and human-AI interaction dynamics (e.g., explanation effects on user perception). Despite prolific publishing, no specific scientific awards are mentioned in the provided materials. Advising and grants are not explicitly detailed, though his extensive co-authorship network indicates active mentoring. The Computational Linguistics Bielefeld group maintains open research directions in visual question answering, poetry generation diversity, and historical hate speech evolution studies.
Ernesto Jimenez-Ruiz is a Researcher at the University of Oslo affiliated with the Centre for Scalable Data Access (SIRIUS) and Logic and Intelligent Data group. He holds a PhD in Computer Science from University Jaume I of Castellon and specializes in semantic technologies and ontology engineering. His research spans bio-medical information processing, ontology reuse/alignment, and semantic web technologies for data analytics. Current projects include Artificial Intelligence for Data Analytics (AIDA) at The Alan Turing Institute, where he serves as Senior Research Associate. Recent publications (2024-2025) focus on neurosymbolic AI systems, knowledge graph construction from tabular data, and ontology alignment techniques. His work demonstrates strong emphasis on practical applications of semantic technologies. Dr. Jimenez-Ruiz has developed several tools including LogMap for ontology matching and BootOX for relational-to-ontology mapping. He teaches Semantic Technologies (INF3580/INF4580) and supervises PhD students in ecotoxicological effect prediction using knowledge graphs.
